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The value of responsibility gaps in algorithmic decision-making

ETHICS AND INFORMATION TECHNOLOGY 2023年第1期25卷 1-11页

作者： Munch, Lauritz Mainz, Jakob Bjerring, Jens Christian Aarhus Univ Aarhus Denmark

Many seem to think that AI-induced responsibility gaps are morally bad and therefore ought to be avoided. We argue, by contrast, that there is at least a pro tanto reason to welcome responsibility gaps. The central reason is that it can be bad for people to be responsible for wrongdoing. This, we argue, gives us one reason to prefer automated decision-making over human decision-making, especially in contexts where the risks of wrongdoing are high. While we are not the first to suggest that responsibility gaps should sometimes be welcomed, our argument is novel. Others have argued that responsibility gaps should sometimes be welcomed because they can reduce or eliminate the psychological burdens caused by tragic moral choice-situations. By contrast, our argument explains why responsibility gaps should sometimes be welcomed even in the absence of tragic moral choice-situations, and even in the absence of psychological burdens.

关键词： Responsibility Gaps algorithmic decision-making Artificial intelligence

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Exploring the roles of trust and social group preference on the legitimacy of algorithmic decision-making vs. human decision-making for allocating COVID-19 vaccinations

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AI & SOCIETY 2024年第1期39卷 309-327页

作者： Lunich, Marco Kieslich, Kimon Heinrich Heine Univ Dusseldorf Dept Social Sci Dusseldorf Germany

In combating the ongoing global health threat of the COVID-19 pandemic, decision-makers have to take actions based on a multitude of relevant health data with severe potential consequences for the affected patients. Because of their presumed advantages in handling and analyzing vast amounts of data, computer systems of algorithmic decision-making (ADM) are implemented and substitute humans in decision-making processes. In this study, we focus on a specific application of ADM in contrast to human decision-making (HDM), namely the allocation of COVID-19 vaccines to the public. In particular, we elaborate on the role of trust and social group preference on the legitimacy of vaccine allocation. We conducted a survey with a 2 x 2 randomized factorial design among n = 1602 German respondents, in which we utilized distinct decision-making agents (HDM vs. ADM) and prioritization of a specific social group (teachers vs. prisoners) as design factors. Our findings show that general trust in ADM systems and preference for vaccination of a specific social group influence the legitimacy of vaccine allocation. However, contrary to our expectations, trust in the agent making the decision did not moderate the link between social group preference and legitimacy. Moreover, the effect was also not moderated by the type of decision-maker (human vs. algorithm). We conclude that trustworthy ADM systems must not necessarily lead to the legitimacy of ADM systems.

关键词： algorithmic decision-making Trust Social group preference COVID-19 decision legitimacy Public opinion

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Weapons of moral construction? On the value of fairness in algorithmic decision-making

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ETHICS AND INFORMATION TECHNOLOGY 2022年第1期24卷 1-13页

作者： Giovanola, Benedetta Tiribelli, Simona Univ Macerata Dept Polit Sci Commun & Int Relat I-62100 Macerata Italy Tufts Univ Dept Philosophy 222 Miner Hall Medford MA 02155 USA PathCheck Fdn Inst Technol & Global Hlth 955 Massachusetts Ave Cambridge MA 02139 USA

Fairness is one of the most prominent values in the Ethics and Artificial Intelligence (AI) debate and, specifically, in the discussion on algorithmic decision-making (ADM). However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed at clarifying the aim and structure of the paper, in section "Fairness in algorithmic decision-making" we provide an overview of the state of the art of the discussion on fairness in ADM and show its shortcomings;in section "Fairness as an ethical value", we pursue an ethical inquiry into the concept of fairness, drawing insights from accounts of fairness developed in moral philosophy, and define fairness as an ethical value. In particular, we argue that fairness is articulated in a distributive and socio-relational dimension;it comprises three main components: fair equality of opportunity, equal right to justification, and fair equality of relationship;these components are grounded in the need to respect persons both as persons and as particular individuals. In section "Fairness in algorithmic decision-making revised", we analyze the implications of our redefinition of fairness as an ethical value on the discussion of fairness in ADM and show that each component of fairness has profound effects on the criteria that ADM ought to meet. Finally, in section "Concluding remarks", we sketch some broader implications and conclude.

