Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders

Autor:	Curto, Georgina, Comim, Flavio
Rok vydání:	2023
Předmět:	Computer Science - Computers and Society Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.06031 Zobrazit plný text záznamu View this record from Arxiv