Biased Thinking in the Face of Uncertainty: Exploring the Process of Diagnostic Decision Making Within the Field of Psychiatry
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Addressing Cognitive Biases in Augmented Business Decision Systems Human Performance Metrics for Generic AI-Assisted Decision Making
Addressing Cognitive Biases in Augmented Business Decision Systems Human performance metrics for generic AI-assisted decision making. THOMAS BAUDEL* IBM France Lab, Orsay, France MANON VERBOCKHAVEN IBM France Lab & ENSAE VICTOIRE COUSERGUE IBM France Lab, Université Paris-Dauphine & Mines ParisTech GUILLAUME ROY IBM France Lab & ENSAI RIDA LAARACH IBM France Lab, Telecom ParisTech & HEC How do algorithmic decision aids introduced in business decision processes affect task performance? In a first experiment, we study effective collaboration. Faced with a decision, subjects alone have a success rate of 72%; Aided by a recommender that has a 75% success rate, their success rate reaches 76%. The human-system collaboration had thus a greater success rate than each taken alone. However, we noted a complacency/authority bias that degraded the quality of decisions by 5% when the recommender was wrong. This suggests that any lingering algorithmic bias may be amplified by decision aids. In a second experiment, we evaluated the effectiveness of 5 presentation variants in reducing complacency bias. We found that optional presentation increases subjects’ resistance to wrong recommendations. We conclude by arguing that our metrics, in real usage scenarios, where decision aids are embedded as system-wide features in Business Process Management software, can lead to enhanced benefits. CCS CONCEPTS • Cross-computing tools and techniques: Empirical studies, Information Systems: Enterprise information systems, Decision support systems, Business Process Management, Human-centered computing, Human computer interaction (HCI), Visualization, Machine Learning, Automation Additional Keywords and Phrases: Business decision systems, Decision theory, Cognitive biases * [email protected]. 1 INTRODUCTION For the past 20 years, Business Process Management (BPM) [29] and related technologies such as Business Rules [10, 52] and Robotic Process Automation [36] have streamlined processes and operational decision- making in large enterprises, transforming work organization. -
Pat Croskerry MD Phd
Thinking (and the factors that influence it) Pat Croskerry MD PhD Scottish Intensive Care Society St Andrews, January 2011 RECOGNIZED Intuition Pattern Pattern Initial Recognition Executive Dysrationalia Calibration Output information Processor override T override Repetition NOT Analytical RECOGNIZED reasoning Dual Process Model Medical Decision Making Intuitive Analytical Orthodox Medical Decision Making (Analytical) Rational Medical Decision-Making • Knowledge base • Differential diagnosis • Best evidence • Reviews, meta-analysis • Biostatistics • Publication bias, citation bias • Test selection and interpretation • Bayesian reasoning • Hypothetico-deductive reasoning .„Cognitive thought is the tip of an enormous iceberg. It is the rule of thumb among cognitive scientists that unconscious thought is 95% of all thought – .this 95% below the surface of conscious awareness shapes and structures all conscious thought‟ Lakoff and Johnson, 1999 Rational blind-spots • Framing • Context • Ambient conditions • Individual factors Individual Factors • Knowledge • Intellect • Personality • Critical thinking ability • Decision making style • Gender • Ageing • Circadian type • Affective state • Fatigue, sleep deprivation, sleep debt • Cognitive load tolerance • Susceptibility to group pressures • Deference to authority Intelligence • Measurement of intelligence? • IQ most widely used barometer of intellect and cognitive functioning • IQ is strongest single predictor of job performance and success • IQ tests highly correlated with each other • Population -
A Task-Based Taxonomy of Cognitive Biases for Information Visualization
A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias, -
Experimental Practices in Economics: a Methodological Challenge for Psychologists?