关键词： Fairness algorithmic decision-making Machine learning Discrimination Respect Ethics of algorithms

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On the Advantages of Distinguishing Between Predictive and Allocative Fairness in algorithmic decision-making

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MINDS AND MACHINES 2022年第4期32卷 655-682页

作者： Beigang, Fabian London Sch Econ & Polit Sci Dept Philosophy Log & Sci Method Houghton St London WC2A 2AE England

The problem of algorithmic fairness is typically framed as the problem of finding a unique formal criterion that guarantees that a given algorithmic decision-making procedure is morally permissible. In this paper, I argue that this is conceptually misguided and that we should replace the problem with two sub-problems. If we examine how most state-of-the-art machine learning systems work, we notice that there are two distinct stages in the decision-making process. First, a prediction of a relevant property is made. Secondly, a decision is taken based (at least partly) on this prediction. These two stages have different aims: the prediction is aimed at accuracy, while the decision is aimed at allocating a given good in a way that maximizes some context-relative utility measure. Correspondingly, two different fairness issues can arise. First, predictions could be biased in discriminatory ways. This means that the predictions contain systematic errors for a specific group of individuals. Secondly, the system's decisions could result in an allocation of goods that is in tension with the principles of distributive justice. These two fairness issues are distinct problems that require different types of solutions. I here provide a formal framework to address both issues and argue that this way of conceptualizing them resolves some of the paradoxes present in the discussion of algorithmic fairness.

关键词： algorithmic fairness algorithmic decision-making Fair machine learning Bias Discrimination

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Implementing Artificial Intelligence Assessing the Cost and Benefits of algorithmic decision-making in Critical Care

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CRITICAL CARE CLINICS 2023年第4期39卷 783-793页

作者： Caruso, Pier Francesco Greco, Massimiliano Ebm, Claudia Angelotti, Giovanni Cecconi, Maurizio Humanitas Univ Dept Biomed Sci Via Rita Levi Montalcini 4 I-20072 Milan Italy IRCCS Humanitas Res Hosp Dept Anesthesiol & Intens Care Via Manzoni 56 I-20089 Milan Italy IRCCS Humanitas Res Hosp Artificial Intelligence Ctr Via Manzoni 56 I-20089 Milan Italy

This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers’ minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.

关键词： Artificial intelligence algorithmic decision-making Cost and benefit Critical care Intensive care unit

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On the Impact of Explanations on Understanding of algorithmic decision-making 23

On the Impact of Explanations on Understanding of Algorithmi...

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6th ACM Conference on Fairness, Accountability, and Transparency (FAccT)

作者： Schmude, Timothee Koesten, Laura Moeller, Torsten Tschiatschek, Sebastian Univ Vienna UniVie Doctoral Sch Comp Sci DoCS Vienna Fac Comp Sci Res Network Data Sci Vienna Austria Univ Vienna Res Grp Visualizat & Data Anal Vienna Fac Comp Sci Vienna Austria Univ Vienna Res Grp Visualizat & Data Anal Vienna Fac Comp Sci Res Network Data Sci Vienna Austria Univ Vienna Res Grp Data Min & Machine Learning Vienna Fac Comp Sci Res Network Data Sci Vienna Austria

ISBN: (纸本)9781450372527

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems' adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider existent theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including users and affected stakeholders. We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe [63]. Our findings indicate that the "six facets" framework is a promising approach to analyse participants' thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants' perceptions of algorithmic fairness, demonstrating the interdependence between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems.

关键词： XAI learning Sciences algorithmic decision-making algorithmic fairness qualitative methods

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Perspectives from Practice: algorithmic decision-making in Public Employment Services 21

Perspectives from Practice: Algorithmic Decision-Making in P...

