Detecting and Mitigating Human Bias in Visual Analytics
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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, -
Application of Human Factors in Reducing Human Error in Existing Offshore Facilities
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln United States Department of Transportation -- Publications & Papers U.S. Department of Transportation 2002 Application of Human Factors in Reducing Human Error in Existing Offshore Facilities Jeffrey Thomas ExxonMobil, [email protected] Clifford C. Baker American Bureau of Shipping Thomas B. Malone Carlow International Incorporated, [email protected] John T. Malone Carlow Associates, [email protected] Christina L. Hard BP America Inc., [email protected] See next page for additional authors Follow this and additional works at: https://digitalcommons.unl.edu/usdot Part of the Civil and Environmental Engineering Commons Thomas, Jeffrey; Baker, Clifford C.; Malone, Thomas B.; Malone, John T.; Hard, Christina L.; Rezende, Ivan C. L.; Caruana, Sally; and Witten, Mark, "Application of Human Factors in Reducing Human Error in Existing Offshore Facilities" (2002). United States Department of Transportation -- Publications & Papers. 34. https://digitalcommons.unl.edu/usdot/34 This Article is brought to you for free and open access by the U.S. Department of Transportation at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in United States Department of Transportation -- Publications & Papers by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Authors Jeffrey Thomas, Clifford C. Baker, Thomas B. Malone, John T. Malone, Christina L. Hard, Ivan C. L. Rezende, Sally Caruana, and Mark Witten This article is available at DigitalCommons@University of Nebraska - Lincoln: https://digitalcommons.unl.edu/usdot/ 34 Application of Human Factors Engineering in Reducing Human Error in Existing Offshore Systems WORKING GROUP 3 2ND INTERNATIONAL WORKSHOP ON HUMAN FACTORS IN OFFSHORE OPERATIONS (HFW2002) APPLICATION OF HUMAN FACTORS ENGINEERING IN REDUCING HUMAN ERROR IN EXISTING OFFSHORE SYSTEMS Jeffrey Thomas ExxonMobil Houston, TX USA [email protected] Clifford C. -
Identifying Present Bias from the Timing of Choices∗
Identifying Present Bias from the Timing of Choices∗ Paul Heidhues Philipp Strack DICE Yale February 22, 2021 Abstract A (partially naive) quasi-hyperbolic discounter repeatedly chooses whether to com- plete a task. Her net benefits of task completion are drawn independently between periods from a time-invariant distribution. We show that the probability of complet- ing the task conditional on not having done so earlier increases towards the deadline. Conversely, we establish non-identifiability by proving that for any time-preference pa- rameters and any data set with such (weakly increasing) task-completion probabilities, there exists a stationary payoff distribution that rationalizes the agent's behavior if she is either sophisticated or fully naive. Additionally, we provide sharp partial identifica- tion for the case of observable continuation values. ∗We thank seminar and conference audiences, and especially Nageeb Ali, Ned Augenblick, Stefano DellaVigna, Drew Fudenberg, Ori Heffetz, Botond K}oszegi, Muriel Niederle, Philipp Schmidt-Dengler, Charles Sprenger, Dmitry Taubinsky, and Florian Zimmermann for insightful and encouraging comments. Part of the work on this paper was carried out while the authors visited briq, whose hospitality is gratefully acknowledged. Philipp Strack was supported by a Sloan fellowship. Electronic copy available at: https://ssrn.com/abstract=3386017 1 Introduction Intuition and evidence suggests that many individuals are present-biased (e.g. Frederick et al., 2002; Augenblick et al., 2015; Augenblick and Rabin, 2019). Building on work by Laibson (1997) and others, O'Donoghue and Rabin (1999, 2001) illustrate within the quasi-hyperbolic discounting model that present bias, especially in combination with a lack of understanding thereof, leads individuals to procrastinate unpleasant tasks and to precrastinate pleasant experiences. -
Addressing Present Bias in Movie Recommender Systems and Beyond