BEHAVIORAL AND BRAIN SCIENCES (2001) 24, 383–451 Printed in the United States of America Experimental practices in economics: A methodological challenge for psychologists? Ralph Hertwig Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, 14195 Berlin, Germany. [email protected] Andreas Ortmann Center for Economic Research and Graduate Education, Charles University, and Economics Institute, Academy of Sciences of the Czech Republic, 111 21 Prague 1, Czech Republic. [email protected] Abstract: This target article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of ex- perimentation in economics, namely, script enactment, repeated trials, performance-based monetary payments, and the proscription against deception, and compare them to experimental practices in psychology, primarily in the area of behavioral decision making. Whereas economists bring a precisely defined “script” to experiments for participants to enact, psychologists often do not provide such a script, leaving participants to infer what choices the situation affords. By often using repeated experimental trials, economists allow participants to learn about the task and the environment; psychologists typically do not. Economists generally pay participants on the ba- sis of clearly defined performance criteria; psychologists usually pay a flat fee or grant a fixed amount of course credit. Economists vir- tually never deceive participants; psychologists, especially in some areas of inquiry, often do. We argue that experimental standards in economics are regulatory in that they allow for little variation between the experimental practices of individual researchers. -
Bias and Fairness in NLP
Bias and Fairness in NLP Margaret Mitchell Kai-Wei Chang Vicente Ordóñez Román Google Brain UCLA University of Virginia Vinodkumar Prabhakaran Google Brain Tutorial Outline ● Part 1: Cognitive Biases / Data Biases / Bias laundering ● Part 2: Bias in NLP and Mitigation Approaches ● Part 3: Building Fair and Robust Representations for Vision and Language ● Part 4: Conclusion and Discussion “Bias Laundering” Cognitive Biases, Data Biases, and ML Vinodkumar Prabhakaran Margaret Mitchell Google Brain Google Brain Andrew Emily Simone Parker Lucy Ben Elena Deb Timnit Gebru Zaldivar Denton Wu Barnes Vasserman Hutchinson Spitzer Raji Adrian Brian Dirk Josh Alex Blake Hee Jung Hartwig Blaise Benton Zhang Hovy Lovejoy Beutel Lemoine Ryu Adam Agüera y Arcas What’s in this tutorial ● Motivation for Fairness research in NLP ● How and why NLP models may be unfair ● Various types of NLP fairness issues and mitigation approaches ● What can/should we do? What’s NOT in this tutorial ● Definitive answers to fairness/ethical questions ● Prescriptive solutions to fix ML/NLP (un)fairness What do you see? What do you see? ● Bananas What do you see? ● Bananas ● Stickers What do you see? ● Bananas ● Stickers ● Dole Bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves ● Bunches of bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas -
“Dysrationalia” Among University Students: the Role of Cognitive
“Dysrationalia” among university students: The role of cognitive abilities, different aspects of rational thought and self-control in explaining epistemically suspect beliefs Erceg, Nikola; Galić, Zvonimir; Bubić, Andreja Source / Izvornik: Europe’s Journal of Psychology, 2019, 15, 159 - 175 Journal article, Published version Rad u časopisu, Objavljena verzija rada (izdavačev PDF) https://doi.org/10.5964/ejop.v15i1.1696 Permanent link / Trajna poveznica: https://urn.nsk.hr/urn:nbn:hr:131:942674 Rights / Prava: Attribution 4.0 International Download date / Datum preuzimanja: 2021-09-29 Repository / Repozitorij: ODRAZ - open repository of the University of Zagreb Faculty of Humanities and Social Sciences Europe's Journal of Psychology ejop.psychopen.eu | 1841-0413 Research Reports “Dysrationalia” Among University Students: The Role of Cognitive Abilities, Different Aspects of Rational Thought and Self-Control in Explaining Epistemically Suspect Beliefs Nikola Erceg* a, Zvonimir Galić a, Andreja Bubić b [a] Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia. [b] Department of Psychology, Faculty of Humanities and Social Sciences, University of Split, Split, Croatia. Abstract The aim of the study was to investigate the role that cognitive abilities, rational thinking abilities, cognitive styles and self-control play in explaining the endorsement of epistemically suspect beliefs among university students. A total of 159 students participated in the study. We found that different aspects of rational thought (i.e. rational thinking abilities and cognitive styles) and self-control, but not intelligence, significantly predicted the endorsement of epistemically suspect beliefs. Based on these findings, it may be suggested that intelligence and rational thinking, although related, represent two fundamentally different constructs. -
The Effects of the Intuitive Prosecutor Mindset on Person Memory
