De-Biasing Legal Factfinders

2021 ◽  
pp. 395-410
Author(s):  
Frank Zenker
Keyword(s):  
Decision Making ◽  
Special Reference ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Future Research ◽  
Research Needs ◽  
Base Rate Neglect ◽  
Human Decision ◽  
Social Biases ◽  
Evidential Basis

This chapter examines the psychological studies of biases and de-biasing measures in human decision-making with special reference to adjudicative factfinding. Research shows that factfinders are prone to cognitive biases (such as anchoring, framing, base-rate neglect, and confirmation bias) as well as social biases. Driven by this research, multiple studies have examined the extent to which those biases can be mitigated by de-biasing measures like “consider the opposite” and “give reasons.” After a brief overview of the research, the author points to the problematic evidential basis and identifies future research needs, and concludes that empirical research on de-biasing measures has so far delivered less than one would hope for.

Overcoming Confirmation and Blind Spot Biases When Communicating Science

2017 ◽  
Author(s):  
Kate Kenski
Keyword(s):  
Decision Making ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Blind Spot ◽  
Future Research ◽  
Costs And Benefits ◽  
Process Information ◽  
Bias Blind Spot

This chapter focuses on two biases that lead people away from evaluating evidence and scientific studies impartially—confirmation bias and bias blind spot. The chapter first discusses different ways in which people process information and reviews the costs and benefits of utilizing cognitive shortcuts in decision making. Next, two common cognitive biases, confirmation bias and bias blind spot, are explained. Then the literature on “debiasing” is explored. Finally, the implications of confirmation bias and bias blind spot in the context of communicating about science are examined, and an agenda for future research on understanding and mitigating these biases is offered.

scholarly journals Confirmation Bias Estimation from Electroencephalography with Machine Learning

2019 ◽  
Vol 63 (1) ◽  
pp. 73-77
Author(s):  
Micah N. Villarreal ◽  
Alexander J. Kamrud ◽  
Brett J. Borghetti
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Supervised Machine Learning ◽  
Bias Estimation ◽  
Human Decision ◽  
Behavioral Evidence ◽  
Post Hoc ◽  
The Relationship

Cognitive biases are known to affect human decision making and can have disastrous effects in the fast-paced environments of military operators. Traditionally, post-hoc behavioral analysis is used to measure the level of bias in a decision. However, these techniques can be hindered by subjective factors and cannot be collected in real-time. This pilot study collects behavior patterns and physiological signals present during biased and unbiased decision-making. Supervised machine learning models are trained to find the relationship between Electroencephalography (EEG) signals and behavioral evidence of cognitive bias. Once trained, the models should infer the presence of confirmation bias during decision-making using only EEG - without the interruptions or the subjective nature of traditional confirmation bias estimation techniques.

scholarly journals How Human Decision-making Biases Influence Health Outcomes in Patient Care

2021 ◽  
Vol 29 (2) ◽  
pp. 64-72
Author(s):  
Rainer Sibbel ◽  
Angelina Huber
Keyword(s):  
Decision Making ◽  
Healthcare Professionals ◽  
Cognitive Biases ◽  
Meta Analysis ◽  
Delayed Diagnosis ◽  
Medical Decision ◽  
Future Research ◽  
Literature Study ◽  
Human Decision ◽  
The Impact

Purpose: Medical treatments and medical decision making are mostly human based and therefore in risk of being influenced by cognitive biases. The potential impact could lead to bad medical outcome, unnecessary harm or even death. The aim of this comprehensive literature study is to analyse the evidence whether healthcare professionals are biased, which biases are most relevant in medicine and how these biases may be reduced. Approach/Findings: The results of the comprehensive literature based meta-analysis confirm on the one hand that several biases are relevant in the medical decision and treatment process. On the other hand, the study shows that the empirical evidence on the impact of cognitive biases on clinical outcome is scarce for most biases and that further research is necessary in this field. Value/Practical Implications: Nevertheless, it is important to determine the extent to which biases in healthcare professionals translate into negative clinical outcomes such as misdiagnosis, delayed diagnosis, or mistreatment. Only this way, the importance of incorporating debiasing strategies into the clinical setting, and which biases to focus on, can be properly assessed. Research Limitations/Future Research: Though recent literature puts great emphasis on cognitive debiasing strategies, there are still very few approaches that have proven to be efficient. Due to the increasing degree of specialization in medicine, the relevance of the different biases varies. Paper type: Theoretical.