Addressing Present Bias in Movie Recommender Systems and Beyond Kai Lukoffa aUniversity of Washington, Seattle, WA, USA Abstract Present bias leads people to choose smaller immediate rewards over larger rewards in the future. Recom- mender systems often reinforce present bias because they rely predominantly upon what people have done in the past to recommend what they should do in the future. How can recommender systems over- come this present bias to recommend items in ways that match with users’ aspirations? Our workshop position paper presents the motivation and design for a user study to address this question in the domain of movies. We plan to ask Netflix users to rate movies that they have watched in the past for thelong- term rewards that these movies provided (e.g., memorable or meaningful experiences). We will then evaluate how well long-term rewards can be predicted using existing data (e.g., movie critic ratings). We hope to receive feedback on this study design from other participants at the HUMANIZE workshop and spark conversations about ways to address present bias in recommender systems. Keywords present bias, cognitive bias, algorithmic bias, recommender systems, digital wellbeing, movies 1. Introduction such as Schindler’s List [2]. Recommender systems (RS), algorithmic People often select smaller immediate re- systems that predict the preference a user wards over larger rewards in the future, a would give to an item, often reinforce present phenomenon that is known as present bias bias. Today, the dominant paradigm of rec- or time discounting. This applies to deci- ommender systems is behaviorism: recom- sions such as what snack to eat [1,2], how mendations are selected based on behavior much to save for retirement [3], or which traces (“what users do”) and they largely ne- movies to watch [2]. -
Prior Belief Innuences on Reasoning and Judgment: a Multivariate Investigation of Individual Differences in Belief Bias
Prior Belief Innuences on Reasoning and Judgment: A Multivariate Investigation of Individual Differences in Belief Bias Walter Cabral Sa A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Education University of Toronto O Copyright by Walter C.Si5 1999 National Library Bibliothèque nationaIe of Canada du Canada Acquisitions and Acquisitions et Bibliographic Services services bibliographiques 395 Wellington Street 395, rue WePington Ottawa ON K1A ON4 Ottawa ON KtA ON4 Canada Canada Your file Votre rëfërence Our fi& Notre réterence The author has granted a non- L'auteur a accordé une licence non exclusive Licence allowing the exclusive permettant à la National Libraq of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sell reproduire, prêter, distribuer ou copies of this thesis in microforni, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/film, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor subçtantial extracts fi-om it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Prior Belief Influences on Reasoning and Judgment: A Multivariate Investigation of Individual Differences in Belief Bias Doctor of Philosophy, 1999 Walter Cabral Sa Graduate Department of Education University of Toronto Belief bias occurs when reasoning or judgments are found to be overly infiuenced by prior belief at the expense of a normatively prescribed accommodation of dl the relevant data. -
Poverty and Decision-Making
Poverty and decision-making How behavioural science can improve opportunity in the UK Kizzy Gandy, Katy King, Pippa Streeter Hurle, Chloe Bustin and Kate Glazebrook October 2016 1 © Behavioural Insights Ltd Acknowledgements Many people contributed to this report. Particular thanks go to David Halpern, Andy Hollingsworth, Raj Chande, Tiina Likki, Ben Curtis and Susannah Hume from the Behavioural Insights Team who provided expert advice on the literature and policy context. Many people inside government also provided review, and several leading academic were an invaluable source of insights and knowledge, particularly Eldar Shafir, Jonathan Morduch and Evrim Altintas. Finally, we thank the Joseph Rowntree Foundation for providing useful guidance along the way and for their enthusiasm and support for our intellectual ambition. 2 © Behavioural Insights Ltd Contents Glossary of terms ............................................................................................. 6 Executive summary ............................................................................................ 7 Chapter 1 - Introduction: applying behavioural science to the study of poverty .... 11 Section 1.1 Background and principal audiences for the report .................. 11 Section 1.2 Report aims and research questions ....................................... 11 Section 1.3 The psychology of decision-making ........................................ 12 Section 1.4 Structure of the report .......................................................... 14 Chapter 2 -
Thinking and Reasoning
