AI and Ethics 3 (2021)

Authors
Luciano Floridi
Oxford University
Michelle Lee
University of Canterbury
Abstract
There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values.
Keywords Machine Learning  Race  Gender  Ethics  Key Ethics Indicators (KEIs)
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References found in this work BETA

Anarchy, State, and Utopia.Robert Nozick - 1974 - New York: Basic Books.
Justice as Fairness: A Restatement.John Rawls (ed.) - 2001 - Harvard University Press.
Morals by Agreement.David P. Gauthier - 1986 - Oxford University Press.
The Metaphysics of Morals.Immanuel Kant - 1797/1996 - Cambridge University Press.

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Citations of this work BETA

Rawls’s Original Position and Algorithmic Fairness.Ulrik Franke - 2021 - Philosophy and Technology 34 (4):1803-1817.

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