THE EFFECTS OF THE INTUITIVE PROSECUTOR MINDSET ON PERSON MEMORY DISSERTATION Presented in Partial Fulfillment of the Requirements for The Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Richard J. Shakarchi, B.S., M.A. ***** The Ohio State University 2002 Dissertation Committee: Professor Marilynn Brewer, Adviser Approved by Professor Gifford Weary ________________________________ Adviser Professor Neal Johnson Department of Psychology Copyright by Richard J. Shakarchi 2002 ABSTRACT The intuitive prosecutor metaphor of human judgment is a recent development within causal attribution research. The history of causal attribution is briefly reviewed, with an emphasis on explanations of attributional biases. The evolution of such explanations is traced through a purely-cognitive phase to the more modern acceptance of motivational explanations for attributional biases. Two examples are offered of how a motivational explanatory framework of attributional biases can account for broad patterns of information processing biases. The intuitive prosecutor metaphor is presented as a parallel explanatory framework for interpreting attributional biases, whose motivation is based on a threat to social order. The potential implications for person memory are discussed, and three hypotheses are developed: That intuitive prosecutors recall norm- violating information more consistently than non-intuitive prosecutors (H1); that this differential recall may be based on differential (biased) encoding of behavioral information (H2); and that this differential recall may also be based on biased retrieval of information from memory rather than the result of a reporting bias (H3). A first experiment is conducted to test the basic recall hypothesis (H1). A second experiment is conducted that employs accountability in order to test the second and third hypotheses. -
Outcome Reporting Bias in COVID-19 Mrna Vaccine Clinical Trials
medicina Perspective Outcome Reporting Bias in COVID-19 mRNA Vaccine Clinical Trials Ronald B. Brown School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L3G1, Canada; [email protected] Abstract: Relative risk reduction and absolute risk reduction measures in the evaluation of clinical trial data are poorly understood by health professionals and the public. The absence of reported absolute risk reduction in COVID-19 vaccine clinical trials can lead to outcome reporting bias that affects the interpretation of vaccine efficacy. The present article uses clinical epidemiologic tools to critically appraise reports of efficacy in Pfzier/BioNTech and Moderna COVID-19 mRNA vaccine clinical trials. Based on data reported by the manufacturer for Pfzier/BioNTech vaccine BNT162b2, this critical appraisal shows: relative risk reduction, 95.1%; 95% CI, 90.0% to 97.6%; p = 0.016; absolute risk reduction, 0.7%; 95% CI, 0.59% to 0.83%; p < 0.000. For the Moderna vaccine mRNA-1273, the appraisal shows: relative risk reduction, 94.1%; 95% CI, 89.1% to 96.8%; p = 0.004; absolute risk reduction, 1.1%; 95% CI, 0.97% to 1.32%; p < 0.000. Unreported absolute risk reduction measures of 0.7% and 1.1% for the Pfzier/BioNTech and Moderna vaccines, respectively, are very much lower than the reported relative risk reduction measures. Reporting absolute risk reduction measures is essential to prevent outcome reporting bias in evaluation of COVID-19 vaccine efficacy. Keywords: mRNA vaccine; COVID-19 vaccine; vaccine efficacy; relative risk reduction; absolute risk reduction; number needed to vaccinate; outcome reporting bias; clinical epidemiology; critical appraisal; evidence-based medicine Citation: Brown, R.B. -
Working Memory, Cognitive Miserliness and Logic As Predictors of Performance on the Cognitive Reflection Test
Working Memory, Cognitive Miserliness and Logic as Predictors of Performance on the Cognitive Reflection Test Edward J. N. Stupple ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Maggie Gale ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Christopher R. Richmond ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Abstract Most participants respond that the answer is 10 cents; however, a slower and more analytic approach to the The Cognitive Reflection Test (CRT) was devised to measure problem reveals the correct answer to be 5 cents. the inhibition of heuristic responses to favour analytic ones. The CRT has been a spectacular success, attracting more Toplak, West and Stanovich (2011) demonstrated that the than 100 citations in 2012 alone (Scopus). This may be in CRT was a powerful predictor of heuristics and biases task part due to the ease of administration; with only three items performance - proposing it as a metric of the cognitive miserliness central to dual process theories of thinking. This and no requirement for expensive equipment, the practical thesis was examined using reasoning response-times, advantages are considerable. There have, moreover, been normative responses from two reasoning tasks and working numerous correlates of the CRT demonstrated, from a wide memory capacity (WMC) to predict individual differences in range of tasks in the heuristics and biases literature (Toplak performance on the CRT. These data offered limited support et al., 2011) to risk aversion and SAT scores (Frederick, for the view of miserliness as the primary factor in the CRT. -
