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Cognitive and Human Factors in Expert Decision Making: Six Fallacies and the Eight Sources of Bias
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Analytical Chemistry

Cite this: Anal. Chem. 2020, 92, 12, 7998–8004
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https://doi.org/10.1021/acs.analchem.0c00704
Published June 8, 2020

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Abstract

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Fallacies about the nature of biases have shadowed a proper cognitive understanding of biases and their sources, which in turn lead to ways that minimize their impact. Six such fallacies are presented: it is an ethical issue, only applies to “bad apples”, experts are impartial and immune, technology eliminates bias, blind spot, and the illusion of control. Then, eight sources of bias are discussed and conceptualized within three categories: (A) factors that relate to the specific case and analysis, which include the data, reference materials, and contextual information, (B) factors that relate to the specific person doing the analysis, which include past experience base rates, organizational factors, education and training, and personal factors, and lastly, (C) cognitive architecture and human nature that impacts all of us. These factors can impact what the data are (e.g., how data are sampled and collected, or what is considered as noise and therefore disregarded), the actual results (e.g., decisions on testing strategies, how analysis is conducted, and when to stop testing), and the conclusions (e.g., interpretation of the results). The paper concludes with specific measures that can minimize these biases.

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Subjects

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Scientists and experts are expected to make observations and draw impartial conclusions based on the relevant data. Therefore, we tend to focus on the data and methods used, as if they exist in isolation from the human decision maker. For example, a recent paper reviews forensic DNA analysis (1) and provides a thorough review of the DNA methods, but does not give sufficient note and attention to the fact that DNA analysis depends and hinges on the humans conducting it, and how their biases may impact the DNA results. Indeed, a flurry of recent publications demonstrate the importance and influence of the human impact on DNA analysis. (2−4)
Such cognitive biases are not limited to DNA analysis, and have been demonstrated in many domains (e.g., fingerprinting and other forensic science disciplines, (5,6) forensic psychology, (7) as well as drug detection and discovery (8)). Biases can impact the actual observation and perception of the data, testing strategies, as well as how the results are then interpreted and how conclusions are reached. (6) Even one of the most objective domains, toxicology, has now been shown to be exposed to issues of cognitive bias, (9) and such biases have created error in real toxicology casework (e.g., determining a wrong testing strategy in a post-mortem toxicology case (10)).
Thus, the impact of bias is not limited to the important interpretation of the results, but it has a much broader and deeper scope as it impacts what the data are (e.g., what is collected, what is considered as noise and disregarded, etc.) and the results of the analyses themselves (e.g., testing strategy as to what tests and types of analysis to conduct, how they are carried out and by whom, and when to stop testing).
Many of the challenges in dealing with these biases arise from the nature of cognitive biases, their various sources, and fallacies about them, which are described in detail below.

Six Fallacies of Bias

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Before delving into the eight sources of cognitive biases, it is important to present and dispel commonly held fallacies. If we do not recognize and write-off these fallacies, it becomes practically impossible to move forward because these fallacies minimize (if not dismiss altogether) the very existence of the biases. The first step to deal with biases is to acknowledge their existence and the impact they may have on hard working, dedicated, and competent experts.

First Fallacy: Ethical Issues

Many incorrectly think these biases are an ethical issue of corrupt individuals or even malicious acts by unscrupulous people, whereas cognitive bias actually impacts honest and dedicated examiners. Time and again cognitive biases are presented under the umbrella of ethical issues, in books (11), conferences, and training (e.g., “ethics in cognitive bias” (12)). It is a fallacy that arises from a basic misunderstanding of what cognitive bias is all about. It is not a matter of dishonesty, intentional discrimination, or of a deliberate act arising from underlying ethical issues. (13) It is true that there are cases of intentional professional misconduct, e.g., when a chemist in a drug laboratory in Boston forged tests. (14,15) However, cognitive bias is not about such ethical issues of personal character, integrity, or intentional misconduct.

Second Fallacy: Bad Apples

Often when errors and biases are found, the experts involved are blamed for the errors, rather than acknowledging any systemic issues, such as the human element in the analysis and the general susceptibility to bias. (16) Surely there are cases where error is due to examiners’ competency, but these are relatively easy to detect and to fix. The kind of biases discussed here, implicit cognitive biases, are widespread and are not due to incompetency, and therefore are harder to detect.

Third Fallacy: Expert Immunity

There is a widely incorrect belief that experts are impartial and immune to biases. (17) However, the truth of the matter is that no one is immune to bias, not even experts. (18) In fact, in many ways, experts are more susceptible to certain biases. The very making of expertise creates and underpins many of the biases. (19) For example, experience and training make experts engage in more selective attention, use chunking and schemas (typical activities and their sequence), and rely on heuristics and expectations arising from past base rate experiences, utilizing a whole range of top-down cognitive processes which create a priori assumptions and expectations.
These cognitive processes enable experts to often make quick and accurate decisions. However, these very mechanisms also create bias that can lead them in the wrong direction. Regardless of the utilities (and vulnerability) of such cognitive processing in experts, they do not immune experts from bias, and indeed, expertise and experience may actually increase (or even cause) certain biases. Experts across domains are subject to cognitive vulnerabilities. (20)
Although experts tend to be very confident (sometimes even overconfident), more experienced experts can actually perform worse than novices. This has been demonstrated, for example, in environmental ecology, where data collection is critical and underpinned by the ability to correctly identify and collect samples, and novices actually outperform experts. (21)

Fourth Fallacy: Technological Protection

People think that the mere use of technology, instrumentation, automation, or artificial intelligence eliminates bias. These can reduce bias, but even when these are in used, human biases are still at play because these systems are built, programmed, operated, or interpreted by humans. There is a danger that people will incorrectly believe that using technology is a guaranteed protection from being susceptible to and affected by bias. Furthermore, technology can even introduce biases, be it mass spectral library matching software, Automated Fingerprint Identification Systems (AFIS), or other technological devices. (22)

Fifth Fallacy: Bias Blind Spot

The experts themselves are often not aware of their biases and therefore hold a fallacy that they are not biased. One must remember that these are implicit cognitive biases, not intentional discriminatory types of biases. The bias blind spot (23) is well documented and has been demonstrated in a variety of domains, including forensic science (17) and forensic psychology. (24) While it is relatively easy to see bias in others, we are often blind to our own biases. Research has found that 70% of forensic scientists now acknowledge that cognitive bias is a cause for concern in forensic science as a whole, but only 52% think it is a concern in their own specific forensic domain, and just 25% think it is relevant to them personally, reflecting the hallmarks of the bias blind spot. (17,23)

Sixth Fallacy: Illusion of Control

Even when experts are made aware of and acknowledge their biases, they nevertheless think they can overcome them by mere willpower. (25) This is the illusion of control. Combating and countering these biases requires taking specific steps—willpower alone is inadequate to deal with the various manifestations of bias.
In fact, trying to deal with bias by the illusion of control may actually increase the bias, due to “ironic processing” or “ironic rebound”. (26) Hence, trying to minimize bias by willpower makes you think of it more and actually increases its effect. This is similar to a judge instructing jurors to disregard certain evidence. By doing so, the judge is actually making the jurors notice this evidence even more. (27)
The beliefs in such fallacies, see Table 1, prevent dealing with the biases because they dismiss their powers and even their very existence. We need to acknowledge the impact of biases and understand their sources, so we can take appropriate measures when needed and when possible to combat their affects.
Table 1. Six Fallacies about Cognitive Bias Commonly Held by Experts
FallacyIncorrect belief
1. Ethical IssuesIt only happens to corrupt and unscrupulous individuals, an issue of morals and personal integrity, a question of personal character.
2. Bad ApplesIt is a question of competency and happens to experts who do not know how to do their job properly.
3. Expert ImmunityExperts are impartial and are not affected because bias does not impact competent experts doing their job with integrity.
4. Technological ProtectionUsing technology, instrumentation, automation, or artificial intelligence guarantees protection from human biases.
5. Blind SpotOther experts are affected by bias, but not me. I am not biased; it is the other experts who are biased.
6. Illusion of ControlI am aware that bias impacts me, and therefore, I can control and counter its affect. I can overcome bias by mere willpower.

Eight Sources of Bias

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Cognitive biases are widespread, a ubiquitous phenomenon in many guises. (13)Figure 1 illustrates eight sources of bias in expert decision making. It shows that cognitive bias can arise from a variety of sources, which are categorized into three groups. Category A relates to the specific case—something about this case causes bias in how data are perceived, analyzed, and interpreted. Other sources of cognitive bias, Category B, have nothing to do with the specific case, but they arise from factors relating to the specific person doing the work—something about them (e.g., their experience, their personality, their working environment, their motivation, etc.) causes the bias. Further sources of cognitive bias, Category C, arise from human nature, the very cognitive architecture of the human brain that we all share, regardless of the specific case or the specific person doing the analysis.

Figure 1

Figure 1. Eight sources of bias that may cognitively contaminate sampling, observations, testing strategies, analysis, and conclusions, even by experts. They are organized in a taxonomy within three categories: starting off at the top with sources relating to the specific case and analysis (Category A), moving down to sources that relate to the specific person doing the analysis (Category B), and at the very bottom sources that relate to human nature (Category C).

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It is important to understand these different sources of bias so we can more easily recognize and expose them, and most importantly, each of the different sources of biases requires specific countermeasures.

(1) The Data

The first source of cognitive bias within the factors that relate to the specific analysis is the data. How can data cause bias? Well, it depends on the data. Some data, such as fingermarks, do not, per se, cause bias, as they convey no information beyond the friction ridges impressions. However, with other types of data (such as in the analysis of voice, handwriting, blood spatter, and bitemarks), the data can contain potentially biasing information. For example, in voice analysis, the content, or even the tone and screaming, can reveal a brutal assault. Similarly, gang-rape mixture DNA evidence can evoke emotions which can impact decision making. (28−30)

(2) Reference Materials

Reference materials can bias how the data are perceived and interpreted. This applies to a variety of data, including DNA, fingerprinting, and any decisions that are based on making comparisons. If DNA evidence interpretation is influenced by the “target” suspect known reference material (i.e., their DNA profile) so to better fit them, then there is biased interpretation of the biological material from the crime scene. (31)
Rather than the actual evidence being the driver of the decision making process—where evidence is interpreted based on the data it contains—the suspect’s profile is driving the decision making. Hence, rather than going from the evidence to the suspect (from data to theory), the reference materials cause examiners to go backward (circular reasoning) from the target/suspect to the evidence, thus biasing how the data are interpreted. (32)
Take the case of Kerry Robinson who was wrongly convicted of rape based on mistaken analysis of DNA evidence. (33) The two DNA examiners in the case made an error as they seem to have been working backward from Kerry Robinson’s known DNA profile to the latent evidence. Thus, the suspect “target” was driving the analysis rather than the evidence itself. He was exonerated and released from jail after serving 17 years. (34) Indeed, when the same DNA evidence was later analyzed properly, different results were obtained. (35)
The problem with “going backward”, circular reasoning, is not limited to DNA and can be found in other comparative domains, such as fingerprinting, handwriting, firearms, and other domains (for a review, see ref (5)), where the reference materials of the known suspect influences the interpretation of the data from the crime scene. This has been exemplified when the FBI (as well as an expert hired by the defense) erroneously identified Mayfield as the Madrid bomber. (36) Again, the “target” (in this case, the fingerprints of the suspect) was driving the analysis, resulting in an erroneous identification. For example, in the analysis, a signal in the evidence was perceived as noise and disregarded because it did not match the target—a typical error emerging from going backward and letting the target or expected results drive the analysis.
Furthermore, this source of bias is not limited to circumstances that have a “target” suspect per se, but can also arise from pre-existing templates and patterns, such as in the interpretation of blood pattern analysis or a crime scene. It can even impact what color is observed. Hence, bias is not limited to the interpretation for the appearance of a color (e.g., pink color in the Griess test, incorrectly interpreted as meaning the suspects handled dynamite, see below), but can even impact what color is observed in the first place when Munsell color charts are used as reference templates. (37)
This bias goes beyond impacting only the conclusion and interpretation of what the presence of the color means, because it can also bias and impact the observation of what the color itself is (for this important distinction between biases impacting observations vs conclusions, see the Hierarchy of Expert Performance (HEP)). (6)
These types of cognitive biases are therefore not limited to a target or reference, they can even be caused by having a theory, a chart, or a pattern in mind, and have been shown to impact a variety of expert domains. (38−40)
This bias can also occur, for example, in a messy mass chromatogram or infrared spectrum where an analyst might “pick out” the peaks they are looking for and ignore the others. This is not intentional and happens without awareness. The expectation biases cognitive resources and attention toward a certain stimulus or signal (while suppressing and ignoring others) (41) and impacts sensory response to predictable object stimuli throughout the ventral visual pathway. (42,43) It also biases perception sensitivity for those targets, (44) changing sensory representations in the visual cortex, (45) and hence impacts what we actually perceive and how. (46) Just as it can bias the visual search of a mass chromatogram or infrared spectrum, it can impact detection in radiography (38) and many other domains. In all of these examples, the human examiner is driven by a “target” they expect (or want) rather than by the actual data.

