DOCSLIB.ORG

IEEE P7003TM Standard for Algorithmic Bias Considerations Work in Progress Paper

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
IEEE P7003TM Standard for Algorithmic Bias Considerations Work in Progress Paper 2018 ACM/IEEE International Workshop on Software Fairness IEEE P7003TM Standard for Algorithmic Bias Considerations Work in progress paper Ansgar Koene Liz Dowthwaite Suchana Seth Chair of IEEE P7003 working group IEEE P7003 working group secretary IEEE P7003 working group member Horizon Digital Economy Research Horizon Digital Economy Research Berkman Klein Center for Internet & institute, University of Nottingham institute, University of Nottingham Society, Harvard University NG7 2TU NG7 2TU MA 02138 United Kingdom United Kingdom USA [email protected] [email protected] [email protected] ABSTRACT* SA) launched the IEEE Global Initiative on Ethics for Autonomous and Intelligence Systems [1] in April 2016. The The IEEE P7003 Standard for Algorithmic Bias Considerations is ‘Global Initiative’ aims to provide “an incubation space for new one of eleven IEEE ethics related standards currently under standards and solutions, certifications and codes of conduct, and development as part of the IEEE Global Initiative on Ethics of consensus building for ethical implementation of intelligent Autonomous and Intelligent Systems. The purpose of the IEEE technologies”. As of early 2018 the main pillars of the Global P7003 standard is to provide individuals or organizations creating Initiative are: algorithmic systems with development framework to avoid a public discussion document “Ethically Aligned Design: A unintended, unjustified and inappropriately differential outcomes vision for Prioritizing human Well-being with Autonomous for users. In this paper, we present the scope and structure of the and Intelligent Systems” [2], on establishing ethical and IEEE P7003 draft standard, and the methodology of the social implementations for intelligent and autonomous development process. systems and technology aligned with values and ethical CCS CONCEPTS principles that prioritize human well-being in a given cultural context; • General and reference → Document types → Computing a set of eleven working groups to create the IEEE P70xx standards, RFCs and guidelines series ethics standards, and associated certification programs, for Intelligent and Autonomous systems. KEYWORDS Algorithmic Bias, Standards, work-in-progress, methods The IEEE P70xx series of ethics standards aims to translate the principles that are discussed in the Ethically Aligned Design document into actionable guidelines or frameworks that can be ACM Reference format: used as practical industry standards. The eleven IEEE P70xx A. Koene, L. Dowthwaite, and S. Seth. 2018. IEEE P7003 Standard for standards that are currently under development are: Algorithmic Bias Consideratoins. FairWare'18, May 29, 2018, • IEEE P7000: Model Process for Addressing Ethical Gothenburg, Sweden. 4 pages. https://doi.org/10.1145/3194770.3194773 Concerns During System Design • IEEE P7001: Transparency of Autonomous Systems 1 INTRODUCTION • IEEE P7002: Data Privacy Process • IEEE P7003: Algorithmic Bias Considerations In recognition of the increasingly pervasive role of algorithmic • IEEE P7004: Standard on Child and Student Data decision making systems in corporate and government service, Governance and growing public concerns regarding the ‘black box’ nature of • IEEE P7005: Standard on Employer Data Governance many of these systems, the IEEE Standards Association (IEEE- • IEEE P7006: Standard on Personal Data AI Agent Working Group * Permission to make digital or hard copies of all or part of this work for personal or • IEEE P7007: Ontological Standard for Ethically Driven classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full Robotics and Automation Systems citation on the first page. Copyrights for components of this work owned by others • IEEE P7008: Standard for Ethically Driven Nudging for than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior Robotic, Intelligent and Autonomous Systems specific permission and/or a fee. Request permissions from [email protected]. • IEEE P7009: Standard for Fail-Safe Design of Autonomous FairWare'18, May 29, 2018, Gothenburg, Sweden and Semi-Autonomous Systems © 2018 Copyright is held by the owner/author(s). Publication rights licensed to ACM. • IEEE P7010: Wellbeing Metrics Standard for Ethical ACM ISBN 978-1-4503-5746-3/18/05…$15.00 Artificial Intelligence and Autonomous Systems https://doi.org/10.1145/3194770.3194773 38 FairWare’18, May 2018, Gothenburg, Sweden A. Koene et al. A brief paper outlining the aims of IEEE P7003 and its Throughout the development process IEEE P7003 challenges relationship to the other IEEE P700x series standards working the developer to think explicitly about the criteria that are being