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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Schoeffer, Jakob Karlsruhe Inst Technol KIT Karlsruhe Germany

ISBN: (纸本)9781450391566

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield unfair outcomes because their sanity is challenging to assess and calibrate in the first place-which is particularly worrisome for human decision-subjects. Based on this observation and building upon existing work, I aim to make the following three main contributions through my doctoral thesis: (a) understand how (potential) decision-subjects perceive algorithmic decisions (with varying degrees of transparency of the underlying ADS), as compared to similar decisions made by humans;(b) evaluate different tools for transparent decision-making with respect to their effectiveness in enabling people to appropriately assess the quality and fairness of ADS;and (c) develop human-understandable technical artifacts for fair automated decision-making. Over the course of the first half of my PhD program, I have already addressed substantial pieces of (a) and (c), whereas (b) will be the major focus of the second half.

关键词： algorithmic decision-making explanations fairness

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Fair, Transparent, and Accountable algorithmic decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges

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Philosophy and Technology 2018年第4期31卷 611-627页

作者： Lepri, Bruno Oliver, Nuria Letouzé, Emmanuel Pentland, Alex Vinck, Patrick Fondazione Bruno Kessler via Sommarive 18 Trento Italy Vodafone Research London United Kingdom Data-Pop Alliance New York NY United States MIT Media Lab Cambridge MA United States Harvard Humanitarian Initiative Cambridge MA United States

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. © 2017, Springer Science+Business Media B.V.

关键词： Accountability algorithmic decision-making algorithmic transparency Fairness Social good

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How to regulate algorithmic decision-making: A framework of regulatory requirements for different applications

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REGULATION & GOVERNANCE 2022年第1期16卷 119-136页

作者： Krafft, Tobias D. Zweig, Katharina A. Koenig, Pascal D. Univ Kaiserslautern Algorithm Accountabil Lab Dept Comp Sci Kaiserslautern Germany Univ Kaiserslautern Dept Social Sci Kaiserslautern Germany

algorithmic decision-making (ADM) systems have come to support, pre-empt or substitute for human decisions in manifold areas, with potentially significant impacts on individuals' lives. Achieving transparency and accountability has been formulated as a general goal regarding the use of these systems. However, concrete applications differ widely in the degree of risk and the accountability problems they entail for data subjects. The present paper addresses this variation and presents a framework that differentiates regulatory requirements for a range of ADM system uses. It draws on agency theory to conceptualize accountability challenges from the point of view of data subjects with the purpose to systematize instruments for safeguarding algorithmic accountability. The paper furthermore shows how such instruments can be matched to applications of ADM based on a risk matrix. The resulting comprehensive framework can guide the evaluation of ADM systems and the choice of suitable regulatory provisions.

关键词： accountability agency theory algorithmic decision-making risk matrix transparency

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Disentangling Fairness Perceptions in algorithmic decision-making: the Effects of Explanations, Human Oversight, and Contestability 23

Disentangling Fairness Perceptions in Algorithmic Decision-M...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Yurrita, Mireia Draws, Tim Balayn, Agathe Murray-Rust, Dave Tintarev, Nava Bozzon, Alessandro Delft Univ Technol Delft Netherlands Maastricht Univ Maastricht Netherlands

ISBN: (纸本)9781450394215

Recent research claims that information cues and system attributes of algorithmic decision-making processes affect decision subjects' fairness perceptions. However, little is still known about how these factors interact. This paper presents a user study (N = 267) investigating the individual and combined effects of explanations, human oversight, and contestability on informational and procedural fairness perceptions for high- and low-stakes decisions in a loan approval scenario. We find that explanations and contestability contribute to informational and procedural fairness perceptions, respectively, but we find no evidence for an effect of human oversight. Our results further show that both informational and procedural fairness perceptions contribute positively to overall fairness perceptions but we do not find an interaction effect between them. A qualitative analysis exposes tensions between information overload and understanding, human involvement and timely decision-making, and accounting for personal circumstances while maintaining procedural consistency. Our results have important design implications for algorithmic decision-making processes that meet decision subjects' standards of justice.

关键词： explanations human oversight contestability fairness perceptions algorithmic decision-making

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Factors Influencing Perceived Fairness in algorithmic decision-making: Algorithm Outcomes, Development Procedures, and Individual Differences 20

Factors Influencing Perceived Fairness in Algorithmic Decisi...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Wang, Ruotong Harper, F. Maxwell Zhu, Haiyi Carnegie Mellon Univ Pittsburgh PA 15213 USA Amazon Seattle WA USA

ISBN: (纸本)9781450367080

algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of "outcome favorability" bias.

关键词： perceived fairness algorithmic decision-making algorithm outcome algorithm development

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Paths to Digital Justice: Judicial Robots, algorithmic decision-making, and Due Process

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ASIAN JOURNAL OF LAW AND SOCIETY 2020年第3期7卷 453-469页

作者： Rubim Borges Fortes, Pedro Univ Fed Rio de Janeiro Rio De Janeiro Brazil Univ Fed Rio de Janeiro Natl Law Sch Doctoral Programme Rio De Janeiro Brazil Attorney Gen Off Rio de Janeiro Rio De Janeiro Brazil

The paths to digital justice focus on the challenges of contemporary digital societies in reaching automated decision-making processes through software, algorithms, and information technology without loss of its human quality and the guarantees of due process. In this context, this article reflects on the possibilities of establishing judicial robots in substitution for human judges, by examining whether artificial intelligence and algorithms may support judicial decision-making independently and without human supervision. The point of departure for this analysis comes from the experience of criminal justice systems with software for judgment of the possibility of recidivism of criminal defendants. algorithmic decision-making may improve the public good in support of judicial decision-making, but the analysis of current technology and our standards for due process of law recommends caution on the conclusion that robots may replace human judges and satisfy our expectations for explainability and fairness in adjudication.