Maria-Pia Victoria-Feser GSEM, University of Geneva B [email protected] Professor of Statistics
Research Center for Statistics Maria-Pia Victoria-Feser GSEM, University of Geneva B [email protected] Professor of Statistics Executive Summary (September 2019) Employment I started my career as a lecturer at the London School of Economics (LSE) in 1993, the year I completed history my Ph. D. in econometrics and statistics. In 1997, I was hired as lecturer in the Faculty of Psychology and Educational Sciences (FPSE) of the University of Geneva. Between 1997 and 2001, my position has evolved until I became full professor (50%) in 2001 at HEC-Genève. Since 2018, I have a 100% permanent position at the Geneva School of Economics and Management (GSEM). Awards and I received a total of 3,277,537 CHF of personal research funding (Swiss National Science Foundation grants (SNSF) and private) for seven projects, and over 500,000 CHF of research funding for collaborative projects. I was awarded the Latzis International Prize (1995) for my Ph. D. Thesis, as well as a doctoral (1991) and a professorial (2000) fellowships from the SNSF. Research My main research interest is statistical methodology and its application in different domains. I started with the development of robust methods of estimation and inference for application in economics (welfare analysis, risk analysis) and psychology (psychometric). I am also interested in computational statistics and have developed estimation methods for complex models (latent dependence structures, missing data), time series (signal processing), and model selection in high dimensions. My publications are often co-authored with former Ph. D. students or with academic researchers in other universities and/or outside the methodological statistics field. -
Opting-In: Participation Bias in Economic Experiments*
Opting-in: Participation Bias in Economic Experiments* Robert Slonim** School of Economics, Faculty of Arts and Social Sciences, University of Sydney and IZA Carmen Wang Harvard Business School Ellen Garbarino Discipline of Marketing, Business School, University of Sydney Danielle Merrett The World Bank, Sydney, Australia. Abstract Assuming individuals rationally decide whether to participate or not to participate in lab experiments, we hypothesize several non-representative biases in the characteristics of lab participants. We test the hypotheses by first collecting survey and experimental data from a typical recruitment population and then inviting them to participate in a lab experiment. The results indicate that lab participants are not representative of the target population on almost all the hypothesized characteristics, including having lower income, working fewer hours, volunteering more often, and exhibiting behaviors correlated with interest in experiments and economics. The results reinforce the commonly understood limits of laboratory research to make quantitative inferences. We also discuss several methods for addressing non-representative biases to advance laboratory methods for improving quantitative inferences and consequently increasing confidence in qualitative conclusions. Key words: Participation Bias, Laboratory Experiments *Acknowledgements: We have greatly benefitted from numerous seminar participant comments at the University of Sydney, the Winter School on Experimental Economics at the University of Sydney, the University of Technology, Sydney, the University of New South Wales and the 6th annual Australia-New Zealand Workshop in Experimental Economics. We also appreciate comments and suggestions from Paul Frijters, Simon Gaechter, Ben Greiner, Nickolas Feltovich, Nikos Nikiforakis, Andreas Ortmann, the editor and two anonymous reviewers at the Journal of Economic Behavior and Organization. -
Cognitive Biases in Software Engineering: a Systematic Mapping Study
Cognitive Biases in Software Engineering: A Systematic Mapping Study Rahul Mohanani, Iflaah Salman, Burak Turhan, Member, IEEE, Pilar Rodriguez and Paul Ralph Abstract—One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work. Index Terms—Antecedents of cognitive bias. cognitive bias. debiasing, effects of cognitive bias. software engineering, systematic mapping. 1 INTRODUCTION OGNITIVE biases are systematic deviations from op- knowledge. No analogous review of SE research exists. The timal reasoning [1], [2]. In other words, they are re- purpose of this study is therefore as follows: curring errors in thinking, or patterns of bad judgment Purpose: to review, summarize and synthesize the current observable in different people and contexts. A well-known state of software engineering research involving cognitive example is confirmation bias—the tendency to pay more at- biases.