groups was published in [3] and a tech-industry oriented summary used for the recommendation process and the rationale, i.e. of the eleven IEEE P70xx series standards appeared on the justification, for why these criteria are relevant and why they are technology-industry blog TechEmergence [4]. appropriate (legally and socially). Documenting these will help In this paper we present a more detailed overview of the the business respond to possible future challenges from scope, structure and development process of the IEEE P7003 customers, competitors or regulators regarding the Standard for Algorithmic Bias Considerations [5]. recommendations produced by this system. At the same time, this IEEE P7003 is aimed to be used by people/organizations who process of analysis will help the business to be aware of the are developing and/or deploying automated decision (support) context for which this recommendation system can confidently be systems (which may or may not involve AI/machine learning) that used, and which uses would require additional testing (e.g. age are part of products/services that affect people. Typical examples ranges of customers, types of products). would include anything related to personalization or individual assessment, including any system that performs a filtering 2 SCOPE function by selecting to prioritize the ease with which people will The IEEE P7003 standard will provide a framework, which find some items over others (e.g. search engines or helps developers of algorithmic systems and those responsible for recommendation systems). Any system that will produce different their deployment to identify and mitigate unintended, unjustified results for some people than for others is open to challenges of and/or inappropriate biases in the outcomes of the algorithmic being biased. Examples could include: system. Algorithmic systems in this context refers to the Security camera applications that detect theft or suspicious combination of algorithms, data and the output deployment behaviour. process that together determine the outcomes that affect end users. Marketing automation applications that calibrate offers, Unjustified bias refers to differential treatment of individuals prices, or content to an individual’s preferences and based on criteria for which no operational justification is given. behaviour. Inappropriate bias refers to bias that is legally or morally etc… unacceptable within the social context where the system is used, e.g. algorithmic systems that produce outcomes with differential The requirements specification provided by the IEEE P7003 impact strongly correlated with protected characteristics (such as standard will allow creators to communicate to users, and race, gender, sexuality, etc). regulatory authorities, that up-to-date best practices were used in The standard will describe specific methodologies that allow the design, testing and evaluation of the algorithm to attempt to users of the standard to assert how they worked to address and avoid unintended, unjustified and inappropriate differential impact eliminate issues of unintended, unjustified and inappropriate bias on users. in the creation of their algorithmic system. This will help to Since the standard aims to allow for the legitimate ends of design systems that are more easily auditable by external parties different users, such as businesses, it should assist them in (such as regulatory bodies). assuring citizens that steps have been taken to ensure fairness, as appropriate to the stated aims and practices of the sector where the Elements include: algorithmic system is applied. For example, it may help customers a set of guidelines for what to do when designing or using of insurance companies to feel more assured that they are not such algorithmic systems following a principled getting a worse deal because of the hidden operation of an methodology (process), engaging with stakeholders (people), algorithm. determining and justifying the objectives of using the As a practical example, an online retailer developing a new algorithm (purpose), and validating the principles that are product recommendation system might use the IEEE P7003 actually embedded in the algorithmic system (product); standard as follows: a practical guideline for developers to identify when they Early in the development cycle, after outlining the intended should step back to evaluate possible bias issues in their functions of the new system IEEE P7003 guides the developer systems, and pointing to methods they can use to do this; through a process of considering the likely customer groups, in benchmarking procedures and criteria
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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 .........................................................................................................................