(3) Contextual Information

Experts are often exposed to irrelevant information. In the forensic domain, for example, such information may be that the suspect confessed to the crime, that they have been identified by eyewitnesses and other lines of evidence, or that the suspect has a criminal record. (47) Even knowing the name of the suspect may be suggestive of a specific race evoking biases and stereotypes. These all cause expectations that can impact not only the interpretation of the results obtained from the analysis, but also the analysis itself because the expectations impact the detection of what goes into the analysis as well as testing strategies. This source of bias is not derived from a target generated by the reference materials (see above) but from contextual information that can be irrelevant to the task carried out by the analyst.
In toxicology, for example, contextual information can bias testing strategies. Consider, for instance, when a post-mortem case is provided with contextual informant, such as “drug-overdose” or/and that “the deceased was a known to have a history of heroin use.” Such information can impact the established testing strategies, such as to go straight to the confirmation and quantification of a limited range of opiate-type drugs (morphine, codeine, 6-monoacetylmorphine, and other heroin markers), without running the other standard testing, such as an immunoassay or looking for other opioids that may have been present (e.g., fentanyl). Hence, the contextual information caused a confirmation bias approach and deviation from the standard testing and screening protocols. This has indeed caused errors in toxicology testing cases. (10)
In the forensic context, for example, irrelevant contextual information has been shown to impact data collection at the crime scene, (48) as well as bias in laboratory DNA analysis (35) (for reviews, see refs (5,6)). Similarly, deviation from standard testing strategies, data collection, and sampling can be biased by contextual information when the laboratory does analyses knowing that a Drug Recognition Expert (DRE) evaluated a person as being impaired and under the influence of a stimulant, but there was no alcohol in their blood, or when a dog signals the presence of drugs, or any other contextual information that causes an expectation to what the results should be.
Contextual expectations not only impact data collection and testing strategies but also the interpretation and conclusions of the analysis. For example, “a poor-quality chromatographic or mass spectral match for a drug could be consciously or subconsciously “upgraded” to a positive result based on the expectation that the sample should be positive for that drug. Conversely, a similarly poor-quality match might be “downgraded” to negative if the analyst is not expecting the drug to be present, or its presence ‘does not fit the circumstances of the case’. In both situations, the context could give the illusion of a stronger basis for the decision than is warranted by the data” (ref (9), page 381). Cognitive biases arise when task-irrelevant context causes some aspect of analysis to be overweighted, underweighted, or neglected (e.g., not perceived, determined to be noise, an anomaly, or an outlier). This does not only happen in subjective judgements but can also bias even established procedures, (10) and criteria for accepting evidence and proper judgment. (49)
The problem with task irrelevant contextual information is that it can cause many kinds of biases that impact analysis in many different ways. Another impact of bias can be overlooking or underweighting the absence of data, not properly confirming results, or not considering alternatives. Consider, for example, how biasing terrorism contextual information can result in a false positive identification of dynamite. The Griess test, a presumptive color test for explosives, (50) was positive for nitroglycerine. However, the positive result could be gained from nitrocellulose in a range of innocent non-terrorism related products. Nevertheless, because of the contextual information, it was wrongly concluded that the appearance of a pink color meant the suspects handled dynamite, when actually the traces were generated from an old pack of playing cards that they had been shuffling. (51) The suspects were wrongfully convicted and were sentenced to life imprisonment, only to be exonerated and released from jail after serving 16 years following a review of the testing used to obtain their original convictions. (52) Such errors happen when analysts are biased to accept results that are expected or wanted, without carrying out proper confirmation or considering alternatives.
Another example is hair-strand drug and alcohol testing. Even though there is a known false-positive problem with immunoassays, and therefore, results must be confirmed by another more specific technique, these were not always property carried out when results were in line with contextual information. (53)
It is important to emphasize that contextual irrelevant information biases scientists and experts, and it can do so at an unconscious level–they may not be aware of the impact. The expectation biases what and how information is represented and processed in the brain. (41−46) These biases impact experts and cannot be properly controlled by mere willpower (see the six bias fallacies above).

(4) Base Rate

An important asset that experts bring to thier work is their experience from previous cases. However, such experience brings expectations to new cases, that are not derived from the specific case or analysis at hand, but nevertheless can still impact their interpretation. Hence, the sampling and analysis being conducted are impacted by factors that have nothing to do with the case at hand, but rather with expected base rates generated from previous unrelated cases, which influence how this case is conducted.
For example, when the cause of death is cerebral hypoxia caused by hanging, then the manner of death is most often suicide. In contrast, when cerebral hypoxia is caused by strangulation, then the manner of death is most often homicide. This is not necessarily the case since, although rarely, hanging can be a result of a homicide and strangulation can be a suicide. However, the base rate of associations between the cause and manner of death can bias the interpretation and determination of manner of death. Such base rate biases are common in many domains from medical diagnosis to security X-rays at airports. (54)
The biasing effects of base rates are not limited to how the results of the analysis are interpreted. They can impact other stages of the analysis, even the sampling and data collection, as well as detection of relevant marks, items, or signals, or even verification. For example, low target prevalence base rate bias shows that if in past experiences it was rare or uncommon to find, then observers are more likely to miss it in the future. (55,56) Hence, even when observers are searching for an item, mark, or signal, then base rate can bias their search: relatively rare and uncommon items, signals, or objects will make observers more likely to miss them even when they are present.
Base rate bias derives from expectations generated from past similar cases. (46) The issue is that this case is biased because its analysis is actually based on other cases. The crux of the bias is that perception and decisions are not based on the case itself. This type of bias is even more potent when the similarity to past cases is superficial and more in the eye of the beholder than in reality. (57)

(5) Organizational Factors

Organizational factors that can cause bias are many and varied, and have been well documented in a variety of domains. When it comes to DNA and other forensic evidence, where analysis and work is often conducted within the adversarial legal system, cognitive bias may emerge from an allegiance effect and myside bias. (58) Indeed, a study showed that when forensic experts are presented with identical data, they nevertheless reach conclusions biased toward the side that retained them (59)—an adversarial allegiance and myside bias. These are implicit biases, not explicit partiality when one side is openly favored over the other.
Many forensic science laboratories are part of law enforcement (or even part of the DA prosecution office). Such organizational influences have been recognized as biasing by the National Academy of Sciences Report, calling for “removing all public forensic laboratories and facilities from the administrative control of law enforcement agencies or prosecutors’ offices” (ref (60), Recommendation #4, page 24). The point is that organizational factors and the administration surrounding forensic science induces biases. (61)
The impact of organizational factors applies to any and every laboratory—they work within a variety of contexts, structures, and frameworks that can bias their work. For example, laboratories have clear hierarchy and dependencies. If there is a senior person who “signs off” on reports or analyses, there can be the danger of “writing what that person wants to read” and a lack of challenge of their scientific decisions. Thus, science is muddled with managerial authority and other organizational pressures. (62,63)
Other organizational factors relate to time pressure, expectations to reach certain results, stress, budget controls, pressure to obtain publications and other targets, and a whole range of organizational factors that can impact the work carried out in laboratories and other professional settings.

(6) Education and Training

Education and training play an important role in how work is conducted. For example, whether forensic examiners see their role more as supporting the police rather than as scientists. When approaching a case, training and education may instil the pursuit of a single hypothesis vs examining multiple hypotheses, considering alterative hypotheses (including scenarios proposed by the opposing side), conducting differential diagnosis, considering categorical decisions (such as “match” and “nonmatch”, often used in fingerprinting and firearms) vs using statistics and other methods to determine the strength of the evidence, etc. Digital forensics, firearms, fingerprinting, and many forensic domains have actually grown out of police work with minimal-to-no proper education and training in science.

(7) Personal Factors

Many personal factors impact biases and decision making. These include motivation, personal ideology and beliefs. Furthermore, some people are more risk takers and others are more risk averse, and people also vary in their tolerance to ambiguity. (64) Other individual differences between people can bias results, for example, tests that use color can be biased because people differ in what color they perceive when looking at the same item. (37)
In areas where there is more objective quantification and instrumentation, these factors are minimized. However, in areas where the human examiner has a greater role in deciding how to collect, sample and interpret the data, and where there is subjectivity in evaluatating the data and conclusions, then such personal factors play a greater role in how work is carried out.
Even when technology is used, human biases are still at play. Technology cannot be totally objective as humans are involved in construction and operation of the technology, as well as calibrating it, maintaining it, and interpreting the results and deciding if and what action to take. (65) Indeed, ISO (International Organization for Standardization) standard 17025:2017 (general requirements for the competence of testing and calibration of laboratories that rely more on instrumentation and objective quantification) (66) now follows the standard for the more subjective laboratory domains, ISO 17020 and ISO 17025, and includes specific requirements for impartiality and freedom from bias. Hence, it acknowledges the role of the human examiner, even when quantification and instrumentation are used. It recognizes that even the use of instrumentation does not guarantee freedom from bias. (67)
Other personal factors that can cause bias in decisions include the need for closure that can result in premature decisions or opting to reach inconclusive decisions, (68) how people respond to stress and fatigue, personality, and a whole range of personal factors that can impact expert decision making. (69−71)

(8) Human and Cognitive Factors, and the Human Brain

The workings of our brain create architectural and capacity constraints that do not allow it to process all the incoming information. The brain therefore engages in a variety of processes (mainly known as “top-down”) to make sense of the world and data around us. The human mind is not a camera, the active nature of human cognition means that we do not see the world “as it is.”
Beyond many cognitive processes and how the human brain is wired which can cause biases, there are biasing affects related to social interaction, in-group and availability biases, processing fluency, and other biasing influences that impact all of us. (72−75)

Snowball and Cascade Bias

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Bias does not impact only the individual in isolation or just one aspect of the work; often the bias cascades from one person to another, from one aspect of the work to another, influencing different elements of an investigation. As people and various aspects are influenced, they then influence others, turning from influenced to influencers, perpetuating the bias and impacting others. Then, biases are not only cascaded but gather momentum and snowball. (32)

Overcoming Bias

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First, we need to acknowledge the existence of bias and move beyond believing the fallacies about its nature. When people have a bias blind spot or think that as experts they are immune to it or that by mere willpower they can overcome it (see the six bias fallacies in Table 1), then biases only perpetuate.
Second, as a general principle to combat bias, we need to take actions that will cause us to focus solely on the relevant data and not work backward. These need to be part of ongoing training and laboratory procedures. Accreditation to the appropriate standards and certification in the appropriate discipline may not solve the problems, but they do force laboratories to document procedures. External scrutiny can also be very helpful for illuminating areas of bias (especially given that ISOs (e.g., 17020 and 17025 (66)) specifically require that steps are taken to make sure there is freedom from bias. (67)
Third, specifically, we can combat various sources of bias by the following:
(A)

Using blinding and masking techniques that prevent exposure to task irrelevant information. (76)

(B)

Using methods, such as Linear Sequential Unmasking (LSU), to control the sequence, timing, and linearity of exposure to information, so as to minimize “going backward” and being biased by the reference materials. (32)

(C)

Using case managers that screen and control what information is given to whom and when.