Thinking and Reasoning Thinking and Reasoning ■ An introduction to the psychology of reason, judgment and decision making Ken Manktelow First published 2012 British Library Cataloguing in Publication by Psychology Press Data 27 Church Road, Hove, East Sussex BN3 2FA A catalogue record for this book is available from the British Library Simultaneously published in the USA and Canada Library of Congress Cataloging in Publication by Psychology Press Data 711 Third Avenue, New York, NY 10017 Manktelow, K. I., 1952– Thinking and reasoning : an introduction [www.psypress.com] to the psychology of reason, Psychology Press is an imprint of the Taylor & judgment and decision making / Ken Francis Group, an informa business Manktelow. p. cm. © 2012 Psychology Press Includes bibliographical references and Typeset in Century Old Style and Futura by index. Refi neCatch Ltd, Bungay, Suffolk 1. Reasoning (Psychology) Cover design by Andrew Ward 2. Thought and thinking. 3. Cognition. 4. Decision making. All rights reserved. No part of this book may I. Title. be reprinted or reproduced or utilised in any BF442.M354 2012 form or by any electronic, mechanical, or 153.4'2--dc23 other means, now known or hereafter invented, including photocopying and 2011031284 recording, or in any information storage or retrieval system, without permission in writing ISBN: 978-1-84169-740-6 (hbk) from the publishers. ISBN: 978-1-84169-741-3 (pbk) Trademark notice : Product or corporate ISBN: 978-0-203-11546-6 (ebk) names may be trademarks or registered trademarks, and are used -
Health Insurance & Behavioral Economics
Health Insurance & Behavioral Economics Alan C. Monheit, Ph.D. Rutgers University School of Public Health Rutgers Center for State Health Policy National Bureau of Economic Research Standard Model Underlying Consumer Choice • In the standard economic model of consumer choice, individuals are characterized as “. lightning quick calculators of pleasure and pain.” • Built on very strong behavioral assumptions: – Rationality – Full information on attributes of commodities and their substitutes. – Easy to assess the benefits & costs of consumption alternatives. – Individuals are constrained by a budget. – More choice is welfare enhancing. • These assumptions of rationality & full information carry over to the standard or benchmark model of health insurance choice. Center for State Health Policy Benchmark Model of Health Insurance Choice • Key assumptions: – Individuals are risk averse: • Prefer a loss with certainty rather than a gamble with the same expected loss. • Will incur a certain loss to obtain an uncertain flow of benefits. • Are willing to pay a “risk premium” above an actuarially fair insurance premium. – Can accurately assess the probability of future losses. – Can accurately assess income losses from adverse events. – Can accurately assess the benefits & costs of purchasing or not purchasing insurance. Center for State Health Policy Implications from the Standard Model • Value of the standard model is that it provides testable predictions about health insurance choices: – More risk averse individuals will purchase more insurance than those who are less risk averse. – Generally, as the probability of a loss increases, individuals will purchase more insurance. However, • Individuals will not purchase insurance for very large or very small probabilities of income loss. – More insurance is purchased as the expected income loss increases. -
Human Factors
Chapter 14 Human Factors Introduction Why are human conditions, such as fatigue, complacency, and stress, so important in aviation maintenance? These conditions, along with many others, are called human factors. Human factors directly cause or contribute to many aviation accidents. It is universally agreed that 80 percent of maintenance errors involve human factors. If they are not detected, they can cause events, worker injuries, wasted time, and even accidents. [Figure 14-1] 14-1 maintenance errors are parts installed incorrectly, missing Human Factors parts, and necessary checks not being performed. In Mental comparison to many other threats to aviation safety, the State Emotional mistakes of an aviation maintenance technician (AMT) can be State more difficult to detect. Often times, these mistakes are present but not visible and have the potential to remain latent, affecting the safe operation of aircraft for longer periods of time. Human AMTs are confronted with a set of human factors unique Capabilities within aviation. Often times, they are working in the evening Physical or early morning hours, in confined spaces, on platforms that State are up high, and in a variety of adverse temperature/humidity conditions. The work can be physically strenuous, yet it also Human requires attention to detail. [Figure 14-2] Because of the Limitations nature of the maintenance tasks, AMTs commonly spend more time preparing for a task than actually carrying it out. Proper Environmental documentation of all maintenance work is a key element, and Conditions Human-Machine AMTs typically spend as much time updating maintenance Interface logs as they do performing the work. -
