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

Giovanola, Benedetta; Tiribelli, Simona

doi:10.1007/s10676-022-09622-5

Cited by 15 publications

(3 citation statements)

References 68 publications

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“…This technical pathway to achieve algorithmic fairness via the development of non-biased ML models also emerges in the wider and more general debate on the ethics of algorithmic decision-making and ML(Barocas 2014;Shah 2018;Diakopoulos and Koliska 2017;Giovanola and Tiribelli 2022).…”

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confidence: 99%

Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms

Giovanola

Tiribelli

2022

AI & Soc

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The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society.

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confidence: 99%

Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms

Giovanola

Tiribelli

2022

AI & Soc

Self Cite

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“…Autonomy runs deeply in moral philosophy as any other ethical principle (Beauchamp, 2019 ). Therefore, conceptualize it properly, that is, as an ethical principle, asks to rely on moral philosophy (Bietti, 2020 ; Giovanola & Tiribelli, 2022a , 2022 ). The latter indeed helps us to shed light on its multidimensionality and bridge the gap between high-level theory and practical feasibility, namely, to prevent ethics principles from being misunderstood or used as mere labels and therefore becoming trivial, useless, and toothless (Munn, 2022 ; Rességuier & Rodrigues, 2020 ).…”

Section: Rethinking Hrs Challenges Based On the Ethics Principle Of A...mentioning

confidence: 99%

Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical Analysis

Tiribelli,

Calvaresi

2024

Sci Eng Ethics

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Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS’ technical aspects allows us to shed light on the ethical risks and challenges they might raise on individuals’ well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA.

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“…In regulation, it is important to identify and correct unintentional discrimination in a timely manner in order to build more inclusive and fair computer systems ( Martin and Waldman, 2023 ). By continually improving algorithmic review and regulatory processes, we can better address the algorithmic challenges of modern society and promote social equity and justice ( Giovanola and Tiribelli, 2022 ).…”

Section: Judicial Review Of Algorithmic Discriminationmentioning

confidence: 99%

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices

Wang,

Wu,

et al. 2024

Front. Artif. Intell.

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IntroductionAlgorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions.MethodsThe methodology includes a systematic literature review, analysis of legal documents, and comparative case studies across different geographic regions and sectors. This multifaceted approach allows for a thorough exploration of the complexity of algorithmic bias and its regulation.ResultsWe identify five primary types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The analysis of the U.S. legal and regulatory framework reveals a landscape of principled regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. A comparative perspective is also provided by examining the status of algorithmic fairness in the EU, Canada, Australia, and Asia.ConclusionReal-world impacts are demonstrated through case studies focusing on criminal risk assessments and hiring algorithms, illustrating the tangible effects of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies.

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

Cited by 15 publications

References 68 publications

Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms

Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms

Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical Analysis

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices

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