The paper focuses on one of the most urgent risks of artificial intelligence, and more specifically of algorithmic decision-making (ADM), that is, the risk of being unfair. In the first section we provide an overview of the discus- sion on fairness in ADM and show its shortcomings; in the second section we pursue an ethical inquiry into the concept of fairness, and identify its main dimensions and components, drawing insight from a renewed reflection on respect, which goes beyond the idea of equal respect to include respect for particular individuals too. In the third section we show how our conceptual re-elaboration of fairness can help identify the criteria that ought to steer the ethical design of ADM-based systems to make them really fair.

Equità e decisioni algoritmiche

B. Giovanola;S. Tiribelli
2022-01-01

Abstract

The paper focuses on one of the most urgent risks of artificial intelligence, and more specifically of algorithmic decision-making (ADM), that is, the risk of being unfair. In the first section we provide an overview of the discus- sion on fairness in ADM and show its shortcomings; in the second section we pursue an ethical inquiry into the concept of fairness, and identify its main dimensions and components, drawing insight from a renewed reflection on respect, which goes beyond the idea of equal respect to include respect for particular individuals too. In the third section we show how our conceptual re-elaboration of fairness can help identify the criteria that ought to steer the ethical design of ADM-based systems to make them really fair.
2022
ETS
Internazionale
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11393/306430
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