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • Combating Bias in Artificial Intelligence and Machine Learning
    Combating Bias in Artificial Intelligence and Machine Learning Used in Healthcare SEPTEMBER 2020 CHARLYN HO MARC MARTIN SARI RATICAN DIVYA TANEJA D. SEAN WEST COUNSEL & LEAD AUTHOR PARTNER SENIOR COUNSEL ASSOCIATE COUNSEL +1.202.654.6341 +1.202.654.6351 +1.310.788.3287 +1.332.223.3935 +1.206.359.3598 [email protected] [email protected] [email protected] [email protected] [email protected] Some jurisdictions in which Perkins Coie LLP practices law may require that this PerkinsCoie.com/AI communication be designated as Advertising Materials. | Perkins Coie LLP CombatingCombating Bias Bias in inArtificial Artificial Intelligence Intelligence and andMachine Machine Learning Learning Used Usedin Healthcare in Healthcare Artificial intelligence (AI) has the promise to revolutionize healthcare through the use of machine learning (ML) techniques to predict patient outcomes and personalize patient care, but this use of AI carries the risk of what is described as “algorithmic bias” affecting such outcomes and care, even unintentionally. AI seeks to enable computers to imitate intelligent human behavior, and ML, a subset of AI, involves systems that learn from data without relying on rules-based programming. AI has the power to unleash insights from large data sets to more accurately diagnose and evaluate patients and dramatically speed up the drug discovery process. However, despite its promise, the use of AI is not without legal risks that can generate significant liability for the developer and/or user of ML algorithms. Among other risks, the development and use of ML algorithms could result in discrimination against individuals on the basis of a protected class, a potential violation of antidiscrimination and other laws.
    [Show full text]
  • A Proposal for Identifying and Managing Bias in Artificial Intelligence, Has Been 203 Developed to Advance Methods to Understand and Reduce Harmful Forms of AI Bias
    1 Draft NIST Special Publication 1270 2 3 4 A Proposal for Identifying and 5 Managing Bias in Artificial 6 Intelligence 7 8 9 Reva Schwartz 10 Leann Down 11 Adam Jonas 12 Elham Tabassi 13 14 This draft publication is available free of charge from: 15 https://doi.org/10.6028/NIST.SP.1270-draft 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Draft NIST Special Publication 1270 30 31 32 A Proposal for Identifying and 33 Managing Bias within Artificial 34 Intelligence 35 36 Reva Schwartz 37 National Institute of Standards and Technology 38 Information Technology Laboratory 39 40 Leann Down 41 Adam Jonas 42 Parenthetic, LLC 43 44 Elham Tabassi 45 National Institute of Standards and Technology 46 Information Technology Laboratory 47 48 49 This draft publication is available free of charge from: 50 https://doi.org/10.6028/NIST.SP.1270-draft 51 52 53 June 2021 54 55 56 57 58 59 U.S. Department of Commerce 60 Gina M. Raimondo, Secretary 61 62 National Institute of Standards and Technology 63 James K. Olthoff, Performing the Non-Exclusive Functions and Duties of the Under Secretary of Commerce 64 for Standards and Technology & Director, National Institute of Standards and Technology 65 66 Certain commercial entities, equipment, or materials may be identified in this 67 document in order to describe an experimental procedure or concept adequately. 68 Such identification is not intended to imply recommendation or endorsement by the 69 National Institute of Standards and Technology, nor is it intended to imply that the 70 entities, materials, or equipment are necessarily the best available for the purpose.
    [Show full text]
  • Bias in Data-Driven AI Systems - an Introductory Survey
    Bias in Data-driven AI Systems - An Introductory Survey Eirini Ntoutsi,a Pavlos Fafalios,b Ujwal Gadiraju,a Vasileios Iosifidis,a Wolfgang Nejdl,a Maria-Esther Vidal,c Salvatore Ruggieri,d Franco Turini,d Symeon Papadopoulos,e Emmanouil Krasanakis,e Ioannis Kompatsiaris,e Katharina Kinder-Kurlanda,f Claudia Wagner,f Fariba Karimi,f Miriam Fernandez,g Harith Alani,g Bettina Berendt,h Tina Kruegel,i Christian Heinze,i Klaus Broelemann,j Gjergji Kasneci,j Thanassis Tiropanis,k Steffen Staaba;k;l Article Type: Overview Abstract AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed arXiv:2001.09762v1 [cs.CY] 14 Jan 2020 aL3S Research Center & Leibniz University Hannover, Germany - [email protected] bInstitute of Computer Science, FORTH-ICS, Greece cTIB Leibniz Information Centre For Science and Tecnhnology, Hannover, Germany dKDDLAB, Dipartimento di Informatica, Universit`adi Pisa, Italy eInformation Technologies Institute, CERTH, Thessaloniki, Greece fGESIS Leibniz Institute for the Social Sciences, Cologne, German gKnowledge Media Institute, The Open University, Milton Keynes, UK hFaculty of Electrical Engineering and Computer Science, TU Berlin, Germany, and Department of Computer Science, KU Leuven, Belgium iInstitute for Legal Informatics, Leibniz University of Hanover, Germany jInnovation Lab, SCHUFA Holding AG, Wiesbaden, Germany kElectronics and Computer Science, University of Southampton, UK lIPVS, Universit¨atStuttgart, Germany 1 ethical and legal principles in their design, training and deployment to ensure social good while still benefiting from the huge potential of the AI technology.