(D)

Using blind, double blind, and proper verifications when possible.

(E)

Rather than have one “reference target” or hypothesis, having a “line up” of competing and alternative conclusions and hypotheses.

(F)

Adopting a differential diagnosis approach, where all different conclusions and their probability are presented, rather than one conclusion. (77,78)

Summary and Conclusions

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Biases, often without our awareness or consciousness, impact how we sample and perceive data, decide on testing strategies and how we interpret the results. People’s beliefs in various fallacies about bias prevent a proper understanding of bias, which in turn precludes taking steps to fight it. Understanding biases and acknowledging them enables discussion about their sources and to develop means to minimize their impact.
Six fallacies are presented (Table 1), which are widely held beliefs about biases that prevent correct conceptualization of bias. Then, sources of biases are presented (Figure 1), which emerge from the specific case being analyzed, the specific person doing the analysis, and from basic human nature that impacts us all. By presenting those fallacies and sources of bias, the hope is that people will reflect on their own practices and consider whether cognitive biases may unwillingly influence their work. Discussing bias often encounters defensive responses, not many want to consider and acknowledge their own biases, let alone research them. However, it is essential to do so if we are to combat and minimize bias. It is the hope of the author that this paper contributes to that direction.

Author Information

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Acknowledgments

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I want to thank Hilary Hamnett, Nikolas P. Lemos, Joseph Almog, Roderick Kennedy, and anonymous reviewers for their helpful comments on an earlier version of this perspective.

