An Intelligence in Our Image: the Risks of Bias and Errors in Artificial
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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. -
An Unconventional Solution for a Sociotechnical
1 RESOLVING THE PROBLEM OF ALGORITHMIC DISSONANCE: AN UNCONVENTIONAL SOLUTION FOR A SOCIOTECHNICAL PROBLEM Shiv Issar | Department of Sociology, University of Wisconsin-Milwaukee | [email protected] June 29, 2021 Abstract In this paper, I propose the concept of “algorithmic dissonance”, which characterizes the inconsistencies that emerge through the fissures that lie between algorithmic systems that utilize system identities, and sociocultural systems of knowledge that interact with them. A product of human-algorithm interaction, algorithmic dissonance builds upon the concepts of algorithmic discrimination and algorithmic awareness, offering greater clarity towards the comprehension of these sociotechnical entanglements. By employing Du Bois’ concept of “double consciousness” and black feminist theory, I argue that all algorithmic dissonance is racialized. Next, I advocate for the use of speculative methodologies and art for the creation of critically informative sociotechnical imaginaries that might serve a basis for the sociological critique and resolution of algorithmic dissonance. Algorithmic dissonance can be an effective check against structural inequities, and of interest to scholars and practitioners concerned with running “algorithm audits”. Keywords: Algorithmic Dissonance, Algorithmic Discrimination, Algorithmic Bias, Algorithm Audits, System Identity, Speculative methodologies. 2 Introduction This paper builds upon Aneesh’s (2015) notion of system identities by utilizing their nature beyond their primary function of serving algorithmic systems and the process of algorithmic decision- making. As Aneesh (2015) describes, system identities are data-bound constructions built by algorithmic systems for the (singular) purpose of being used by algorithmic systems. They are meant to simulate a specific dimension (or a set of dimensions) of personhood , just as algorithmic systems and algorithmic decision-making are meant to simulate the agents of different social institutions, and the decisions that those agents would make. -
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, -
Empirical Assessment of Risk Factors: How Online and Offline Lifestyle, Social Learning, and Social Networking Sites Influence Crime Victimization Ashley Bettencourt
Bridgewater State University Virtual Commons - Bridgewater State University Master’s Theses and Projects College of Graduate Studies 12-2014 Empirical Assessment of Risk Factors: How Online and Offline Lifestyle, Social Learning, And Social Networking Sites Influence Crime Victimization Ashley Bettencourt Follow this and additional works at: http://vc.bridgew.edu/theses Part of the Criminology and Criminal Justice Commons Recommended Citation Bettencourt, Ashley. (2014). Empirical Assessment of Risk Factors: How Online and Offline Lifestyle, Social Learning, And Social Networking Sites Influence Crime Victimization. In BSU Master’s Theses and Projects. Item 10. Available at http://vc.bridgew.edu/theses/10 Copyright © 2014 Ashley Bettencourt This item is available as part of Virtual Commons, the open-access institutional repository of Bridgewater State University, Bridgewater, Massachusetts. Empirical Assessment of Risk Factors: How Online and Offline Lifestyle, Social Learning, and Social Networking Sites Influence Crime Victimization By: Ashley Bettencourt Thesis Submitted in fulfillment of the requirements for the degree of Master of Science in Criminal Justice in the Graduate College of Bridgewater State University, 2014 Bridgewater, Massachusetts Thesis Chair: Dr. Kyung-Shick Choi 2 Empirical Assessment of Risk Factors: How Online and Offline Lifestyle, Social Learning, and Social Networking Sites Influence Crime Victimization Thesis By Ashley Bettencourt Approved as to style and content by: ______________________________ Kyung-shick -
Artificial Consciousness and the Consciousness-Attention Dissociation
