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  1. Reconstructing AI Ethics Principles: Rawlsian Ethics of Artificial Intelligence.Salla Westerstrand - 2024 - Science and Engineering Ethics 30 (5):1-21.
    The popularisation of Artificial Intelligence (AI) technologies has sparked discussion about their ethical implications. This development has forced governmental organisations, NGOs, and private companies to react and draft ethics guidelines for future development of ethical AI systems. Whereas many ethics guidelines address values familiar to ethicists, they seem to lack in ethical justifications. Furthermore, most tend to neglect the impact of AI on democracy, governance, and public deliberation. Existing research suggest, however, that AI can threaten key elements of western democracies (...)
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  • On prediction-modelers and decision-makers: why fairness requires more than a fair prediction model.Teresa Scantamburlo, Joachim Baumann & Christoph Heitz - forthcoming - AI and Society:1-17.
    An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called (...)
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  • On Hedden's proof that machine learning fairness metrics are flawed.Anders Søgaard, Klemens Kappel & Thor Grünbaum - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    1. Fairness is about the just distribution of society's resources, and in ML, the main resource being distributed is model performance, e.g. the translation quality produced by machine translation...
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  • Fair equality of chances for prediction-based decisions.Michele Loi, Anders Herlitz & Hoda Heidari - 2024 - Economics and Philosophy 40 (3):557-580.
    This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently specified (...)
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  • Algorithmic Fairness, Risk, and the Dominant Protective Agency.Ulrik Franke - 2023 - Philosophy and Technology 36 (4):1-7.
    With increasing use of automated algorithmic decision-making, issues of algorithmic fairness have attracted much attention lately. In this growing literature, existing concepts from ethics and political philosophy are often applied to new contexts. The reverse—that novel insights from the algorithmic fairness literature are fed back into ethics and political philosophy—is far less established. However, this short commentary on Baumann and Loi (Philosophy & Technology, 36(3), 45 2023) aims to do precisely this. Baumann and Loi argue that among algorithmic group fairness (...)
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