    [Show full text]
  • Addressing the Problem of Algorithmic Bias Technical Paper
    Using artificial intelligence to make decisions: Addressing the problem of algorithmic bias Technical Paper © Australian Human Rights Commission 2020. This Paper incorporates material from Gradient Institute, Consumer Policy Research Centre, CHOICE and CSIRO’s Data61. Infographics and charts by Gradient Institute reproduced with permission. The Commission encourages the dissemination and exchange of information presented in this publication and endorses the use of the Australian Governments Open Access and Licensing Framework (AusGOAL). All material presented in this publication is licensed under the Creative Commons Attribution 4.0 International Licence, with the exception of: • photographs and images • organisational logos, any branding or trademarks • where otherwise indicated. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/legalcode. In essence, you are free to copy, communicate and adapt the publication, as long as you attribute the Australian Human Rights Commission and abide by the other licence terms. Please give attribution to: © Australian Human Rights Commission • Using artificial intelligence to make decisions: Addressing the problem of algorithmic bias • Technical Paper • 2020 ISBN: 978-1-925917-27-7 For further information about copyright in this publication, please contact: Communications Unit Australian Human Rights Commission GPO Box 5218 SYDNEY NSW 2001 Telephone: (02) 9284 9600 TTY: 1800 620 241 Email: [email protected]. Design and layout: Herbert Smith Freehills Cover image: iStock images Internal photography: iStock images Authors: Finn Lattimore, Simon O’Callaghan, Zoe Paleologos, Alistair Reid, Edward Santow, Holli Sargeant and Andrew Thomsen. Acknowledgements • Herbert Smith Freehills for the design and publication of this Paper. • Prof Rosalind Croucher, President, Australian Human Rights Commission.
    [Show full text]
  • Artificial Intelligence, Algorithmic Bias and Disparate Impact Liability in Hiring Practices Mckenzie Raub University of Arkansas, Fayetteville
    Arkansas Law Review Volume 71 | Number 2 Article 7 December 2018 Bots, Bias and Big Data: Artificial Intelligence, Algorithmic Bias and Disparate Impact Liability in Hiring Practices McKenzie Raub University of Arkansas, Fayetteville Follow this and additional works at: https://scholarworks.uark.edu/alr Part of the Artificial Intelligence and Robotics Commons, and the Science and Technology Law Commons Recommended Citation McKenzie Raub, Bots, Bias and Big Data: Artificial Intelligence, Algorithmic Bias and Disparate Impact Liability in Hiring Practices, 71 Ark. L. Rev. 529 (2018). Available at: https://scholarworks.uark.edu/alr/vol71/iss2/7 This Article is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Arkansas Law Review by an authorized editor of ScholarWorks@UARK. For more information, please contact [email protected], [email protected]. BOTS, BIAS AND BIG DATA: ARTIFICIAL INTELLIGENCE, ALGORITHMIC BIAS AND DISPARATE IMPACT LIABILITY IN HIRING PRACTICES* I. INTRODUCTION “With artificial intelligence, we are summoning the demon. You know all those stories where there’s the guy with the pentagram and the holy water and he’s like, yeah, he’s sure he can control the demon? Doesn’t work out.”1 While this is perhaps dramatic, many Americans share Elon Musk’s underlying anxieties about artificial intelligence’s increasing proliferation into everyday life.2 However, few realize the depth of artificial intelligence’s involvement in mundane daily activities.3 Fewer than half of Americans are aware of the existence of “computer programs that can review job * The author sincerely thanks Danielle Weatherby, Associate Professor of Law, University of Arkansas School of Law, for her thoughtful guidance throughout the drafting and editing this comment.
    [Show full text]