Consciousness and Cognition 45 (2016) 210–225 Contents lists available at ScienceDirect Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog Review article Artificial consciousness and the consciousness-attention dissociation ⇑ Harry Haroutioun Haladjian a, , Carlos Montemayor b a Laboratoire Psychologie de la Perception, CNRS (UMR 8242), Université Paris Descartes, Centre Biomédical des Saints-Pères, 45 rue des Saints-Pères, 75006 Paris, France b San Francisco State University, Philosophy Department, 1600 Holloway Avenue, San Francisco, CA 94132 USA article info abstract Article history: Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to Received 6 July 2016 implement sophisticated forms of human intelligence in machines. This research attempts Accepted 12 August 2016 to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable Keywords: once we overcome short-term engineering challenges. We believe, however, that phenom- Artificial intelligence enal consciousness cannot be implemented in machines. This becomes clear when consid- Artificial consciousness ering emotions and examining the dissociation between consciousness and attention in Consciousness Visual attention humans. While we may be able to program ethical behavior based on rules and machine Phenomenology learning, we will never be able to reproduce emotions or empathy by programming such Emotions control systems—these will be merely simulations. Arguments in favor of this claim include Empathy considerations about evolution, the neuropsychological aspects of emotions, and the disso- ciation between attention and consciousness found in humans. -
Computability and Complexity
Computability and Complexity Lecture Notes Herbert Jaeger, Jacobs University Bremen Version history Jan 30, 2018: created as copy of CC lecture notes of Spring 2017 Feb 16, 2018: cleaned up font conversion hickups up to Section 5 Feb 23, 2018: cleaned up font conversion hickups up to Section 6 Mar 15, 2018: cleaned up font conversion hickups up to Section 7 Apr 5, 2018: cleaned up font conversion hickups up to the end of these lecture notes 1 1 Introduction 1.1 Motivation This lecture will introduce you to the theory of computation and the theory of computational complexity. The theory of computation offers full answers to the questions, • what problems can in principle be solved by computer programs? • what functions can in principle be computed by computer programs? • what formal languages can in principle be decided by computer programs? Full answers to these questions have been found in the last 70 years or so, and we will learn about them. (And it turns out that these questions are all the same question). The theory of computation is well-established, transparent, and basically simple (you might disagree at first). The theory of complexity offers many insights to questions like • for a given problem / function / language that has to be solved / computed / decided by a computer program, how long does the fastest program actually run? • how much memory space has to be used at least? • can you speed up computations by using different computer architectures or different programming approaches? The theory of complexity is historically younger than the theory of computation – the first surge of results came in the 60ties of last century. -
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 -
Inventing Computational Rhetoric
INVENTING COMPUTATIONAL RHETORIC By Michael W. Wojcik A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Digital Rhetoric and Professional Writing — Master of Arts 2013 ABSTRACT INVENTING COMPUTATIONAL RHETORIC by Michael W. Wojcik Many disciplines in the humanities are developing “computational” branches which make use of information technology to process large amounts of data algorithmically. The field of computational rhetoric is in its infancy, but we are already seeing interesting results from applying the ideas and goals of rhetoric to text processing and related areas. After considering what computational rhetoric might be, three approaches to inventing computational rhetorics are presented: a structural schema, a review of extant work, and a theoretical exploration. Copyright by MICHAEL W. WOJCIK 2013 For Malea iv ACKNOWLEDGEMENTS Above all else I must thank my beloved wife, Malea Powell, without whose prompting this thesis would have remained forever incomplete. I am also grateful for the presence in my life of my terrific stepdaughter, Audrey Swartz, and wonderful granddaughter Lucille. My thesis committee, Dean Rehberger, Bill Hart-Davidson, and John Monberg, pro- vided me with generous guidance and inspiration. Other faculty members at Michigan State who helped me explore relevant ideas include Rochelle Harris, Mike McLeod, Joyce Chai, Danielle Devoss, and Bump Halbritter. My previous academic program at Miami University did not result in a degree, but faculty there also contributed greatly to my the- oretical understanding, particularly Susan Morgan, Mary-Jean Corbett, Brit Harwood, J. Edgar Tidwell, Lori Merish, Vicki Smith, Alice Adams, Fran Dolan, and Keith Tuma. -
JULIAN ASSANGE: When Google Met Wikileaks
