Why Do Humans Reason? Arguments for an Argumentative Theory
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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. -
(HCW) Surveys in Humanitarian Contexts in Lmics
Analytics for Operations working group GUIDANCE BRIEF Guidance for Health Care Worker (HCW) Surveys in humanitarian contexts in LMICs Developed by the Analytics for Operations Working Group to support those working with communities and healthcare workers in humanitarian and emergency contexts. This document has been developed for response actors working in humanitarian contexts who seek rapid approaches to gathering evidence about the experience of healthcare workers, and the communities of which they are a part. Understanding healthcare worker experience is critical to inform and guide humanitarian programming and effective strategies to promote IPC, identify psychosocial support needs. This evidence also informs humanitarian programming that interacts with HCWs and facilities such as nutrition, health reinforcement, communication, SGBV and gender. In low- and middle-income countries (LMIC), healthcare workers (HCW) are often faced with limited resources, equipment, performance support and even formal training to provide the life-saving work expected of them. In humanitarian contexts1, where human resources are also scarce, HCWs may comprise formally trained doctors, nurses, pharmacists, dentists, allied health professionals etc. as well as community members who perform formal health worker related duties with little or no trainingi. These HCWs frequently work in contexts of multiple public health crises, including COVID-19. Their work will be affected by availability of resources (limited supplies, materials), behaviour and emotion (fear), flows of (mis)information (e.g. understanding of expected infection prevention and control (IPC) measures) or services (healthcare policies, services and use). Multiple factors can therefore impact patients, HCWs and their families, not only in terms of risk of exposure to COVID-19, but secondary health, socio-economic and psycho-social risks, as well as constraints that interrupt or hinder healthcare provision such as physical distancing practices. -
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, -
Argumentation Theory and Practical Discourse
Ulrich's Bimonthly 1 Werner Ulrich's Home Page: Ulrich's Bimonthly Formerly "Picture of the Month" November-December 2009 Reflections on Reflective Practice (6b/7) HOME Part 6b: Argumentation theory and practical discourse – Habermas 2 Previous | Next WER NER ULRICH'S BIO We are still engaged in an effort to review the practical philosophies of For a hyperlinked overview of all issues of "Ulrich's PUBLICATIONS Aristotle, Kant, and Habermas, to see what we can learn from them for the Bimonthly" and the previous "Picture of the Month" series, READINGS ON CSH purpose of grounding reflective practice philosophically. The discussions of see the site map DOWNLOADS Aristotle and Kant were detailed but still found place within a single essay PDF file HARD COPIES each. The current discussion of Habermas, however, takes more space and I CRITICAL SYSTEMS have therefore decided to split it into three parts (see the right-hand note). HEURISTICS (CSH) Note: This is the second of three parts reviewing the Before we continue with the second part, it may help returning readers if I CST FOR PROFESSIONALS implications of the work of & CITIZENS briefly sum up where we stand; should you be new to the Bimonthly, I Habermas for reflective professional practice. The first A TRIBUTE TO C.W. CHURCHMAN recommend you read the previous Part 6a/7 to facilitate your reading of the part appeared in the Bimonthly of September-October 2009; LUG ANO SUMMER SCHOOL present Part 6b/7 (click on the "Previous" button at the top right of this page). the third and concluding part is planned for a later ULRICH'S BIMONTHLY Bimonthly. -
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. -
Nonresponse Bias and Trip Generation Models
64 TRANSPORTATION RESEARCH RECORD 1412 Nonresponse Bias and Trip Generation Models PIYUSHIMITA THAKURIAH, As:H1sH SEN, SnM S66T, AND EDWARD CHRISTOPHER There is serious concern over the fact that travel surveys often On the other hand, if the model does not satisfy these con overrepresent smaller households with higher incomes and better ditions, bias will occur even if there is a 100 percent response education levels and, in general, that nonresponse is nonrandom. rate. These conditions are satisfied by the model if the func However, when the data are used to build linear models, such as trip generation models, and the model is correctly specified, tional form of the model is correct and all important explan estimates of parameters are unbiased regardless of the nature of atory variables are included. the respondents, and the issues of how response rates and non Because categorical models do not have any problems with response bias are ameliorated. The more important task then is their functional form, and weighting and related issues are the complete specification of the model, without leaving out var taken care of, the authors prefer categorical trip generation iables that have some effect on the variable to be predicted. The models. This preference is discussed in a later section. There theoretical basis for this reasoning is given along with an example fore, the issue that remains when assessing bias in estimates of how bias may be assessed in estimates of trip generation model parameters. Some of the methods used are quite standard, but from categorical trip generation models is whether the model the manner in which these and other more nonstandard methods includes all the relevant independent variables or at least all have been systematically put together to assess bias in estimates important predictors. -
Detection of Confirmation and Distinction Biases in Visual