关键词： digital justice judicial robots algorithmic decision-making COMPAS due process explainability

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Understanding Contestability on the Margins: Implications for the Design of algorithmic decision-making in Public Services 24

Understanding Contestability on the Margins: Implications fo...

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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Karusala, Naveena Upadhyay, Sohini Veeraraghavan, Rajesh Gajos, Krzysztof Harvard Univ Allston MA 02138 USA Georgetown Univ Washington DC USA

ISBN: (纸本)9798400703300

Policymakers have established that the ability to contest decisions made by or with algorithms is core to responsible artificial intelligence (AI). However, there has been a disconnect between research on contestability of algorithms, and what the situated practice of contestation looks like in contexts across the world, especially amongst communities on the margins. We address this gap through a qualitative study of follow-up and contestation in accessing public services for land ownership in rural India and afordable housing in the urban United States. We find there are significant barriers to exercising rights and contesting decisions, which intermediaries like NGO workers or lawyers work with communities to address. We draw on the notion of accompaniment in global health to highlight the open-ended work required to support people in navigating violent social systems. We discuss the implications of our findings for key aspects of contestability, including building capacity for contestation, human review, and the role of explanations. We also discuss how sociotechnical systems of algorithmic decision-making can embody accompaniment by taking on a higher burden of preventing denials and enabling contestation.

关键词： algorithmic decision-making public services contestability India United States

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From the Vantage Point of Vulnerability Theory: algorithmic decision-making and Access to the European Court of Human Rights

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NORDIC JOURNAL OF HUMAN RIGHTS 2022年第1期40卷 235-249页

作者： Godzimirska, Zuzanna Kucuksu, Aysel Ravn, Salome Addo Univ Copenhagen Fac Law Ctr Excellence Int Courts iCourts Copenhagen Denmark

The past two decades at the European Court of Human Rights have been marked by various efforts to reduce its backlog of cases through changing the substantive, procedural, and formal practices surrounding access to the Court. Proposals aimed at facilitating these efforts have also rested on the unarticulated premise that solving the ECtHR's backlog problem necessarily involves an eitheror choice between improving the Court's efficiency and shrinking individual access to it. This article departs from that premise. Drawing on Martha Fineman's 'theory of vulnerability' and her vision for social justice, the article lays out a proposal that allows for the coexistence of efficiency and individual access through a hybrid decision-making (HDM) model. First, we show that from a vulnerability theory perspective, better access to human rights courts is a key component of a just human rights system. Second, we argue that in order to be just, procedures need to be context-sensitive and adopted in ways that acknowledge humans' inherent vulnerability. To support the argument, we draw inspiration from the African Court on Human and Peoples' Rights, whose current practices help illustrate the point that more equitable access to justice need not be a relic of the past.

关键词： vulnerability theory European Court of Human Rights African Court of Human and Peoples' Rights algorithmic decision-making hybrid decision-making justice

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Preferred Explanations of algorithmic decision-making Systems in the Dutch Property & Casualty Insurance Industry

Preferred Explanations of Algorithmic Decision-Making System...

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作者： Schotman, E.H.H. Open Universiteit Nederland

学位级别：硕士

There are multiple challenges relating to the explainability of algorithmic decision-making (ADM) systems, such as that there is not a clear consensus on what a ‘good’ explanation is and what sort of explanation fit the different types of ADM-systems. Therefore, in this research, the Delphi-method was used to study which explanation would fit the different types of ADM-systems best. This was done in the Dutch property & casualty insurance industry. This report will show that often the data that is used to reach a decision should be included in the explanation because of the privacy perspective. Also, for most ADM-systems in this study, can be said that the general idea behind the algorithm and/or data should be included in the explanation. The main conclusion, however, is that there is not a one-size-fits-all explanation for ADM-systems and that it depends on the type of ADM-system, for what it is used and in which social context.

关键词： algorithmic decision-making explainability insurance Delphi-method

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algorithmic decision-making in social work practice and pedagogy: confronting the competency/critique dilemma

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SOCIAL WORK EDUCATION 2024年第6期43卷 1552-1569页

作者： James, Paul Lal, Jason Liao, Ashley Magee, Liam Soldatic, Karen Univ Western Sydney Inst Culture & Soc Sydney Australia

The world is experiencing an accelerating digital transformation. One aspect of this is the implementation of algorithmic decision-making, often supported by Artificial Intelligence. algorithmic systems have the potential to change the meaning of critical elements of the engagement between social workers and people who need support. At the same time, social workers are increasingly being required by their agencies to integrate such abstracting technologies and techniques evermore comprehensively into their professional practice. This integration also creates tensions for training future social workers. It requires on the one hand directly responding to an old but intensifying tension between training students technically, and, on the other, providing the pedagogical tools for finding some critical distance from those same technologies and techniques. The article works through this tension of technique/critique, and a second associated tension between deploying technologies of mediated engagement and being present 'in the room' (presence/mediation). The consequences of how both tensions are handled, we suggest, are being intensified through the technologization of service-delivery. The article suggests ways of approaching a technical-critical pedagogy that responds to this complexity by being practice-embedded and theoretically engaged.

关键词： Social work pedagogy algorithmic decision-making agency curriculum relational engagement competency critique

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