What's (Not) Ideal about Fair Machine Learning? Abstract

Tutkimustuotos: AbstraktiScientific

Abstrakti

Fair machine learning frameworks are normative models that specify and guide the implementation of non-discrimination principles in machine learning (ML) systems. The dominant methodological approach involves (i) defining a fairness metric, the maximum value of which constitutes a target, an end-state of "ideal fairness", and (ii) applying a "bias mitigation" method that improves the system against this metric. Recent works have charged severe critiques against existing proposals in fair ML, attributing many alleged shortcomings to the dominant "idealized" methodology therein. These charges echo critiques of so-called "ideal theory" in political philosophy. I review methodological critiques of fair machine learning and contextualize them against the background of the "ideal theory" debate, drawing lessons for "nonideal" approaches to fair machine learning.
AlkuperäiskieliEnglanti
Sivut911
Sivumäärä1
DOI - pysyväislinkit
TilaJulkaistu - 27 heinäk. 2022
OKM-julkaisutyyppiEi OKM-tyyppiä
TapahtumaAAAI/ACM Conference on Artificial Intelligence, Ethics, and Society - Oxford, Iso-Britannia
Kesto: 1 elok. 20223 elok. 2022
https://www.aies-conference.com/2022/

Conference

ConferenceAAAI/ACM Conference on Artificial Intelligence, Ethics, and Society
LyhennettäAIES
Maa/AlueIso-Britannia
KaupunkiOxford
Ajanjakso1/08/223/08/22
www-osoite

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