Algorithmic Bias, AI Ethics, Allocative Harm, Representational Harm
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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
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. -
Computational Thinking's Influence on Research and Education For
Computational thinking’s influence on research and education for all Influenza del pensiero computazionale nella ricerca e nell’educazione per tutti Jeannette M. Wing Columbia University, New York, N.Y., USA, [email protected] HOW TO CITE Wing, J.M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14. doi: 10.17471/2499-4324/922 ABSTRACT Computer science has produced, at an astonishing and breathtaking pace, amazing technology that has transformed our lives with profound economic and societal impact. In the course of the past ten years, we have come to realize that computer science offers not just useful software and hardware artifacts, but also an intellectual framework for thinking, what I call “computational thinking”. Everyone can benefit from thinking computationally. My grand vision is that computational thinking will be a fundamental skill—just like reading, writing, and arithmetic—used by everyone by the middle of the 21st Century. KEYWORDS Computational Thinking, Education, Curriculum. SOMMARIO L’informatica ha prodotto, a un ritmo sorprendente e mozzafiato, una straordinaria tecnologia che ha profondamente trasformato le nostre vite con importanti conseguenze economiche e sociali. Negli ultimi dieci anni ci si è resi conto che l’informatica non solo offre utili artefatti software e hardware, ma anche un paradigma di ragionamento che chiamo “pensiero computazionale”. Ognuno di noi può trarre vantaggio dal pensare in modo computazionale. Mi aspetto che il pensiero computazionale costituirà un’abilità fondamentale - come leggere, scrivere e far di conto – che verrà utilizzata da tutti entro la prima metà del XXI secolo. -
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 ......................................................................................................................... -
Algorithm Bias Playbook Presentation
Algorithmic Bias Playbook Ziad Obermeyer June, 2021 Rebecca Nissan Michael Stern Stephanie Eaneff Emily Joy Bembeneck Sendhil Mullainathan ALGORITHMIC BIAS PLAYBOOK Is your organization using biased algorithms? How would you know? What would you do if so? This playbook describes 4 steps your organization can take to answer these questions. It distills insights from our years of applied work helping others diagnose and mitigate bias in live algorithms. Algorithmic bias is everywhere. Our work with dozens of organizations—healthcare providers, insurers, technology companies, and regulators—has taught us that biased algorithms are deployed throughout the healthcare system, influencing clinical care, operational workflows, and policy. This playbook will teach you how to define, measure, and mitigate racial bias in live algorithms. By working through concrete examples—cautionary tales—you’ll learn what bias looks like. You’ll also see reasons for optimism—success stories—that demonstrate how bias can be mitigated, transforming flawed algorithms into tools that fight injustice. Who should read this? We wrote this playbook with three kinds of people in mind. ● C-suite leaders (CTOs, CMOs, CMIOs, etc.): Algorithms may be operating at scale in your organization—but what are they doing? And who is responsible? This playbook will help you think strategically about how algorithms can go wrong, and what your technical teams can do about it. It also lays out oversight structures you can put in place to prevent bias. ● Technical teams working in health care: We’ve found that the difference between biased and unbiased algorithms is often a matter of subtle technical choices. If you build algorithms, this playbook will help you make those choices better. -
Algorithmic Bias on the Implicit Biases of Social Technology
Algorithmic Bias On the Implicit Biases of Social Technology Gabbrielle M Johnson New York University Abstract Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scien- tists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the bias itself, making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques by presenting what I call `the Proxy Prob- lem'. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algo- rithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution. 1 Introduction On March 23rd, 2016, Microsoft Corporation released Tay, an artificial intelligence (AI) Twitter chatbot intended to mimic the language patterns of a 19-year-old American girl. Tay operated by learning from human Twitter users with whom it interacted. Only 16 hours after its launch, Tay was shut down for authoring a number of tweets endorsing Nazi ideology and harassing other Twitter users. -
Deep Automation Bias: How to Tackle a Wicked Problem of AI?