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24th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

作者： Flugge, Asbjorn Ammitzboll Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9781450384797

Algorithms are increasingly being implemented into core welfare areas as Public Employment Services. These data-driven technologies are implemented with the ambition to support caseworkers' decision-making capabilities, by profiling unemployed individual's risk of long-term unemployment. The research outlined in this paper investigates how we can study opaque technologies as algorithms from the perspective of the users (caseworkers) and those categorized (unemployed individuals) by these systems. This is done by combining established methods within Computer-Supported Cooperative Work, including ethnographic fieldwork and Participatory Design methods. I present preliminary results focused on caseworker's perception of the value of AI in job placement, and find documentation plays a central role in collaboration in casework. With this research, I am to contribute to a deeper understanding of how the organization of work is impacted by data-driven technologies like AI and explore ways to include the voice of unemployed individuals in the development of digital public services.

关键词： algorithmic decision-making Casework Participatory Design Transparency Public Services

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algorithmic decision-making: The right to explanation and the significance of stakes

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BIG DATA & SOCIETY 2024年第1期11卷

作者： Munch, Lauritz Aastrup Bjerring, Jens Christian Mainz, Jakob Thrane Aarhus Univ Dept Philosophy & Hist Ideas Aarhus Denmark

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, "high stakes" decisions are often said to engender such a right to explanation whereas "low stakes" or "non-high" stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will call the "Simple Stakes Thesis." The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low-and hence does not engender a right to an explanation-such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized but it also shows that the stakes thesis is significantly harder to apply in practice.

关键词： algorithmic decision-making black box algorithms right to explanation stakes

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"The Algorithm Will See You Now": Exploring the Implications of algorithmic decision-making in Connected Health 13

"The Algorithm Will See You Now": Exploring the Implications...

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13th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Carroll, Noel Richardson, Ita Abbas, Raja Manzar Lero Dublin Ireland NUI Galway Business Informat Syst Galway Ireland Univ Limerick Dept Comp Sci & Informat Syst Limerick Ireland Univ Limerick ARC Ageing Res Ctr HRI Hlth Res Inst Limerick Ireland

ISBN: (纸本)9789897583988

Despite abundant literature theorizing on Connected Health innovations to support decision-making, the extant literature provides sparse coverage on users' awareness of algorithmic decision-making. As a result, little is known regarding the role of algorithmically generated insights which directly influence clinical decisions nor the consequences of distancing clinicians and patients from decision-making capabilities. Indeed, recent studies highlight the growing emphasis on algorithmic decision-making but there is a need to raise questions as to how this is impacting on the risk and quality of delivering care. In this article, a summary of key concerns from the literature is provided, and a discussion on the implications of algorithmic decision-making in Connected Health is presented. In addition, a research roadmap is presented to draw more research focus on the role of algorithmically generated insights in Connected Health.

关键词： Algorithm algorithmic decision-making Connected Health Artificial Intelligence

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Managing unintended consequences of algorithmic decision-making: The case of Robodebt

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Journal of Information Technology Teaching Cases 2024年第1期14卷 165-171页

作者： Rinta-Kahila, Tapani Someh, Ida Gillespie, Nicole Indulska, Marta Gregor, Shirley Business School The University of Queensland Saint Lucia QLD Australia ARC Industrial Transformation Training Centre for Information Resilience (CIRES) The University of Queensland Brisbane QLD Australia College of Business and Economics Australian National University Canberra ACT Australia

In 2016, the Australian welfare agency Centrelink implemented an information system to automate the identification and recollection of welfare overpayments. Such algorithmic decision-making systems are increasingly leveraged to improve the efficiency of public administration. However, Centrelink’s scheme went horribly wrong: the system, branded as “Robodebt” by the popular media, generated debt notices that were inaccurate and based on insufficient evidence. Numerous vulnerable citizens who received the debt notices suffered a great deal of distress. While public controversy ensued, the ruling government continued to defend the flawed system until a court decision ruled it unlawful in 2019. This teaching case challenges one to analyze what went wrong in the implementation and management of the Robodebt system through the lens of sociotechnical systems. © Association for Information Technology Trust 2023.

关键词： algorithmic decision-making automation Centrelink public agency Robodebt sociotechnical system

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