Confirmation bias in decision making for fingerprints, DNA and eyewitness evidence cannot be explained by cognitive style or thinking dispositions.

2019 ◽  
Author(s):  
Daniel Edgcumbe
Keyword(s):  
Decision Making ◽  
Cognitive Style ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Criminal Case ◽  
Court Cases ◽  
Legal Proceedings ◽  
Thinking Dispositions ◽  
Evaluation Of Evidence ◽  
Facial Composite

Pre-existing beliefs about the background or guilt of a suspect can bias the subsequent evaluation of evidence for forensic examiners and lay people alike. This biasing effect, called the confirmation bias, has influenced legal proceedings in prominent court cases such as that of Brandon Mayfield. Today many forensic providers attempt to train their examiners against these cognitive biases. Nine hundred and forty-two participants read a fictional criminal case and received either neutral, incriminating or exonerating evidence (fingerprint, eyewitness, or DNA) before providing an initial rating of guilt. Participants then viewed ambiguous evidence (alibi, facial composite, handwriting sample or informant statement) before providing a final rating of guilt. Final guilt ratings were higher for all evidence conditions (neutral, incriminating or exonerating) following exposure to the ambiguous evidence. This provides evidence that the confirmation bias influences the evaluation of evidence.

scholarly journals Individual goals and social preferences in operational decisions

2019 ◽  
Vol 39 (1) ◽  
pp. 116-137 ◽  
Author(s):  
Nienke Hofstra ◽  
Wout Dullaert ◽  
Sander De Leeuw ◽  
Eirini Spiliotopoulou
Keyword(s):  
Decision Making ◽  
Social Preferences ◽  
Operational Planning ◽  
Future Research ◽  
Monetary Incentives ◽  
Content Type ◽  
Planning Decisions ◽  
Multiple Data ◽  
Human Decision ◽  
Insight Into

Purpose The purpose of this paper is to develop propositions explaining the influence of individual goals and social preferences on human decision making in transport planning. The aim is to understand which individual goals and social preferences planners pursue and how these influence planners’ decisions. Design/methodology/approach Propositions are developed based on investigation of decision making of transport planners in a Dutch logistics service provider using multiple data collection methods. Findings The study shows how decision making of transport planners is motivated by individual goals as well as social preferences for reciprocity and group identity. Research limitations/implications Further research including transaction data analysis is needed to triangulate findings and to strengthen conclusions. Propositions are developed to be tested in future research. Practical implications Results suggest that efforts to guide planners in their decision making should go beyond traditional (monetary) incentives and consider their individual goals and social preferences. Moreover, this study provides insight into why transport planners deviate from desired behaviour. Originality/value While individual decision making plays an essential role in operational planning, the factors influencing how individuals make operational planning decisions are not fully understood.

scholarly journals Human decision-making biases in the moral dilemmas of autonomous vehicles

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Darius-Aurel Frank ◽  
Polymeros Chrysochou ◽  
Panagiotis Mitkidis ◽  
Dan Ariely
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Moral Dilemmas ◽  
Situational Factors ◽  
Future Research ◽  
Personal Perspective ◽  
Moral Code ◽  
Moral Decision Making ◽  
Moral Decisions ◽  
Human Decision

Abstract The development of artificial intelligence has led researchers to study the ethical principles that should guide machine behavior. The challenge in building machine morality based on people’s moral decisions, however, is accounting for the biases in human moral decision-making. In seven studies, this paper investigates how people’s personal perspectives and decision-making modes affect their decisions in the moral dilemmas faced by autonomous vehicles. Moreover, it determines the variations in people’s moral decisions that can be attributed to the situational factors of the dilemmas. The reported studies demonstrate that people’s moral decisions, regardless of the presented dilemma, are biased by their decision-making mode and personal perspective. Under intuitive moral decisions, participants shift more towards a deontological doctrine by sacrificing the passenger instead of the pedestrian. In addition, once the personal perspective is made salient participants preserve the lives of that perspective, i.e. the passenger shifts towards sacrificing the pedestrian, and vice versa. These biases in people’s moral decisions underline the social challenge in the design of a universal moral code for autonomous vehicles. We discuss the implications of our findings and provide directions for future research.