big data and cognitive computing Article Deep Automation Bias: How to Tackle a Wicked Problem of AI? Stefan Strauß Institute of Technology Assessment (ITA), Austrian Academy of Sciences, 1030 Vienna, Austria Abstract: The increasing use of AI in different societal contexts intensified the debate on risks, ethical problems and bias. Accordingly, promising research activities focus on debiasing to strengthen fairness, accountability and transparency in machine learning. There is, though, a tendency to fix societal and ethical issues with technical solutions that may cause additional, wicked problems. Alternative analytical approaches are thus needed to avoid this and to comprehend how societal and ethical issues occur in AI systems. Despite various forms of bias, ultimately, risks result from eventual rule conflicts between the AI system behavior due to feature complexity and user practices with limited options for scrutiny. Hence, although different forms of bias can occur, automation is their common ground. The paper highlights the role of automation and explains why deep automation bias (DAB) is a metarisk of AI. Based on former work it elaborates the main influencing factors and develops a heuristic model for assessing DAB-related risks in AI systems. This model aims at raising problem awareness and training on the sociotechnical risks resulting from AI-based automation and contributes to improving the general explicability of AI systems beyond technical issues. Keywords: artificial intelligence; machine learning; automation bias; fairness; transparency; account- ability; explicability; uncertainty; human-in-the-loop; awareness raising Citation: Strauß, S. Deep Automation Bias: How to Tackle a 1. Introduction Wicked Problem of AI? Big Data Cogn. -
Review Into Bias in Algorithmic Decision-Making
Review into bias in algorithmic decision-making November 2020 Review into bias in algorithmic decision-making: Contents Contents Preface 3 By the Board of the Centre for Data Ethics and Innovation Executive summary 5 Part I: Introduction 14 1. Background and scope 15 2. The issue 20 Part II: Sector reviews 37 3. Recruitment 39 4. Financial services 48 5. Policing 61 6. Local government 72 Part III: Addressing the challenges 80 7. Enabling fair innovation 83 8. The regulatory environment 108 9. Transparency in the public sector 130 10. Next steps and future challenges 144 11. Acknowledgements 147 Centre for Data Ethics and Innovation 2 Review into bias in algorithmic decision-making: Contents Preface Fairness is a highly prized human value. Algorithms, like all technology, should work for people, Societies in which individuals can flourish and not against them. need to be held together by practices and This is true in all sectors, but especially key in the public institutions that are regarded as fair. What sector. When the state is making life-affecting decisions it means to be fair has been much debated about individuals, that individual often can’t go elsewhere. throughout history, rarely more so than in Society may reasonably conclude that justice requires decision-making processes to be designed so that human recent months. Issues such as the global judgment can intervene where needed to achieve fair Black Lives Matter movement, the “levelling and reasonable outcomes for each person, informed by up” of regional inequalities within the UK, individual evidence. and the many complex questions of fairness raised by the COVID-19 pandemic have kept Data gives us a powerful weapon to see where fairness and equality at the centre of public bias is occurring and measure whether our debate. -
Review Into Bias in Algorithmic Decision-Making 12Th March 2021
Review into Bias in Algorithmic Decision-Making 12th March 2021 Lara Macdonald, Senior Policy Advisor, CDEI Ghazi Ahamat, Senior Policy Advisor, CDEI Dr Adrian Weller, CDEI Board Member and Programme Director for AI at the Alan Turing Institute @CDEIUK Government’s role in reducing algorithmic bias ● As a major user of technology, government and the public sector should set standards, provide guidance and highlight good practice ● As a regulator, government needs to adapt existing regulatory frameworks to incentivise ethical innovation 2 Bias, discrimination & fairness ● We interpreted bias in algorithmic decision-making as: the use of algorithms which can cause a systematic skew in decision-making that results in unfair outcomes. ● Some forms of bias constitute discrimination under UK equality (anti-discrimination) law, namely