References

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    One of the main goals of the Spanish and Portuguese-Speaking Group of the International Society for Forensic Genetics (GHEP-ISFG) is to promote and contribute to the development and dissemination of scientific knowledge in the field of forensic genetics. Due to this fact, GHEP-ISFG holds different working commissions that are set up to develop activities in scientific aspects of general interest. One of them, the Mixt. Commission of GHEP-ISFG, has organized annually, since 2009, a collaborative exercise on anal. and interpretation of autosomal short tandem repeat (STR) mixt. profiles. Until now, six exercises have been organized. At the present edition (GHEP-MIX06), with 25 participant labs., the exercise main aim was to assess mixt. profiles results by issuing a report, from the proposal of a complex mock case. One of the conclusions obtained from this exercise is the increasing tendency of participating labs. to validate DNA mixt. profiles anal. following international recommendations. However, the results have shown some differences among them regarding the edition and also the interpretation of mixt. profiles. Besides, although the last revision of ISO/IEC 17025:2017 gives indications of how results should be reported, not all labs. strictly follow their recommendations. Regarding the statistical aspect, all those labs. that have performed statistical evaluation of the data have employed the likelihood ratio (LR) as a parameter to evaluate the statistical compatibility. However, LR values obtained show a wide range of variation. This fact could not be attributed to the software employed, since the vast majority of labs. that performed LR calcn. employed the same software (LRmixStudio). Thus, the final allelic compn. of the edited mixt. profile and the parameters employed in the software could explain this data dispersion. This highlights the need, for each lab., to define through internal validations its criteria for editing and interpreting mixts., and to continuous train in software handling.
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  3. 3
    Butler, J. M.; Kline, M. C.; Coble, M. D. Forensic Sci. Int.: Genet. 2018, 37, 81– 94,  DOI: 10.1016/j.fsigen.2018.07.024
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    NIST interlaboratory studies involving DNA mixtures (MIX05 and MIX13): Variation observed and lessons learned
    Butler, John M.; Kline, Margaret C.; Coble, Michael D.
    Forensic Science International: Genetics (2018), 37 (), 81-94CODEN: FSIGA3; ISSN:1872-4973. (Elsevier Ireland Ltd.)
    Interlab. studies are a type of collaborative exercise in which many labs. are presented with the same set of data to interpret, and the results they produce are examd. to get a "big picture" view of the effectiveness and accuracy of anal. protocols used across participating labs. In 2005 and again in 2013, the Applied Genetics Group of the National Institute of Stds. and Technol. (NIST) conducted interlab. studies involving DNA mixt. interpretation. In the 2005 NIST MIX05 study, 69 labs. interpreted data in the form of electropherograms of two-person DNA mixts. representing four different mock sexual assault cases with different contributor ratios. In the 2013 NIST MIX13 study,108 labs. interpreted electropherogram data for five different case scenarios involving two, three, or four contributors, with some of the contributors potentially related. This paper describes the design of these studies, the variations obsd. among lab. results, and lessons learned.
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  4. 4
    Bright, J.-A.; Cheng, K.; Kerr, Z.; McGovern, C.; Kelly, H.; Moretti, T. R.; Smith, M. A.; Bieber, F. R.; Budowle, B.; Coble, M. D.; Alghafri, R.; Allen, P. S.; Barber, A.; Beamer, V.; Buettner, C.; Russell, M.; Gehrig, C.; Hicks, T.; Charak, J.; Cheong-Wing, K.; Ciecko, A.; Davis, C. T.; Donley, M.; Pedersen, N.; Gartside, B.; Granger, D.; Greer-Ritzheimer, M.; Reisinger, E.; Kennedy, J.; Grammer, E.; Kaplan, M.; Hansen, D.; Larsen, H. J.; Laureano, A.; Li, C.; Lien, E.; Lindberg, E.; Kelly, C.; Mallinder, B.; Malsom, S.; Yacovone-Margetts, A.; McWhorter, A.; Prajapati, S. M.; Powell, T.; Shutler, G.; Stevenson, K.; Stonehouse, A. R.; Smith, L.; Murakami, J.; Halsing, E.; Wright, D.; Clark, L.; Taylor, D. A.; Buckleton, J. Forensic Sci. Int.: Genet. 2019, 40, 1– 8,  DOI: 10.1016/j.fsigen.2019.01.006
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    STRmix collaborative exercise on DNA mixture interpretation
    Bright, Jo-Anne; Cheng, Kevin; Kerr, Zane; McGovern, Catherine; Kelly, Hannah; Moretti, Tamyra R.; Smith, Michael A.; Bieber, Frederick R.; Budowle, Bruce; Coble, Michael D.; Alghafri, Rashed; Allen, Paul Stafford; Barber, Amy; Beamer, Vickie; Buettner, Christina; Russell, Melanie; Gehrig, Christian; Hicks, Tacha; Charak, Jessica; Cheong-Wing, Kate; Ciecko, Anne; Davis, Christie T.; Donley, Michael; Pedersen, Natalie; Gartside, Bill; Granger, Dominic; Greer-Ritzheimer, MaryMargaret; Reisinger, Erick; Kennedy, Jarrah; Grammer, Erin; Kaplan, Marla; Hansen, David; Larsen, Hans J.; Laureano, Alanna; Li, Christina; Lien, Eugene; Lindberg, Emilia; Kelly, Ciara; Mallinder, Ben; Malsom, Simon; Yacovone-Margetts, Alyse; McWhorter, Andrew; Prajapati, Sapana M.; Powell, Tamar; Shutler, Gary; Stevenson, Kate; Stonehouse, April R.; Smith, Lindsey; Murakami, Julie; Halsing, Eric; Wright, Darren; Clark, Leigh; Taylor, Duncan A.; Buckleton, John
    Forensic Science International: Genetics (2019), 40 (), 1-8CODEN: FSIGA3; ISSN:1872-4973. (Elsevier Ireland Ltd.)
    An intra and inter-lab. study using the probabilistic genotyping (PG) software STRmix is reported. Two complex mixts. from the PROVEDIt set, analyzed on an Applied Biosystems 3500 Series Genetic Analyzer, were selected. 174 participants responded. For Sample 1 (low template, in the order of 200 rfu for major contributors) five participants described the comparison as inconclusive with respect to the POI or excluded him. Where LRs were assigned, the point ests. ranging from 2 × 104 to 8 × 106. For Sample 2 (in the order of 2000 rfu for major contributors), LRs ranged from 2 × 1028 to 2 × 1029. Where LRs were calcd., the differences between participants can be attributed to (from largest to smallest impact):varying no. of contributors (NoC),the exclusion of some loci within the interpretation,differences in local CE data anal. methods leading to variation in the peaks present and their heights in the input files used,and run-to-run variation due to the random sampling inherent to all MCMC-based methods. This study demonstrates a high level of repeatability and reproducibility among the participants. For those results that differed from the mode, the differences in LR were almost always minor or conservative.
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    Cognitive bias research in forensic science: A systematic review
    Cooper Glinda S; Meterko Vanessa
    Forensic science international (2019), 297 (), 35-46 ISSN:.
    The extent to which cognitive biases may influence decision-making in forensic science is an important question with implications for training and practice. We conducted a systematic review of the literature on cognitive biases in forensic science disciplines. The initial literature search including electronic searching of three databases (two social science, one science) and manual review of reference lists in identified articles. An initial screening of title and abstract by two independent reviewers followed by full text review resulted in the identification of 29 primary source (research) studies. A critical methodological deficiency, serious enough to make the study too problematic to provide useful evidence, was identified in two of the studies. Most (n = 22) conducted analyses limited to practitioners (n = 17), forensic science trainees (n = 2), or both forensic science practitioners and students (n = 3); other analyses were based on university student or general population participants. Latent fingerprint analysis was examined in 11 studies, with 1-3 other studies found in 13 other disciplines or domains. This set of studies provides a robust database, with evidence of the influence of confirmation bias on analysts conclusions, specifically among the studies with practitioners or trainees presented with case-specific information about the "suspect" or crime scenario (in 9 of 11 studies examining this question), procedures regarding use of exemplar(s) (in 4 of 4 studies), or knowledge of a previous decision (in 4 of 4 studies). The available research supports the idea of susceptibility of forensic science practitioners to various types of confirmation bias and of the potential value of procedures designed to reduce access to unnecessary information and control the order of providing relevant information, use of multiple comparison samples rather than a single suspect exemplar, and replication of results by analysts blinded to previous results.
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    Future Medicinal Chemistry (2011), 3 (7), 771-774CODEN: FMCUA7; ISSN:1756-8919. (Future Science Ltd.)
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    Hamnett Hilary J; Jack Rachael E
    Science & justice : journal of the Forensic Science Society (2019), 59 (4), 380-389 ISSN:.
    Cognitive bias is a well-documented automatic process that can have serious negative consequences in a variety of settings. For example, cognitive bias within a forensic science setting can lead to examiners' judgements being swayed by details that they have learned while working on the case, and which go beyond the physical evidence being examined. Although cognitive bias has been studied in many forensic disciplines, such as fingerprints, bullet comparison, and document examination, knowledge of cognitive bias within forensic toxicology is lacking. Here, we address this knowledge gap by assessing the reported use of contextual information by an international group of forensic toxicologists attending the 54th conference of The International Association of Forensic Toxicologists (TIAFT) in Brisbane in 2016. In a first study, participants read a set of simple post-mortem toxicology results (two drug concentrations in blood) and then indicated what information they would normally use when interpreting these results in their day-to-day casework. Using a questionnaire, we then surveyed the familiarity of toxicologists with contextual bias and captured any suggested bias-minimizing procedures for use in forensic toxicology laboratories. Thirty-six participants from 23 different countries and with a range of 1-35 years' forensic toxicology reporting experience volunteered. Analysis of their responses showed that the majority of participants reported using some contextual information in their interpretation of these post-mortem toxicology results (range = 3-15 pieces of information, median ± SD = 11 ± 3), the most common being the deceased's history of prescription or illicit drug use. More than three-quarters of participants reported being familiar with the concept of contextual bias, although few (n = 9) worked in laboratories that had a formal policy covering it. Over half of participants knew of at least one bias-minimizing procedure specifically for forensic toxicology casework, but only a quarter (overall) reported using bias-minimizing procedures in their laboratories. Our results provide substantial evidence that although practising forensic toxicologists are familiar with contextual bias, many report that they still engage in behaviours that could lead to cognitive bias (e.g., through the use of contextual information, through lack of explicit policies or bias-minimizing procedures). We anticipate that our work will form the basis of further research involving a larger sample of participants and examining other potentially relevant factors such as sex/gender, country and accreditation of laboratories.
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    The curse of observer experience: Error in noninvasive genetic sampling
    Soller, Jillian M.; Ausband, David E.; Szykman Gunther, Micaela
    PLoS One (2020), 15 (3), e0229762CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
    Noninvasive genetic sampling (NGS) is commonly used to study elusive or rare species where direct observation or capture is difficult. Little attention has been paid to the potential effects of observer bias while collecting noninvasive genetic samples in the field, however. Over a period of 7 years, we examd. whether different observers (n = 58) and observer experience influenced detection, amplification rates, and correct species identification of 4,836 Gy wolf (Canis lupus) fecal samples collected in Idaho and Yellowstone National Park, USA and southwestern Alberta, Canada (2008-2014). We compared new observers (n = 33) to experienced observers (n = 25) and hypothesized experience level would increase the overall success of using NGS techniques in the wild. In contrast to our hypothesis, we found that new individuals were better than experienced observers at detecting and collecting wolf scats and correctly identifying wolf scats from other sympatric carnivores present in the study areas. While adequate training of new observers is crucial for the successful use of NGS techniques, attention should also be directed to experienced observers. Observer experience could be a curse because of their potential effects on NGS data quality arising from fatigue, boredom or other factors. The ultimate benefit of an observer to a project is a combination of factors (i.e., field savvy, local knowledge), but project investigators should be aware of the potential neg. effects of experience on NGS sampling.
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    Experts play a critical role in forensic decision making, even when cognition is offloaded and distributed between human and machine. In this paper, we investigated the impact of using Automated Fingerprint Identification Systems (AFIS) on human decision makers. We provided 3680 AFIS lists (a total of 55,200 comparisons) to 23 latent fingerprint examiners as part of their normal casework. We manipulated the position of the matching print in the AFIS list. The data showed that latent fingerprint examiners were affected by the position of the matching print in terms of false exclusions and false inconclusives. Furthermore, the data showed that false identification errors were more likely at the top of the list and that such errors occurred even when the correct match was present further down the list. These effects need to be studied and considered carefully, so as to optimize human decision making when using technologies such as AFIS.
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    Strengthening forensic DNA decision making through a better understanding of the influence of cognitive bias
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    Science & justice : journal of the Forensic Science Society (2017), 57 (6), 415-420 ISSN:1355-0306.
    Cognitive bias may influence process flows and decision making steps in forensic DNA analyses and interpretation. Currently, seven sources of bias have been identified that may affect forensic decision making with roots in human nature; environment, culture, and experience; and case specific information. Most of the literature and research on cognitive bias in forensic science has focused on patterned evidence; however, forensic DNA testing is not immune to bias, especially when subjective interpretation is involved. DNA testing can be strengthened by recognizing the existence of bias, evaluating where it influences decision making, and, when applicable, implementing practices to reduce or control its effects. Elements that may improve forensic decision making regarding bias include cognitively informed education and training, quality assurance procedures, review processes, analysis and interpretation, and context management of irrelevant information. Although bias exists, reliable results often can be (and have been) produced. However, at times bias can (and has) impacted the interpretation of DNA results negatively. Therefore, being aware of the dangers of bias and implementing measures to control its potential impact should be considered. Measures and procedures that handicap the workings of the crime laboratory or add little value to improving the operation are not advocated, but simple yet effective measures are suggested. This article is meant to raise awareness of cognitive bias contamination in forensic DNA testing and to give laboratories possible pathways to make sound decisions to address its influences.
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    Subjectivity and bias in forensic DNA mixture interpretation
    Dror, Itiel E.; Hampikian, Greg
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    The objectivity of forensic science decision making has received increased attention and scrutiny. However, there are only a few published studies exptl. addressing the potential for contextual bias. Because of the esteem of DNA evidence, it is important to study and assess the impact of subjectivity and bias on DNA mixt. interpretation. The study reported here presents empirical data suggesting that DNA mixt. interpretation is subjective. When 17 North American expert DNA examiners were asked for their interpretation of data from an adjudicated criminal case in that jurisdiction, they produced inconsistent interpretations. Furthermore, the majority of 'context free' experts disagreed with the lab.'s pre-trial conclusions, suggesting that the extraneous context of the criminal case may have influenced the interpretation of the DNA evidence, thereby showing a biasing effect of contextual information in DNA mixt. interpretation.