SHARED DECISION MAKING IN ONCOLOGY AND PALLIATIVE CARE

2020 ◽  
Author(s):  
Paul A Glare
Keyword(s):  
Decision Making ◽  
Palliative Care ◽  
Shared Decision Making ◽  
Cognitive Biases ◽  
Treatment Options ◽  
Care Plan ◽  
Treatment Modalities ◽  
Shared Decision ◽  
Human Decision ◽  
Based Medicine

Background: Cancer raises many questions for people afflicted by it. Do I want to have genetic testing? Will I comply with screening recommendations? If I am diagnosed with it, where will I have treatment? What treatment modalities will I have? Will I go on a clinical trial? Am I willing to bankrupt my family in the process of pursuing treatment? Will I write an advance care plan? Will I accept hospice if I have run out of available treatment options? Most of these questions have more than one correct answer, and the evidence for the superiority of one option over another is either not available or does not allow differentiation. Often the best choice between two or more valid approaches depends on how individuals value their respective risks and benefits; “preference-based medicine” may be more important than “evidence-based medicine.” There are various models for eliciting preferences, but applying them can raise a number of challenges. Objectives: To present the concepts, the value, the strategies, the quandaries, and the potential pitfalls of Shared Decision Making in Oncology and Palliative Care. Method: Narrative review. Results: Some challenges to practicing preference-based medicine in oncology and palliative care include: some patients don’t want to participate in shared decision making (SDM); the whole situation needs to be addressed, not just part of it; but are some topics out of bounds? Cognitive biases apply as much in SDM as any other human decision making, affecting the choice; how numerically equivalent data are framed can also affect the outcome; conducting SDM is also important at the end of life. Conclusions: By being aware of the potential pitfalls with SDM, clinicians are more able to facilitate the discussion so that the patients’ choices truly reflect their informed preferences, at a time when stakes and emotions are high.

scholarly journals Measuring individual differences in cognitive biases: The Cognitive Bias Inventory

2021 ◽  
Author(s):  
Vincent Berthet ◽  
Vincent de Gardelle
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Bias ◽  
Cognitive Biases ◽  
Temporal Stability ◽  
Real Life ◽  
Future Research ◽  
Personality Factors ◽  
Wrong Answer ◽  
Outcome Bias

This article described the behavioral measurement of six classic cognitive biases (framing, availability, anchoring, overconfidence, hindsight/outcome bias, confirmation bias). Each measure showed a satisfactory level of reliability with regard both to internal consistency (mean Cronbach’s alpha = .77) and temporal stability (mean test-retest correlation = .71). Multivariate analysis supported the hypothesis that each cognitive bias captures specific decision-making processes as the six biases: (a) were virtually uncorrelated (mean correlation = .08), thus indicating no general decision-making competence factor, (b) were moderately correlated with other relevant constructs (the A-DMC components, cognitive ability, decision-making styles, and personality factors), (c) were more related to performance on a narrow domain of decision-making (the ability to overcome an intuitive wrong answer as measured by the CRT) than to the general success in real-life decision-making as measured by the Decision Outcomes Inventory (DOI). We introduce this set of behavioral tasks as the Cognitive Bias Inventory (CBI), a psychometric tool allowing for the reliable assessment of individual differences in six common, independent cognitive shortcuts. The CBI appears as a useful tool for future research on decision-making competence and how it relates to decision errors.

scholarly journals The Measurement of Individual Differences in Cognitive Biases: A Review and Improvement

2021 ◽  
Vol 12 ◽  
Author(s):  
Vincent Berthet
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Biases ◽  
Empirical Work ◽  
Confirmation Bias ◽  
Generic Measures

Individual differences have been neglected in decision-making research on heuristics and cognitive biases. Addressing that issue requires having reliable measures. The author first reviewed the research on the measurement of individual differences in cognitive biases. While reliable measures of a dozen biases are currently available, our review revealed that some measures require improvement and measures of other key biases are still lacking (e.g., confirmation bias). We then conducted empirical work showing that adjustments produced a significant improvement of some measures and that confirmation bias can be reliably measured. Overall, our review and findings highlight that the measurement of individual differences in cognitive biases is still in its infancy. In particular, we suggest that contextualized (in addition to generic) measures need to be improved or developed.

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