Fixing Biases: Principles of Cognitive De-Biasing
Fixing biases: Principles of cognitive de-biasing Pat Croskerry MD, PhD Clinical Reasoning in Medical Education National Science Learning Centre, University of York Workshop, November 26, 2019 Case q A 65 year old female presents to the ED with a complaint of shoulder sprain. She said she was gardening this morning and injured her shoulder pushing her lawn mower. q At triage she has normal vital signs and in no distress. The triage nurse notes her complaint and triages her to the fast track area. q She is seen by an emergency physician who notes her complaint and examines her shoulder. He orders an X-ray. q The shoulder X ray shows narrowing of the joint and signs of osteoarthrtritis q He discharges her with a sling and Rx for Arthrotec q She is brought to the ED 4 hours later following an episode of syncope, sweating, and weakness. She is diagnosed with an inferior MI. Biases q A 65 year old female presents to the ED with a complaint of ‘shoulder sprain’. She said she was gardening this morning and sprained her shoulder pushing her lawn mower (Framing). q At triage she has normal vital signs and in no distress. The triage nurse notes her complaint and triages her to the fast track area (Triage cueing). q She is seen by an emergency physician who notes her complaint and examines her shoulder. He orders an X-ray (Ascertainment bias). q The shoulder X ray shows narrowing of the joint and signs of osteoarthrtritis. He explains to the patient the cause of her pain (Confirmation bias). -
Trends and Issues
TRENDS AND ISSUES DECEMBER 2015 THE ROLE OF TIME PREFERENCES AND EXPONENTIAL- GROWTH BIAS IN RETIREMENT SAVINGS Gopi Shah Goda Matthew R. Levy Colleen Flaherty Aaron J. Sojourner Joshua Tasoff Stanford University London School of Manchester University of Minnesota Claremont Graduate and NBER Economics University of Minnesota and IZA University EXECUTIVE SUMMARY Understanding the drivers of retirement-wealth accumulation is critical given that changes in employer-provided retirement plans have shifted responsibility for retirement savings increasingly to individuals. This paper empirically investigates two factors – one cognitive and one motivational – that may lead to low levels of retirement wealth accumulation. Cognitively, individuals may underestimate the future value of saving today by neglecting compound interest when assessing returns to saving, which is known as exponential-growth bias (EG bias). The motivational factor is present bias, which is the tendency for individuals to act patient in the long term, but impatient in the near term such that they continually put off saving today. Using an online survey of a broad sample of US households, we measure individuals’ tendency for EG bias and present bias and relate them to individuals’ retirement savings. We find that both biases are prevalent and are negatively related to levels of retirement savings. In particular, 78% of the sample exhibits EG bias and 55% is present biased. However, having one bias does not increase the likelihood of having the other. As for retirement savings, individuals who have accurate understanding of exponential growth have $27,300 more in retirement savings relative to those who neglect exponential growth, while those who value time consistently have $25,200 more in savings relative to individuals who have present bias. -
Behavioral Economics of Education: IZA DP No
IZA DP No. 8853 Behavioral Economics of Education: Progress and Possibilities Adam M. Lavecchia Heidi Liu Philip Oreopoulos February 2015 DISCUSSION PAPER SERIES Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor Behavioral Economics of Education: Progress and Possibilities Adam M. Lavecchia University of Toronto Heidi Liu Harvard University Philip Oreopoulos University of Toronto, NBER, CIFAR and IZA Discussion Paper No. 8853 February 2015 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: [email protected] Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character.