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    Circular analysis in systems neuroscience: the dangers of double dipping
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    Nature Neuroscience (2009), 12 (5), 535-540CODEN: NANEFN; ISSN:1097-6256. (Nature Publishing Group)
    A neuroscientific expt. typically generates a large amt. of data, of which only a small fraction is analyzed in detail and presented in a publication. However, selection among noisy measurements can render circular an otherwise appropriate anal. and invalidate results. Here we argue that systems neuroscience needs to adjust some widespread practices to avoid the circularity that can arise from selection. In particular, 'double dipping', the use of the same dataset for selection and selective anal., will give distorted descriptive statistics and invalid statistical inference whenever the results statistics are not inherently independent of the selection criteria under the null hypothesis. To demonstrate the problem, we apply widely used analyses to noise data known to not contain the exptl. effects in question. Spurious effects can appear in the context of both univariate activation anal. and multivariate pattern-information anal. We suggest a policy for avoiding circularity.
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    Prediction plays a crucial role in perception, as prominently suggested by predictive coding theories. However, the exact form and mechanism of predictive modulations of sensory processing remain unclear, with some studies reporting a downregulation of the sensory response for predictable input whereas others obsd. an enhanced response. In a similar vein, downregulation of the sensory response for predictable input has been linked to either sharpening or dampening of the sensory representation, which are opposite in nature. In the present study, we set out to investigate the neural consequences of perceptual expectation of object stimuli throughout the visual hierarchy, using fMRI in human volunteers. Participants of both sexes were exposed to pairs of sequentially presented object images in a statistical learning paradigm, in which the first object predicted the identity of the second object. Image transitions were not task relevant; thus, all learning of statistical regularities was incidental. We found strong suppression of neural responses to expected compared with unexpected stimuli throughout the ventral visual stream, including primary visual cortex, lateral occipital complex, and anterior ventral visual areas. Expectation suppression in lateral occipital complex scaled pos. with image preference and voxel selectivity, lending support to the dampening account of expectation suppression in object perception.
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    What is the role of selective attention in visual perception. Before answering this question, it is necessary to differentiate between attentional mechanisms that influence the identification of a stimulus from those that operate after perception is complete. Cognitive neuroscience techniques are particularly well suited to making this distinction because they allow different attentional mechanisms to be isolated in terms of timing and/or neuroanatomy. The present article describes the use of these techniques in differentiating between perceptual and postperceptual attentional mechanisms and then proposes a specific role of attention in visual perception. Specifically, attention is proposed to resolve ambiguities in neural coding that arise when multiple objects are processed simultaneously. Evidence for this hypothesis is provided by two expts. showing that attention-as measured electrophysiol.-is allocated to visual search targets only under conditions that would be expected to lead to ambiguous neural coding.
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    Journal of experimental psychology. General (2015), 144 (6), 1089-104 ISSN:.
    The detectability of an object in our visual environment is primarily determined by the object's low-level visual salience, resulting from the physical characteristics of the object and its surroundings. In the present study we demonstrate that object detectability is additionally influenced by internally generated expectations about object properties, and that these influences are mediated by changes in perceptual sensitivity. Using continuous flash suppression (CFS) to render objects invisible, we found that providing valid information about the category membership of the object (e.g., "car") before stimulus presentation facilitated awareness of the object, as shown by improved localization performance relative to a noninformative baseline condition and to a condition with invalid prior information. Experiments 2 and 3 showed that the effect of expectation on detection generalized to binocular viewing conditions, with valid category cues facilitating the localization and detection of briefly presented objects. Experiment 4 extended these results to simple stimuli (oriented Gabor patches), for which valid orientation information improved localization performance. Finally, in Experiment 5 we found that the effect of expectation on detection and localization performance partly reflects increased perceptual sensitivity, as evidenced by decreased contrast detection thresholds for validly cued stimuli relative to noncued and invalidly cued stimuli. Together, these findings demonstrate that prior information about specific object properties dynamically enhances the effective signal of visual input matching the expected content, thereby biasing object detection in favor of expected objects.
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    Kok, P.; Brouwer, G. J.; van Gerven, M. A. J.; de Lange, F. P. J. Neurosci. 2013, 33, 16275– 16284,  DOI: 10.1523/JNEUROSCI.0742-13.2013
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    Journal of Neuroscience (2013), 33 (41), 16275-16284CODEN: JNRSDS; ISSN:0270-6474. (Society for Neuroscience)
    Perception is strongly influenced by expectations. Accordingly, perception has sometimes been cast as a process of inference, whereby sensory inputs are combined with prior knowledge. However, despite a wealth of behavioral literature supporting an account of perception as probabilistic inference, the neural mechanisms underlying this process remain largely unknown. One important question is whether top-down expectation biases stimulus representations in early sensory cortex, i.e., whether the integration of prior knowledge and bottom-up inputs is already observable at the earliest levels of sensory processing. Alternatively, early sensory processing may be unaffected by top-down expectations, and integration of prior knowledge and bottom-up input may take place in downstream assocn. areas that are proposed to be involved in perceptual decision-making. Here, we implicitly manipulated human subjects' prior expectations about visual motion stimuli, and probed the effects on both perception and sensory representations in visual cortex. To this end, we measured neural activity noninvasively using functional magnetic resonance imaging, and applied a forward modeling approach to reconstruct the motion direction of the perceived stimuli from the signal in visual cortex. Our results show that top-down expectations bias representations in visual cortex, demonstrating that the integration of prior information and sensory input is reflected at the earliest stages of sensory processing.
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    How Do Expectations Shape Perception?
    de Lange Floris P; Heilbron Micha; Kok Peter
    Trends in cognitive sciences (2018), 22 (9), 764-779 ISSN:.
    Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.
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    Do evidence submission forms expose latent print examiners to task-irrelevant information?
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    Emerging research documents the ways in which task-irrelevant contextual information may influence the opinions and decisions that forensic analysts reach regarding evidence (e.g., Dror and Cole, 2010; National Academy of Sciences, 2009; President's Council of Advisors on Science and Technology, 2016). Consequently, authorities have called for forensic analysts to rely solely on task-relevant information-and to actively avoid task-irrelevant information-when conducting analyses (National Commission on Forensic Science, 2015). In this study, we examined 97 evidence submission forms, used by 148 accredited crime laboratories across the United States, to determine what types of information laboratories solicit when performing latent print analyses. Results indicate that many laboratories request information with no direct relevance to the specific task of latent print comparison. More concerning, approximately one in six forms (16.5%) request information that appears to have a high potential for bias without any discernible relevance to latent print comparison. Solicitations for task-irrelevant information may carry meaningful consequences and current findings inform strategies to reduce the potential for cognitive bias.
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    OBJECTIVE: To determine whether radiologists' interpretations of images are biased by their context and by prevalence of disease in other recently observed cases. METHODS: A test set of 24 right pulmonary arteriograms with a 33% prevalence of pulmonary emboli (PE) was assembled and embedded in 2 larger groups of films. Group A contained 16 additional arteriograms, all showing PE involving the right lung, so that total prevalence was 60%. Group B contained 16 additional arteriograms without PE so that total prevalence was 20%. Six radiologists were randomly assigned to see either group first and then "cross over" to review the other group after a hiatus of at least 8 weeks. The direction of changes in a 5-point rating scale for the 2 readings of each film in the test set was compared with the sign test; mean sensitivity, specificity, and areas under receiver operating characteristic (ROC) curves were compared with the paired t test. RESULTS: In the context of group A's higher disease prevalence, radiologists shifted more of their diagnoses toward higher suspicion than expected by chance (P=.03, sign test). In group A, mean sensitivity for diagnosing PE was significantly higher (75% vs 60%; P=.04), and area under the ROC curve was significantly larger (0.88 vs 0.82; P=.02). CONCLUSIONS: Radiologists' diagnoses are significantly influenced by the context of interpretation, even when spectrum and verification bias are avoided. This "context bias" effect is unique to the evaluation of subjectively interpreted tests, and illustrates the difficulty of obtaining unbiased estimates of diagnostic accuracy for both new and existing technologies.
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    Nature (London, United Kingdom) (2005), 435 (7041), 439-440CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    There is no expanded citation for this reference.
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    In visual search tasks, observers look for targets in displays containing distractors. Likelihood that targets will be missed varies with target prevalence, the frequency with which targets are presented across trials. Miss error rates are much higher at low target prevalence (1%-2%) than at high prevalence (50%). Unfortunately, low prevalence is characteristic of important search tasks such as airport security and medical screening where miss errors are dangerous. A series of experiments show this prevalence effect is very robust. In signal detection terms, the prevalence effect can be explained as a criterion shift and not a change in sensitivity. Several efforts to induce observers to adopt a better criterion fail. However, a regime of brief retraining periods with high prevalence and full feedback allows observers to hold a good criterion during periods of low prevalence with no feedback.
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    How objective are forensic experts when they are retained by one of the opposing sides in an adversarial legal proceeding? Despite long-standing concerns from within the legal system, little is known about whether experts can provide opinions unbiased by the side that retained them. In this experiment, we paid 108 forensic psychologists and psychiatrists to review the same offender case files, but deceived some to believe that they were consulting for the defense and some to believe that they were consulting for the prosecution. Participants scored each offender on two commonly used, well-researched risk-assessment instruments. Those who believed they were working for the prosecution tended to assign higher risk scores to offenders, whereas those who believed they were working for the defense tended to assign lower risk scores to the same offenders; the effect sizes (d) ranged up to 0.85. The results provide strong evidence of an allegiance effect among some forensic experts in adversarial legal proceedings.
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    The term "authority gradient" was first defined in aviation when it was noted that pilots and copilots may not communicate effectively in stressful situations if there is a significant difference in their experience, perceived expertise, or authority. A number of unintentional aviation, aerospace, and industrial incidents have been attributed, in part, to authority gradients. The concept of authority gradient was introduced to medicine in the Institute of Medicine report To Err Is Human, yet little has been written or acknowledged in the medical literature regarding its role in medical error. The practice of medicine and medical training programs are highly organized, hierarchical structures that depend on supervision by authority figures. The concept that authority gradients might contribute to medical error is largely unrecognized. This article presents one case and a series of examples to detail how authority gradients can contribute to medical error, and describes methods used in other disciplines to avoid their potentially negative impact.
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    BMC medical informatics and decision making (2016), 16 (1), 138 ISSN:.
    BACKGROUND: Cognitive biases and personality traits (aversion to risk or ambiguity) may lead to diagnostic inaccuracies and medical errors resulting in mismanagement or inadequate utilization of resources. We conducted a systematic review with four objectives: 1) to identify the most common cognitive biases, 2) to evaluate the influence of cognitive biases on diagnostic accuracy or management errors, 3) to determine their impact on patient outcomes, and 4) to identify literature gaps. METHODS: We searched MEDLINE and the Cochrane Library databases for relevant articles on cognitive biases from 1980 to May 2015. We included studies conducted in physicians that evaluated at least one cognitive factor using case-vignettes or real scenarios and reported an associated outcome written in English. Data quality was assessed by the Newcastle-Ottawa scale. Among 114 publications, 20 studies comprising 6810 physicians met the inclusion criteria. Nineteen cognitive biases were identified. RESULTS: All studies found at least one cognitive bias or personality trait to affect physicians. Overconfidence, lower tolerance to risk, the anchoring effect, and information and availability biases were associated with diagnostic inaccuracies in 36.5 to 77 % of case-scenarios. Five out of seven (71.4 %) studies showed an association between cognitive biases and therapeutic or management errors. Of two (10 %) studies evaluating the impact of cognitive biases or personality traits on patient outcomes, only one showed that higher tolerance to ambiguity was associated with increased medical complications (9.7 % vs 6.5 %; p = .004). Most studies (60 %) targeted cognitive biases in diagnostic tasks, fewer focused on treatment or management (35 %) and on prognosis (10 %). Literature gaps include potentially relevant biases (e.g. aggregate bias, feedback sanction, hindsight bias) not investigated in the included studies. Moreover, only five (25 %) studies used clinical guidelines as the framework to determine diagnostic or treatment errors. Most studies (n = 12, 60 %) were classified as low quality. CONCLUSIONS: Overconfidence, the anchoring effect, information and availability bias, and tolerance to risk may be associated with diagnostic inaccuracies or suboptimal management. More comprehensive studies are needed to determine the prevalence of cognitive biases and personality traits and their potential impact on physicians' decisions, medical errors, and patient outcomes.
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    ISO Standards Addressing Issues of Bias and Impartiality in Forensic Work
    Dror Itiel E; Pierce Michal L
    Journal of forensic sciences (2020), 65 (3), 800-808 ISSN:.
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Analytical Chemistry