EuroVis Workshop on Trustworthy Visualization (TrustVis) (2019) R. Kosara, K. Lawonn, L. Linsen, and N. Smit (Editors) Detection of Confirmation and Distinction Biases in Visual Analytics Systems . A. Nalcaci , D. Girgin , S. Balki , F. Talay, H. A. Boz and S. Balcisoy 1Sabanci University, Faculty of Engineering & Natural Sciences, Istanbul, Turkey Abstract Cognitive bias is a systematic error that introduces drifts and distortions in the human judgment in terms of visual decompo- sition in the direction of the dominant instance. It has a significant role in decision-making process by means of evaluation of data visualizations. This paper elaborates on the experimental depiction of two cognitive bias types, namely Distinction Bias and Confirmation Bias, through the examination of cognate visual experimentations. The main goal of this implementation is to indicate the existence of cognitive bias in visual analytics systems through the adjustment of data visualization and crowdsourc- ing in terms of confirmation and distinction biases. Two distinct surveys that include biased and unbiased data visualizations which are related to a given data set were established in order to detect and measure the level of existence of introduced bias types. Practice of crowdsourcing which is provided by Amazon Mechanical Turk have been used for experimentation purposes through prepared surveys. Results statistically indicate that both distinction and confirmation biases has substantial effect and prominent significance on decision-making process. CCS Concepts • Human-centered computing ! Empirical studies in visualization; Visualization design and evaluation methods; 1. Introduction cording to existing studies, usual heuristic errors involve confirma- tion bias, which characterizes people’s approaches to receive the Visual perception is the combination of physical and thoughtful de- confirmatory corroboration of a pre-existing hypothesis and dis- sign that provides the ability to interpret the environment and the miss contrary information. -
Developments in Argumentation Theory
Developments in Argumentation Theory Frans H van Eemeren and Rob Grootendorst, University of Amsterdam Abstract In this paper, a survey is provided of the state of the art in argumentation theory. Some of the most significant approaches of the past two decades are discussed: Informal Logic, the formal theory of fallacies, formal dialectics, pragma-dialectics, Radical Argumentativism, and the modem revival of rhetoric. The survey is based not only on books, but also on papers published in professional joumals or included in conference proceedings. 1. Introduction Argumentation is a speech act complex aimed at resolving a difference of opinion. According to a prominent handbook definition, it is a verbal and social activity of reason carried out by a speaker or writer concerned with increasing (or decreasing) the acceptability of a controversial standpoint for a listener or reader; the con stellation of propositions brought to bear in this endeavour is intended to justify (or refute) the standpoint before a rational judge.\ Argumentation theory is the name given to the (systematic results of the) study of this discourse phenomenon. Argumentation theory studies the production, analysis and evaluation of argumen tation with a view of developing adequate criteria for determining the validity of the point of departure and presentational layout of argumentative discourse. The constellation of propositions advanced in argumentation is often referred to by the term argument, particularly by logicians and philosophers. This may lead to confusion because (in English) the word 'argument' has various meanings. Apart from (a) a reason and (b) a logical inference of a conclusion from one or more premisses, 'argument' can also denote (c) a discussion and (d) a quarrel. -
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. -
Equally Flexible and Optimal Response Bias in Older Compared to Younger Adults
AGING AND RESPONSE BIAS Equally Flexible and Optimal Response Bias in Older Compared to Younger Adults Roderick Garton, Angus Reynolds, Mark R. Hinder, Andrew Heathcote Department of Psychology, University of Tasmania Accepted for publication in Psychology and Aging, 8 February 2019 © 2019, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors’ permission. The final article will be available, upon publication, via its DOI: 10.1037/pag0000339 Author Note Roderick Garton, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Angus Reynolds, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Mark R. Hinder, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Andrew Heathcote, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia. Correspondence concerning this article should be addressed to Roderick Garton, University of Tasmania Private Bag 30, Hobart, Tasmania, Australia, 7001. Email: [email protected] This study was supported by Australian Research Council Discovery Project DP160101891 (Andrew Heathcote) and Future Fellowship FT150100406 (Mark R. Hinder), and by Australian Government Research Training Program Scholarships (Angus Reynolds and Roderick Garton). The authors would like to thank Matthew Gretton for help with data acquisition, Luke Strickland and Yi-Shin Lin for help with data analysis, and Claire Byrne for help with study administration. The trial-level data for the experiment reported in this manuscript are available on the Open Science Framework (https://osf.io/9hwu2/). 1 AGING AND RESPONSE BIAS Abstract Base-rate neglect is a failure to sufficiently bias decisions toward a priori more likely options. -
Can Self-Persuasion Reduce Hostile Attribution Bias in Young Children?
Journal of Abnormal Child Psychology https://doi.org/10.1007/s10802-018-0499-2 Can Self-Persuasion Reduce Hostile Attribution Bias in Young Children? Anouk van Dijk1 & Sander Thomaes1 & Astrid M. G. Poorthuis1 & Bram Orobio de Castro1 # The Author(s) 2018 Abstract Two experiments tested an intervention approach to reduce young children’s hostile attribution bias and aggression: self-persua- sion. Children with high levels of hostile attribution bias recorded a video-message advocating to peers why story characters who caused a negative outcome may have had nonhostile intentions (self-persuasion condition), or they simply described the stories (control condition). Before and after the manipulation, hostile attribution bias was assessed using vignettes of ambiguous provocations. Study 1 (n =83,age4–8) showed that self-persuasion reduced children’s hostile attribution bias. Study 2 (n = 121, age 6–9) replicated this finding, and further showed that self-persuasion was equally effective at reducing hostile attribution bias as was persuasion by others (i.e., listening to an experimenter advocating for nonhostile intentions). Effects on aggressive behavior, however, were small and only significant for one out of four effects tested. This research provides the first evidence that self-persuasion may be an effective approach to reduce hostile attribution bias in young children. Keywords Hostile attribution bias . Self-persuasion . Aggression . Intervention . Experiments Children’s daily social interactions abound with provocations by Dodge 1994). The present research tests an intervention approach peers, such as when they are physically hurt, laughed at, or ex- to reduce hostile attribution bias in young children. cluded from play. The exact reasons behind these provocations, Most interventions that effectively reduce children’s hostile and especially the issue of whether hostile intent was involved, attribution bias rely on attribution retraining techniques (e.g., are often unclear.