JULIAN ASSANGE JULIAN +OR Books Email Images Behind Google’s image as the over-friendly giant of global tech when.google.met.wikileaks.org Nobody wants to acknowledge that Google has grown big and bad. But it has. Schmidt’s tenure as CEO saw Google integrate with the shadiest of US power structures as it expanded into a geographically invasive megacorporation... Google is watching you when.google.met.wikileaks.org As Google enlarges its industrial surveillance cone to cover the majority of the world’s / WikiLeaks population... Google was accepting NSA money to the tune of... WHEN GOOGLE MET WIKILEAKS GOOGLE WHEN When Google Met WikiLeaks Google spends more on Washington lobbying than leading military contractors when.google.met.wikileaks.org WikiLeaks Search I’m Feeling Evil Google entered the lobbying rankings above military aerospace giant Lockheed Martin, with a total of $18.2 million spent in 2012. Boeing and Northrop Grumman also came below the tech… Transcript of secret meeting between Julian Assange and Google’s Eric Schmidt... wikileaks.org/Transcript-Meeting-Assange-Schmidt.html Assange: We wouldn’t mind a leak from Google, which would be, I think, probably all the Patriot Act requests... Schmidt: Which would be [whispers] illegal... Assange: Tell your general counsel to argue... Eric Schmidt and the State Department-Google nexus when.google.met.wikileaks.org It was at this point that I realized that Eric Schmidt might not have been an emissary of Google alone... the delegation was one part Google, three parts US foreign-policy establishment... We called the State Department front desk and told them that Julian Assange wanted to have a conversation with Hillary Clinton... -
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. -
Algorithmic Awareness
ALGORITHMIC AWARENESS CONVERSATIONS WITH YOUNG CANADIANS ABOUT ARTIFICIAL INTELLIGENCE AND PRIVACY Algorithmic Awareness: Conversations with Young Canadians about Artificial Intelligence and Privacy This report can be downloaded from: mediasmarts.ca/research-policy Cite as: Brisson-Boivin, Kara and Samantha McAleese. (2021). “Algorithmic Awareness: Conversations with Young Canadians about Artificial Intelligence and Privacy.” MediaSmarts. Ottawa. Written for MediaSmarts by: Kara Brisson-Boivin, PhD Samantha McAleese MediaSmarts 205 Catherine Street, Suite 100 Ottawa, ON Canada K2P 1C3 T: 613-224-7721 F: 613-761-9024 Toll-free line: 1-800-896-3342 [email protected] mediasmarts.ca @mediasmarts This research was made possible by the financial contributions from the Office of the Privacy Commissioner of Canada’s contributions program. Algorithmic Awareness: Conversations with Young Canadians about Artificial Intelligence and Privacy. MediaSmarts © 2021 2 Table of Contents Key Terms .................................................................................................................................................... 4 Introduction ................................................................................................................................................. 6 Algorithms and Artificial Intelligence: ............................................................................................... 9 What We (Don't) Know ......................................................................................................................... -
Computability Theory
CSC 438F/2404F Notes (S. Cook and T. Pitassi) Fall, 2019 Computability Theory This section is partly inspired by the material in \A Course in Mathematical Logic" by Bell and Machover, Chap 6, sections 1-10. Other references: \Introduction to the theory of computation" by Michael Sipser, and \Com- putability, Complexity, and Languages" by M. Davis and E. Weyuker. Our first goal is to give a formal definition for what it means for a function on N to be com- putable by an algorithm. Historically the first convincing such definition was given by Alan Turing in 1936, in his paper which introduced what we now call Turing machines. Slightly before Turing, Alonzo Church gave a definition based on his lambda calculus. About the same time G¨odel,Herbrand, and Kleene developed definitions based on recursion schemes. Fortunately all of these definitions are equivalent, and each of many other definitions pro- posed later are also equivalent to Turing's definition. This has lead to the general belief that these definitions have got it right, and this assertion is roughly what we now call \Church's Thesis". A natural definition of computable function f on N allows for the possibility that f(x) may not be defined for all x 2 N, because algorithms do not always halt. Thus we will use the symbol 1 to mean “undefined". Definition: A partial function is a function n f :(N [ f1g) ! N [ f1g; n ≥ 0 such that f(c1; :::; cn) = 1 if some ci = 1. In the context of computability theory, whenever we refer to a function on N, we mean a partial function in the above sense.