when bias leads to unfair treatment based on certain protected characteristics. ● There are also other kinds of algorithmic bias that are non-discriminatory, but still lead to unfair outcomes. ● Fairness is about much more than the absence of bias ● There are multiple (incompatible) concepts of fairness 3 How should organisations address algorithmic bias? Guidance to organisation leaders and boards Achieving this in practice will include: ● Understand the capabilities and limits of algorithmic tools Building (diverse, multidisciplinary) internal capacity ● Consider carefully whether individuals will be fairly treated by the decision-making process that the tool forms part of Understand risks of bias by measurement -
Thomas Heldt
Thomas Heldt Institute for Medical Engineering & Science p: (617) 324-5005 Massachusetts Institute of Technology f: (617) 253-7498 Room E25-324 e: [email protected] 77 Massachusetts Avenue Cambridge, MA 02139 Education Massachusetts Institute of Technology, Cambridge, MA, USA 1997 { 2004 Harvard { MIT Division of Health Sciences and Technology Ph.D. Medical Physics, September 2004 Thesis title: Computational Models of Cardiovascular Response to Orthostatic Stress Yale University, New Haven, CT, USA 1995 { 1997 M.S. Physics, June 1997 M.Phil. Physics, December 1998 Johannes Gutenberg-Universit¨at,Mainz, Germany 1994 { 1995 First-year medical studies Johannes Gutenberg-Universit¨at,Mainz, Germany 1992 { 1995 Diplomvorpr¨ufungPhysics, June 1994 Passed Diplomvorpr¨ufungin Physics with honors Experience Associate Professor of Electrical and Biomedical Engineering 07/2017 { present Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science W.M. Keck Career Development Professor in Biomedical Engineering 07/2016 { present Massachusetts Institute of Technology Member of the Affiliate Faculty of Health Sciences & Technology 05/2014 { present Harvard Medical School Core Faculty Member 07/2013 { present Massachusetts Institute of Technology Institute for Medical Engineering and Science Principal Investigator 07/2013 { present Massachusetts Institute of Technology Research Laboratory of Electronics Hermann L.F. von Helmholtz Career Development Professor 07/2013 { 06/2016 Massachusetts Institute of Technology Institute for Medical Engineering and Science Assistant Professor of Electrical and Biomedical Engineering 07/2013 { 06/2017 Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science Principal Research Scientist 07/2012 { 07/2013 Massachusetts Institute of Technology Computational Physiology and Clinical Inference Group Thomas Heldt, Ph.D. -
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Introduction to the Literature on Programming Language Design Gary T
Computer Science Technical Reports Computer Science 7-1999 Introduction to the Literature On Programming Language Design Gary T. Leavens Iowa State University Follow this and additional works at: http://lib.dr.iastate.edu/cs_techreports Part of the Programming Languages and Compilers Commons Recommended Citation Leavens, Gary T., "Introduction to the Literature On Programming Language Design" (1999). Computer Science Technical Reports. 59. http://lib.dr.iastate.edu/cs_techreports/59 This Article is brought to you for free and open access by the Computer Science at Iowa State University Digital Repository. It has been accepted for inclusion in Computer Science Technical Reports by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Introduction to the Literature On Programming Language Design Abstract This is an introduction to the literature on programming language design and related topics. It is intended to cite the most important work, and to provide a place for students to start a literature search. Keywords programming languages, semantics, type systems, polymorphism, type theory, data abstraction, functional programming, object-oriented programming, logic programming, declarative programming, parallel and distributed programming languages Disciplines Programming Languages and Compilers This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/cs_techreports/59 Intro duction to the Literature On Programming Language Design Gary T. Leavens TR 93-01c Jan. 1993, revised Jan. 1994, Feb. 1996, and July 1999 Keywords: programming languages, semantics, typ e systems, p olymorphism, typ e theory, data abstrac- tion, functional programming, ob ject-oriented programming, logic programming, declarative programming, parallel and distributed programming languages.