Cite this: Anal. Chem. 2020, 92, 12, 7998–8004
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https://doi.org/10.1021/acs.analchem.0c00704
Published June 8, 2020

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  • Abstract

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    Figure 1

    Figure 1. Eight sources of bias that may cognitively contaminate sampling, observations, testing strategies, analysis, and conclusions, even by experts. They are organized in a taxonomy within three categories: starting off at the top with sources relating to the specific case and analysis (Category A), moving down to sources that relate to the specific person doing the analysis (Category B), and at the very bottom sources that relate to human nature (Category C).

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  • References

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      The extent to which cognitive biases may influence decision-making in forensic science is an important question with implications for training and practice. We conducted a systematic review of the literature on cognitive biases in forensic science disciplines. The initial literature search including electronic searching of three databases (two social science, one science) and manual review of reference lists in identified articles. An initial screening of title and abstract by two independent reviewers followed by full text review resulted in the identification of 29 primary source (research) studies. A critical methodological deficiency, serious enough to make the study too problematic to provide useful evidence, was identified in two of the studies. Most (n = 22) conducted analyses limited to practitioners (n = 17), forensic science trainees (n = 2), or both forensic science practitioners and students (n = 3); other analyses were based on university student or general population participants. Latent fingerprint analysis was examined in 11 studies, with 1-3 other studies found in 13 other disciplines or domains. This set of studies provides a robust database, with evidence of the influence of confirmation bias on analysts conclusions, specifically among the studies with practitioners or trainees presented with case-specific information about the "suspect" or crime scenario (in 9 of 11 studies examining this question), procedures regarding use of exemplar(s) (in 4 of 4 studies), or knowledge of a previous decision (in 4 of 4 studies). The available research supports the idea of susceptibility of forensic science practitioners to various types of confirmation bias and of the potential value of procedures designed to reduce access to unnecessary information and control the order of providing relevant information, use of multiple comparison samples rather than a single suspect exemplar, and replication of results by analysts blinded to previous results.
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      Soller, Jillian M.; Ausband, David E.; Szykman Gunther, Micaela
      PLoS One (2020), 15 (3), e0229762CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      Noninvasive genetic sampling (NGS) is commonly used to study elusive or rare species where direct observation or capture is difficult. Little attention has been paid to the potential effects of observer bias while collecting noninvasive genetic samples in the field, however. Over a period of 7 years, we examd. whether different observers (n = 58) and observer experience influenced detection, amplification rates, and correct species identification of 4,836 Gy wolf (Canis lupus) fecal samples collected in Idaho and Yellowstone National Park, USA and southwestern Alberta, Canada (2008-2014). We compared new observers (n = 33) to experienced observers (n = 25) and hypothesized experience level would increase the overall success of using NGS techniques in the wild. In contrast to our hypothesis, we found that new individuals were better than experienced observers at detecting and collecting wolf scats and correctly identifying wolf scats from other sympatric carnivores present in the study areas. While adequate training of new observers is crucial for the successful use of NGS techniques, attention should also be directed to experienced observers. Observer experience could be a curse because of their potential effects on NGS data quality arising from fatigue, boredom or other factors. The ultimate benefit of an observer to a project is a combination of factors (i.e., field savvy, local knowledge), but project investigators should be aware of the potential neg. effects of experience on NGS sampling.
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    22. 22
      Dror, I. E.; Wertheim, K.; Fraser-Mackenzie, P.; Walajtys, J. J. Forensic Sci. 2012, 57 (2), 343– 352,  DOI: 10.1111/j.1556-4029.2011.02013.x
      22
      The impact of human-technology cooperation and distributed cognition in forensic science: biasing effects of AFIS contextual information on human experts
      Dror Itiel E; Wertheim Kasey; Fraser-Mackenzie Peter; Walajtys Jeff
      Journal of forensic sciences (2012), 57 (2), 343-52 ISSN:.
      Experts play a critical role in forensic decision making, even when cognition is offloaded and distributed between human and machine. In this paper, we investigated the impact of using Automated Fingerprint Identification Systems (AFIS) on human decision makers. We provided 3680 AFIS lists (a total of 55,200 comparisons) to 23 latent fingerprint examiners as part of their normal casework. We manipulated the position of the matching print in the AFIS list. The data showed that latent fingerprint examiners were affected by the position of the matching print in terms of false exclusions and false inconclusives. Furthermore, the data showed that false identification errors were more likely at the top of the list and that such errors occurred even when the correct match was present further down the list. These effects need to be studied and considered carefully, so as to optimize human decision making when using technologies such as AFIS.
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    23. 23
      Pronin, E.; Lin, D. Y.; Ross, L. Person. Soc. Psych. Bull. 2002, 28, 369– 381,  DOI: 10.1177/0146167202286008
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      Zapf, P.; Kukucka, J.; Kassin, S.; Dror, I. E. Psych., Pub. Policy Law 2018, 24 (1), 1– 10,  DOI: 10.1037/law0000153
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      Thornton, J. I. J. Forensic Sci. 2010, 55 (6), 1663,  DOI: 10.1111/j.1556-4029.2010.01497.x
      25
      Letter to the editor--a rejection of "working blind" as a cure for contextual bias
      Thornton John I
      Journal of forensic sciences (2010), 55 (6), 1663 ISSN:.
      There is no expanded citation for this reference.
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      Wegner, D. M. Psych. Rev. 1994, 101, 34– 52,  DOI: 10.1037/0033-295X.101.1.34
      26
      Ironic processes of mental control
      Wegner D M
      Psychological review (1994), 101 (1), 34-52 ISSN:0033-295X.
      A theory of ironic processes of mental control is proposed to account for the intentional and counterintentional effects that result from efforts at self-control of mental states. The theory holds that an attempt to control the mind introduces 2 processes: (a) an operating process that promotes the intended change by searching for mental contents consistent with the intended state and (b) a monitoring process that tests whether the operating process is needed by searching for mental contents inconsistent with the intended state. The operating process requires greater cognitive capacity and normally has more pronounced cognitive effects than the monitoring process, and the 2 working together thus promote whatever degree of mental control is enjoyed. Under conditions that reduce capacity, however, the monitoring process may supersede the operating process and thus enhance the person's sensitivity to mental contents that are the ironic opposite of those that are intended.
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      Steblay, N.; Hosch, H. M.; Culhane, S. E.; McWethy, A. Law Hum. Beh. 2006, 30, 469– 492,  DOI: 10.1007/s10979-006-9039-7
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      Zajonc, R. B. Am. Psychol. 1980, 35, 151– 175,  DOI: 10.1037/0003-066X.35.2.151
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      Finucane, M. L.; Alhakami, A.; Slovic, P.; Johnson, S. M. J. Behav. Decis. Making 2000, 13, 1– 17,  DOI: 10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S
      There is no corresponding record for this reference.
    30. 30
      Damasio, A. R. Descartes’ Error: Emotion, Reason, and the Human Brain; Penguin Books: New York, 2005; pp 1– 312.
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      Jeanguenat, A. M.; Budowle, B.; Dror, I. E. Sci. Justice 2017, 57 (6), 415– 420,  DOI: 10.1016/j.scijus.2017.07.005
      31
      Strengthening forensic DNA decision making through a better understanding of the influence of cognitive bias
      Jeanguenat Amy M; Budowle Bruce; Dror Itiel E
      Science & justice : journal of the Forensic Science Society (2017), 57 (6), 415-420 ISSN:1355-0306.
      Cognitive bias may influence process flows and decision making steps in forensic DNA analyses and interpretation. Currently, seven sources of bias have been identified that may affect forensic decision making with roots in human nature; environment, culture, and experience; and case specific information. Most of the literature and research on cognitive bias in forensic science has focused on patterned evidence; however, forensic DNA testing is not immune to bias, especially when subjective interpretation is involved. DNA testing can be strengthened by recognizing the existence of bias, evaluating where it influences decision making, and, when applicable, implementing practices to reduce or control its effects. Elements that may improve forensic decision making regarding bias include cognitively informed education and training, quality assurance procedures, review processes, analysis and interpretation, and context management of irrelevant information. Although bias exists, reliable results often can be (and have been) produced. However, at times bias can (and has) impacted the interpretation of DNA results negatively. Therefore, being aware of the dangers of bias and implementing measures to control its potential impact should be considered. Measures and procedures that handicap the workings of the crime laboratory or add little value to improving the operation are not advocated, but simple yet effective measures are suggested. This article is meant to raise awareness of cognitive bias contamination in forensic DNA testing and to give laboratories possible pathways to make sound decisions to address its influences.
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    32. 32
      Dror, I. E. Science 2018, 360 (6386), 243,  DOI: 10.1126/science.aat8443
      32
      Biases in forensic experts
      Dror, Itiel E.
      Science (Washington, DC, United States) (2018), 360 (6386), 243CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      There is no expanded citation for this reference.
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    33. 33
      Starr, D. Forensics gone wrong: When DNA snares the innocent. Science 2016, 7, na– na,  DOI: 10.1126/science.aaf4160
      There is no corresponding record for this reference.
    34. 34
      Hanna, J.; Valencia, N. DNA Analysis Clears Georgia Man Who Served 17 Years in Wrongful Rape Conviction. CNN, January 10, 2020.
      There is no corresponding record for this reference.
    35. 35
      Dror, I. E.; Hampikian, G. Sci. Justice 2011, 51 (4), 204– 208,  DOI: 10.1016/j.scijus.2011.08.004
      35
      Subjectivity and bias in forensic DNA mixture interpretation
      Dror, Itiel E.; Hampikian, Greg
      Science & Justice (2011), 51 (4), 204-208CODEN: SJUSFE; ISSN:1355-0306. (Elsevier Ireland Ltd.)
      The objectivity of forensic science decision making has received increased attention and scrutiny. However, there are only a few published studies exptl. addressing the potential for contextual bias. Because of the esteem of DNA evidence, it is important to study and assess the impact of subjectivity and bias on DNA mixt. interpretation. The study reported here presents empirical data suggesting that DNA mixt. interpretation is subjective. When 17 North American expert DNA examiners were asked for their interpretation of data from an adjudicated criminal case in that jurisdiction, they produced inconsistent interpretations. Furthermore, the majority of 'context free' experts disagreed with the lab.'s pre-trial conclusions, suggesting that the extraneous context of the criminal case may have influenced the interpretation of the DNA evidence, thereby showing a biasing effect of contextual information in DNA mixt. interpretation.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsFOnsLvF&md5=da201449ee63fc832724b2a54ad5b35d
    36. 36
      A Review of the FBI’s Handling of the Brandon Mayfield Case; Office of the Inspector General, Oversight & Review Division, U.S. Department of Justice, 2006.
      There is no corresponding record for this reference.
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      Marqués-Mateu, Á.; Moreno-Ramón, H.; Balasch, S.; Ibáñez-Asensio, S. Catena 2018, 171, 44– 53,  DOI: 10.1016/j.catena.2018.06.027
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      Berlin, L. AJR, Am. J. Roentgenol. 2007, 189, 517– 522,  DOI: 10.2214/AJR.07.2209
      38
      Radiologic errors and malpractice: a blurry distinction
      Berlin Leonard
      AJR. American journal of roentgenology (2007), 189 (3), 517-22 ISSN:.
      There is no expanded citation for this reference.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2svpsFOrsA%253D%253D&md5=88dc6af078f7c8279f3fcd9d84035ca6
    39. 39
      Kriegeskorte, N.; Simmons, W K.; Bellgowan, P. S F; Baker, C. I Nat. Neurosci. 2009, 12, 535– 540,  DOI: 10.1038/nn.2303
      39
      Circular analysis in systems neuroscience: the dangers of double dipping
      Kriegeskorte, Nikolaus; Simmons, W. Kyle; Bellgowan, Patrick S. F.; Baker, Chris I.
      Nature Neuroscience (2009), 12 (5), 535-540CODEN: NANEFN; ISSN:1097-6256. (Nature Publishing Group)
      A neuroscientific expt. typically generates a large amt. of data, of which only a small fraction is analyzed in detail and presented in a publication. However, selection among noisy measurements can render circular an otherwise appropriate anal. and invalidate results. Here we argue that systems neuroscience needs to adjust some widespread practices to avoid the circularity that can arise from selection. In particular, 'double dipping', the use of the same dataset for selection and selective anal., will give distorted descriptive statistics and invalid statistical inference whenever the results statistics are not inherently independent of the selection criteria under the null hypothesis. To demonstrate the problem, we apply widely used analyses to noise data known to not contain the exptl. effects in question. Spurious effects can appear in the context of both univariate activation anal. and multivariate pattern-information anal. We suggest a policy for avoiding circularity.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXltValt7g%253D&md5=0a8d620535567cbb52b0d24cc260be33
    40. 40
      Vul, E.; Kanwisher, N. In Foundational Issues for Human Brain Mapping; Hanson, S., Bunzl, M., Eds.; MIT Press: Cambridge, MA, 2010; pp 71– 92.
      There is no corresponding record for this reference.
    41. 41
      Richter, D.; Ekman, M.; de Lange, F. P. J. Neurosci. 2018, 38, 7452– 7461,  DOI: 10.1523/JNEUROSCI.3421-17.2018
      41
      Suppressed sensory response to predictable object stimuli throughout the ventral visual stream
      Richter, David; Ekman, Matthias; de Lange, Floris P.
      Journal of Neuroscience (2018), 38 (34), 7452-7461CODEN: JNRSDS; ISSN:1529-2401. (Society for Neuroscience)
      Prediction plays a crucial role in perception, as prominently suggested by predictive coding theories. However, the exact form and mechanism of predictive modulations of sensory processing remain unclear, with some studies reporting a downregulation of the sensory response for predictable input whereas others obsd. an enhanced response. In a similar vein, downregulation of the sensory response for predictable input has been linked to either sharpening or dampening of the sensory representation, which are opposite in nature. In the present study, we set out to investigate the neural consequences of perceptual expectation of object stimuli throughout the visual hierarchy, using fMRI in human volunteers. Participants of both sexes were exposed to pairs of sequentially presented object images in a statistical learning paradigm, in which the first object predicted the identity of the second object. Image transitions were not task relevant; thus, all learning of statistical regularities was incidental. We found strong suppression of neural responses to expected compared with unexpected stimuli throughout the ventral visual stream, including primary visual cortex, lateral occipital complex, and anterior ventral visual areas. Expectation suppression in lateral occipital complex scaled pos. with image preference and voxel selectivity, lending support to the dampening account of expectation suppression in object perception.
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    42. 42
      Luck, S. J.; Ford, M. A. Proc. Natl. Acad. Sci. U. S. A. 1998, 95 (3), 825– 830,  DOI: 10.1073/pnas.95.3.825
      42
      On the role of selective attention in visual perception
      Luck, Steven J.; Ford, Michelle A.
      Proceedings of the National Academy of Sciences of the United States of America (1998), 95 (3), 825-830CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      What is the role of selective attention in visual perception. Before answering this question, it is necessary to differentiate between attentional mechanisms that influence the identification of a stimulus from those that operate after perception is complete. Cognitive neuroscience techniques are particularly well suited to making this distinction because they allow different attentional mechanisms to be isolated in terms of timing and/or neuroanatomy. The present article describes the use of these techniques in differentiating between perceptual and postperceptual attentional mechanisms and then proposes a specific role of attention in visual perception. Specifically, attention is proposed to resolve ambiguities in neural coding that arise when multiple objects are processed simultaneously. Evidence for this hypothesis is provided by two expts. showing that attention-as measured electrophysiol.-is allocated to visual search targets only under conditions that would be expected to lead to ambiguous neural coding.
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    43. 43
      Simons, D. J.; Chabris, C. F. Percept. 1999, 28, 1059– 1074,  DOI: 10.1068/p281059
      43
      Gorillas in our midst: sustained inattentional blindness for dynamic events
      Simons D J; Chabris C F
      Perception (1999), 28 (9), 1059-74 ISSN:0301-0066.
      With each eye fixation, we experience a richly detailed visual world. Yet recent work on visual integration and change direction reveals that we are surprisingly unaware of the details of our environment from one view to the next: we often do not detect large changes to objects and scenes ('change blindness'). Furthermore, without attention, we may not even perceive objects ('inattentional blindness'). Taken together, these findings suggest that we perceive and remember only those objects and details that receive focused attention. In this paper, we briefly review and discuss evidence for these cognitive forms of 'blindness'. We then present a new study that builds on classic studies of divided visual attention to examine inattentional blindness for complex objects and events in dynamic scenes. Our results suggest that the likelihood of noticing an unexpected object depends on the similarity of that object to other objects in the display and on how difficult the priming monitoring task is. Interestingly, spatial proximity of the critical unattended object to attended locations does not appear to affect detection, suggesting that observers attend to objects and events, not spatial positions. We discuss the implications of these results for visual representations and awareness of our visual environment.
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    44. 44
      Stein, T.; Peelen, M. V. J. Exp. Psychol. Gen. 2015, 144, 1089– 1104,  DOI: 10.1037/xge0000109
      44
      Content-specific expectations enhance stimulus detectability by increasing perceptual sensitivity
      Stein Timo; Peelen Marius V
      Journal of experimental psychology. General (2015), 144 (6), 1089-104 ISSN:.
      The detectability of an object in our visual environment is primarily determined by the object's low-level visual salience, resulting from the physical characteristics of the object and its surroundings. In the present study we demonstrate that object detectability is additionally influenced by internally generated expectations about object properties, and that these influences are mediated by changes in perceptual sensitivity. Using continuous flash suppression (CFS) to render objects invisible, we found that providing valid information about the category membership of the object (e.g., "car") before stimulus presentation facilitated awareness of the object, as shown by improved localization performance relative to a noninformative baseline condition and to a condition with invalid prior information. Experiments 2 and 3 showed that the effect of expectation on detection generalized to binocular viewing conditions, with valid category cues facilitating the localization and detection of briefly presented objects. Experiment 4 extended these results to simple stimuli (oriented Gabor patches), for which valid orientation information improved localization performance. Finally, in Experiment 5 we found that the effect of expectation on detection and localization performance partly reflects increased perceptual sensitivity, as evidenced by decreased contrast detection thresholds for validly cued stimuli relative to noncued and invalidly cued stimuli. Together, these findings demonstrate that prior information about specific object properties dynamically enhances the effective signal of visual input matching the expected content, thereby biasing object detection in favor of expected objects.
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    45. 45
      Kok, P.; Brouwer, G. J.; van Gerven, M. A. J.; de Lange, F. P. J. Neurosci. 2013, 33, 16275– 16284,  DOI: 10.1523/JNEUROSCI.0742-13.2013
      45
      Prior expectations bias sensory representations in visual cortex
      Kok, Peter; Brouwer, Gijs Joost; van Gerven, Marcel A. J.; de Lange, Floris P.
      Journal of Neuroscience (2013), 33 (41), 16275-16284CODEN: JNRSDS; ISSN:0270-6474. (Society for Neuroscience)
      Perception is strongly influenced by expectations. Accordingly, perception has sometimes been cast as a process of inference, whereby sensory inputs are combined with prior knowledge. However, despite a wealth of behavioral literature supporting an account of perception as probabilistic inference, the neural mechanisms underlying this process remain largely unknown. One important question is whether top-down expectation biases stimulus representations in early sensory cortex, i.e., whether the integration of prior knowledge and bottom-up inputs is already observable at the earliest levels of sensory processing. Alternatively, early sensory processing may be unaffected by top-down expectations, and integration of prior knowledge and bottom-up input may take place in downstream assocn. areas that are proposed to be involved in perceptual decision-making. Here, we implicitly manipulated human subjects' prior expectations about visual motion stimuli, and probed the effects on both perception and sensory representations in visual cortex. To this end, we measured neural activity noninvasively using functional magnetic resonance imaging, and applied a forward modeling approach to reconstruct the motion direction of the perceived stimuli from the signal in visual cortex. Our results show that top-down expectations bias representations in visual cortex, demonstrating that the integration of prior information and sensory input is reflected at the earliest stages of sensory processing.
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    46. 46
      de Lange, F. P.; Heilbron, M.; Kok, P. Trends Cognit. Sci. 2018, 22, 764– 779,  DOI: 10.1016/j.tics.2018.06.002
      46
      How Do Expectations Shape Perception?
      de Lange Floris P; Heilbron Micha; Kok Peter
      Trends in cognitive sciences (2018), 22 (9), 764-779 ISSN:.
      Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.
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    47. 47
      Gardner, B. O.; Kelley, S.; Murrie, D. C.; Blaisdell, K. N. Forensic Sci. Int. 2019, 297, 236– 242,  DOI: 10.1016/j.forsciint.2019.01.048
      47
      Do evidence submission forms expose latent print examiners to task-irrelevant information?
      Gardner Brett O; Kelley Sharon; Murrie Daniel C; Blaisdell Kellyn N
      Forensic science international (2019), 297 (), 236-242 ISSN:.
      Emerging research documents the ways in which task-irrelevant contextual information may influence the opinions and decisions that forensic analysts reach regarding evidence (e.g., Dror and Cole, 2010; National Academy of Sciences, 2009; President's Council of Advisors on Science and Technology, 2016). Consequently, authorities have called for forensic analysts to rely solely on task-relevant information-and to actively avoid task-irrelevant information-when conducting analyses (National Commission on Forensic Science, 2015). In this study, we examined 97 evidence submission forms, used by 148 accredited crime laboratories across the United States, to determine what types of information laboratories solicit when performing latent print analyses. Results indicate that many laboratories request information with no direct relevance to the specific task of latent print comparison. More concerning, approximately one in six forms (16.5%) request information that appears to have a high potential for bias without any discernible relevance to latent print comparison. Solicitations for task-irrelevant information may carry meaningful consequences and current findings inform strategies to reduce the potential for cognitive bias.
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    48. 48
      Eeden, C. A. J.; de Poot, C. J.; van Koppen, P. J. J. Forensic Sci. 2019, 64 (1), 120– 126,  DOI: 10.1111/1556-4029.13817
      There is no corresponding record for this reference.
    49. 49
      Morewedge, C. K.; Kahneman, D. Trends Cognit. Sci. 2010, 14, 435– 440,  DOI: 10.1016/j.tics.2010.07.004
      49
      Associative processes in intuitive judgment
      Morewedge Carey K; Kahneman Daniel
      Trends in cognitive sciences (2010), 14 (10), 435-40 ISSN:.
      Dual-system models of reasoning attribute errors of judgment to two failures: the automatic operations of a 'System 1' generate a faulty intuition, which the controlled operations of a 'System 2' fail to detect and correct. We identify System 1 with the automatic operations of associative memory and draw on research in the priming paradigm to describe how it operates. We explain how three features of associative memory--associative coherence, attribute substitution and processing fluency--give rise to major biases of intuitive judgment. Our article highlights both the ability of System 1 to create complex and skilled judgments and the role of the system as a source of judgment errors.
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    50. 50
      Moorcroft, M.; Davis, J.; Compton, R. G. Talanta 2001, 54, 785– 803,  DOI: 10.1016/S0039-9140(01)00323-X
      50
      Detection and determination of nitrate and nitrite: a review
      Moorcroft, M. J.; Davis, J.; Compton, R. G.
      Talanta (2001), 54 (5), 785-803CODEN: TLNTA2; ISSN:0039-9140. (Elsevier Science B.V.)
      A review, with 179 refs., of the strategies employed to facilitate the detection, detn. and monitoring of nitrate and/or nitrite is presented. A concise survey of the literature covering 180 reports submitted over the past decade was compiled and the relevant anal. parameters (methodol., matrix, detection limits, range, etc.) tabulated. The various advantages/disadvantages and limitations of the various techniques were exposed such that the applicability of a technique developed for one type of matrix can be meaningfully assessed before attempting to transfer the technol. to another.
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    51. 51
      Almog, J.; Zitrin, S. In Aspects of Explosive Detection; Marshal, M., Oxley, M., Eds.; Elsevier: Amsterdam, 2009; pp 47– 48.
      There is no corresponding record for this reference.
    52. 52
      Lissaman, C. Birmingham Pub Bombers Will Probably Never Be Found. BBC News, March 14, 2011.
      There is no corresponding record for this reference.
    53. 53
      Lang, S. E. Report of the Motherisk Hair Analysis Independent Review; Ontario Ministry of the Attorney General, Canada, 2015.
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    54. 54
      Egglin, T. K.; Feinstein, A. R. J. Am. Med. Assoc. 1996, 276 (21), 1752– 1755,  DOI: 10.1001/jama.1996.03540210060035
      54
      Context bias. A problem in diagnostic radiology
      Egglin T K; Feinstein A R
      JAMA (1996), 276 (21), 1752-5 ISSN:0098-7484.
      OBJECTIVE: To determine whether radiologists' interpretations of images are biased by their context and by prevalence of disease in other recently observed cases. METHODS: A test set of 24 right pulmonary arteriograms with a 33% prevalence of pulmonary emboli (PE) was assembled and embedded in 2 larger groups of films. Group A contained 16 additional arteriograms, all showing PE involving the right lung, so that total prevalence was 60%. Group B contained 16 additional arteriograms without PE so that total prevalence was 20%. Six radiologists were randomly assigned to see either group first and then "cross over" to review the other group after a hiatus of at least 8 weeks. The direction of changes in a 5-point rating scale for the 2 readings of each film in the test set was compared with the sign test; mean sensitivity, specificity, and areas under receiver operating characteristic (ROC) curves were compared with the paired t test. RESULTS: In the context of group A's higher disease prevalence, radiologists shifted more of their diagnoses toward higher suspicion than expected by chance (P=.03, sign test). In group A, mean sensitivity for diagnosing PE was significantly higher (75% vs 60%; P=.04), and area under the ROC curve was significantly larger (0.88 vs 0.82; P=.02). CONCLUSIONS: Radiologists' diagnoses are significantly influenced by the context of interpretation, even when spectrum and verification bias are avoided. This "context bias" effect is unique to the evaluation of subjectively interpreted tests, and illustrates the difficulty of obtaining unbiased estimates of diagnostic accuracy for both new and existing technologies.
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    55. 55
      Wolfe, J. M.; Horowitz, T. S.; Kenner, N. M. Nature 2005, 435, 439– 440,  DOI: 10.1038/435439a
      55
      Cognitive psychology: Rare items often missed in visual searches
      Wolfe, Jeremy M.; Horowitz, Todd S.; Kenner, Naomi M.
      Nature (London, United Kingdom) (2005), 435 (7041), 439-440CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      There is no expanded citation for this reference.
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    56. 56
      Wolfe, J. M.; Horowitz, T. S.; Van Wert, M. J.; Kenner, N. M.; Place, S. S.; Kibbi, N. J. Exp. Psychol. Gen. 2007, 136, 623– 638,  DOI: 10.1037/0096-3445.136.4.623
      56
      Low target prevalence is a stubborn source of errors in visual search tasks
      Wolfe Jeremy M; Horowitz Todd S; Van Wert Michael J; Kenner Naomi M; Place Skyler S; Kibbi Nour
      Journal of experimental psychology. General (2007), 136 (4), 623-38 ISSN:0096-3445.
      In visual search tasks, observers look for targets in displays containing distractors. Likelihood that targets will be missed varies with target prevalence, the frequency with which targets are presented across trials. Miss error rates are much higher at low target prevalence (1%-2%) than at high prevalence (50%). Unfortunately, low prevalence is characteristic of important search tasks such as airport security and medical screening where miss errors are dangerous. A series of experiments show this prevalence effect is very robust. In signal detection terms, the prevalence effect can be explained as a criterion shift and not a change in sensitivity. Several efforts to induce observers to adopt a better criterion fail. However, a regime of brief retraining periods with high prevalence and full feedback allows observers to hold a good criterion during periods of low prevalence with no feedback.
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    57. 57
      Shafffi, E. B.; Smith, E. E.; Osherson, D. N. Mem Cog. 1990, 18 (3), 229– 239,  DOI: 10.3758/BF03213877
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      Simon, D.; Ahn, M.; Stenstrom, D. M.; Read, S. J. Psych. Public Policy. Law 2020, n, na,  DOI: 10.1037/law0000226
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      Murrie, D. C.; Boccaccini, M. T.; Guarnera, L. A.; Rufino, K. A. Psych Sci. 2013, 24, 1889– 1897,  DOI: 10.1177/0956797613481812
      59
      Are forensic experts biased by the side that retained them?
      Murrie Daniel C; Boccaccini Marcus T; Guarnera Lucy A; Rufino Katrina A
      Psychological science (2013), 24 (10), 1889-97 ISSN:.
      How objective are forensic experts when they are retained by one of the opposing sides in an adversarial legal proceeding? Despite long-standing concerns from within the legal system, little is known about whether experts can provide opinions unbiased by the side that retained them. In this experiment, we paid 108 forensic psychologists and psychiatrists to review the same offender case files, but deceived some to believe that they were consulting for the defense and some to believe that they were consulting for the prosecution. Participants scored each offender on two commonly used, well-researched risk-assessment instruments. Those who believed they were working for the prosecution tended to assign higher risk scores to offenders, whereas those who believed they were working for the defense tended to assign lower risk scores to the same offenders; the effect sizes (d) ranged up to 0.85. The results provide strong evidence of an allegiance effect among some forensic experts in adversarial legal proceedings.
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    60. 60
      Strengthening Forensic Science in the United States: A Path Forward; National Academies Press: Washington, DC, 2009.
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    61. 61
      Whitman, G.; Koppl, R. Law Prob. Risk 2010, 9, 69– 90,  DOI: 10.1093/lpr/mgp028
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      Howard, J. Cognitive Errors and Diagnostic Mistakes: A Case-Based Guide to Critical Thinking in Medicine; Springer: New York, 2019.
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      Cosby, K. S.; Croskerry, P. Acad. Emergency Med. 2004, 11, 1341– 1345,  DOI: 10.1197/j.aem.2004.07.005
      63
      Profiles in patient safety: authority gradients in medical error
      Cosby Karen S; Croskerry Pat
      Academic emergency medicine : official journal of the Society for Academic Emergency Medicine (2004), 11 (12), 1341-5 ISSN:1069-6563.
      The term "authority gradient" was first defined in aviation when it was noted that pilots and copilots may not communicate effectively in stressful situations if there is a significant difference in their experience, perceived expertise, or authority. A number of unintentional aviation, aerospace, and industrial incidents have been attributed, in part, to authority gradients. The concept of authority gradient was introduced to medicine in the Institute of Medicine report To Err Is Human, yet little has been written or acknowledged in the medical literature regarding its role in medical error. The practice of medicine and medical training programs are highly organized, hierarchical structures that depend on supervision by authority figures. The concept that authority gradients might contribute to medical error is largely unrecognized. This article presents one case and a series of examples to detail how authority gradients can contribute to medical error, and describes methods used in other disciplines to avoid their potentially negative impact.
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    64. 64
      Saposnik, G.; Redelmeier, D.; Ruff, C. C.; Tobler BMC Med. Inf. Decis. Making 2016, 16, 138,  DOI: 10.1186/s12911-016-0377-1
      64
      Cognitive biases associated with medical decisions: a systematic review
      Saposnik Gustavo; Ruff Christian C; Tobler Philippe N; Saposnik Gustavo; Saposnik Gustavo; Redelmeier Donald; Saposnik Gustavo
      BMC medical informatics and decision making (2016), 16 (1), 138 ISSN:.
      BACKGROUND: Cognitive biases and personality traits (aversion to risk or ambiguity) may lead to diagnostic inaccuracies and medical errors resulting in mismanagement or inadequate utilization of resources. We conducted a systematic review with four objectives: 1) to identify the most common cognitive biases, 2) to evaluate the influence of cognitive biases on diagnostic accuracy or management errors, 3) to determine their impact on patient outcomes, and 4) to identify literature gaps. METHODS: We searched MEDLINE and the Cochrane Library databases for relevant articles on cognitive biases from 1980 to May 2015. We included studies conducted in physicians that evaluated at least one cognitive factor using case-vignettes or real scenarios and reported an associated outcome written in English. Data quality was assessed by the Newcastle-Ottawa scale. Among 114 publications, 20 studies comprising 6810 physicians met the inclusion criteria. Nineteen cognitive biases were identified. RESULTS: All studies found at least one cognitive bias or personality trait to affect physicians. Overconfidence, lower tolerance to risk, the anchoring effect, and information and availability biases were associated with diagnostic inaccuracies in 36.5 to 77 % of case-scenarios. Five out of seven (71.4 %) studies showed an association between cognitive biases and therapeutic or management errors. Of two (10 %) studies evaluating the impact of cognitive biases or personality traits on patient outcomes, only one showed that higher tolerance to ambiguity was associated with increased medical complications (9.7 % vs 6.5 %; p = .004). Most studies (60 %) targeted cognitive biases in diagnostic tasks, fewer focused on treatment or management (35 %) and on prognosis (10 %). Literature gaps include potentially relevant biases (e.g. aggregate bias, feedback sanction, hindsight bias) not investigated in the included studies. Moreover, only five (25 %) studies used clinical guidelines as the framework to determine diagnostic or treatment errors. Most studies (n = 12, 60 %) were classified as low quality. CONCLUSIONS: Overconfidence, the anchoring effect, information and availability bias, and tolerance to risk may be associated with diagnostic inaccuracies or suboptimal management. More comprehensive studies are needed to determine the prevalence of cognitive biases and personality traits and their potential impact on physicians' decisions, medical errors, and patient outcomes.
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    65. 65
      Dror, I. E.; Mnookin, J. Law Prob. Risk 2010, 9 (1), 47– 67,  DOI: 10.1093/lpr/mgp031
      There is no corresponding record for this reference.
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      General Requirements for the Competence of Testing and Calibration Laboratories, 3rd ed.; ISO/IEC 17025; International Organization for Standardization/International Electrotechnical Commission, Geneva, Switzerland, 2017.
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      Dror, I. E.; Pierce, M. L. J. Forensic Sci. 2020, 65 (3), 800– 808,  DOI: 10.1111/1556-4029.14265
      67
      ISO Standards Addressing Issues of Bias and Impartiality in Forensic Work
      Dror Itiel E; Pierce Michal L
      Journal of forensic sciences (2020), 65 (3), 800-808 ISSN:.
      The ISO/IEC 17020 and 17025 standards both include requirements for impartiality and the freedom from bias. Meeting these requirements for implicit cognitive bias is not a simple matter. In this article, we address these international standards, specifically focusing on evaluating and mitigating the risk to impartiality, and quality assurance checks, so as to meet accreditation program requirements. We cover their meaning to management as well as to practitioners, addressing how these issues of impartiality and bias relate to forensic work, and how one can effectively evaluate and mitigate those risks. We then elaborate on specific quality assurance policies and checks and identify when corrective action may be appropriate. These measures will not only serve to meet ISO/IEC 17020 and 17025 requirements, but also enhance forensic work and decision-making.
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    68. 68
      Dror, I. E.; Langenburg, G. J. Forensic Sci. 2019, 64 (1), 10– 15,  DOI: 10.1111/1556-4029.13854
      68
      "Cannot Decide": The Fine Line Between Appropriate Inconclusive Determinations Versus Unjustifiably Deciding Not To Decide
      Dror Itiel E; Langenburg Glenn
      Journal of forensic sciences (2019), 64 (1), 10-15 ISSN:.
      Inconclusive decisions, deciding not to decide, are decisions. We present a cognitive model which takes into account that decisions are an outcome of interactions and intersections between the actual data and human cognition. Using this model it is suggested under which circumstances inconclusive decisions are justified and even warranted (reflecting proper caution and meta-cognitive abilities in recognizing limited abilities), and, conversely, under what circumstances inconclusive decisions are unjustifiable and should not be permitted. The model further explores the limitations and problems in using categorical decision-making when the data are actually a continuum. Solutions are suggested within the forensic fingerprinting domain, but they can be applied to other forensic domains, and, with modifications, may also be applied to other expert domains.
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    69. 69
      Gok, K.; Atsan, N. Intern J. Business Soc. Res. 2016, 6 (3), 38– 47,  DOI: 10.18533/ijbsr.v6i3.936
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      Neal, T. PLoS One 2016, 11 (4), e0154434  DOI: 10.1371/journal.pone.0154434
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      Miller, A. K.; Rufino, K. A.; Boccaccini, M. T.; Jackson, R. L.; Murrie, D. C. Assessment 2011, 18 (2), 253– 260,  DOI: 10.1177/1073191111402460
      71
      On individual differences in person perception: raters' personality traits relate to their psychopathy checklist-revised scoring tendencies
      Miller Audrey K; Rufino Katrina A; Boccaccini Marcus T; Jackson Rebecca L; Murrie Daniel C
      Assessment (2011), 18 (2), 253-60 ISSN:.
      This study investigated raters' personality traits in relation to scores they assigned to offenders using the Psychopathy Checklist-Revised (PCL-R). A total of 22 participants, including graduate students and faculty members in clinical psychology programs, completed a PCL-R training session, independently scored four criminal offenders using the PCL-R, and completed a comprehensive measure of their own personality traits. A priori hypotheses specified that raters' personality traits, and their similarity to psychopathy characteristics, would relate to raters' PCL-R scoring tendencies. As hypothesized, some raters assigned consistently higher scores on the PCL-R than others, especially on PCL-R Facets 1 and 2. Also as hypothesized, raters' scoring tendencies related to their own personality traits (e.g., higher rater Agreeableness was associated with lower PCL-R Interpersonal facet scoring). Overall, findings underscore the need for future research to examine the role of evaluator characteristics on evaluation results and the need for clinical training to address evaluators' personality influences on their ostensibly objective evaluations.
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      Griffin, D.; Ross, L. In Advances in Experimental Social Psychology; Zanna, M. P., Ed.; Academic Press: San Diego, CA, 1991; pp 319– 359.
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      Ross, L.; Greene, D.; House, P. J. Exp. Soc. Psych. 1977, 13, 279– 301,  DOI: 10.1016/0022-1031(77)90049-X
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      Oppenheimer, D. M. Trends Cognit. Sci. 2008, 12, 237– 241,  DOI: 10.1016/j.tics.2008.02.014
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      The secret life of fluency
      Oppenheimer Daniel M
      Trends in cognitive sciences (2008), 12 (6), 237-41 ISSN:1364-6613.
      Fluency - the subjective experience of ease or difficulty associated with completing a mental task - has been shown to be an influential cue in a wide array of judgments. Recently researchers have begun to look at how fluency impacts judgment through more subtle and indirect routes. Fluency impacts whether information is represented in working memory and what aspects of that information are attended to. Additionally, fluency has an impact in strategy selection; depending on how fluent information is, people engage in qualitatively different cognitive operations. This suggests that the role of fluency is more nuanced than previously believed and that understanding fluency could be of critical importance to understanding cognition more generally.
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      Goldstein, D. G.; Gigerenzer, G. Psychol. Rev. 2002, 109, 75– 90,  DOI: 10.1037/0033-295X.109.1.75
      75
      Models of ecological rationality: the recognition heuristic
      Goldstein Daniel G; Gigerenzer Gerd
      Psychological review (2002), 109 (1), 75-90 ISSN:0033-295X.
      One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counterintuitive less-is-more effect in which less knowledge is better than more for making accurate inferences.
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    76. 76
      Robertson, C., Kesselheim, A., Eds.; Blinding as a Solution to Bias: Strengthening Biomedical Science, Forensic Science, and Law; Academic Press: New York, 2016; pp 1– 388.
      There is no corresponding record for this reference.
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      Maude, J. Diagnosis 2014, 1, 107– 109,  DOI: 10.1515/dx-2013-0009
      77
      Differential diagnosis: the key to reducing diagnosis error, measuring diagnosis and a mechanism to reduce healthcare costs
      Maude Jason
      Diagnosis (Berlin, Germany) (2014), 1 (1), 107-109 ISSN:.
      Differential diagnosis has been taught in medical schools for over 100 years and yet it is not routinely carried out in practice; nor is it required to be documented within medical notes. I strongly believe that the routine use of a differential diagnosis would not only substantially reduce the level of diagnostic error but would also greatly reduce the cost of healthcare. This solution to the seemingly intractable problems of diagnostic error and rising healthcare costs is simple and has been with us for 100 years!
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      Barondess, J. A., Carpenter, C. C., Eds.; Differential Diagnosis; Lea & Febiger: Philadelphia, PA, 1994; pp 1– 800.
      There is no corresponding record for this reference.