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Assessing and Enforcing Fairness in the AI Lifecycle
Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), pages 6554–6562
IJCAI Organization
August 2023
A significant challenge in detecting and mitigating bias is creating a mindset amongst AI developers to address unfairness. The current literature on fairness is broad, and the learning curve to distinguish where to use existing metrics and techniques for bias detection or mitigation is difficult. This survey systematises the state-of-the-art about distinct notions of fairness and relative techniques for bias mitigation according to the AI lifecycle. Gaps and challenges identified during the development of this work are also discussed.
keywordsAI Ethics, Trust, Fairness
funding project
AEQUITAS — Assessment and Engineering of eQuitable, Unbiased, Impartial and Trustworthy Ai Systems (01/11/2022–31/10/2025)
works as
reference publication for talk
Assessing and Enforcing Fairness in the AI Lifecycle (Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), 23/08/2023) — Roberta Calegari (Roberta Calegari, Gabriel Gonzalez Castane, Michela Milano, Barry O'Sullivan)
AEQUITAS — Assessment and Engineering of eQuitable, Unbiased, Impartial and Trustworthy Ai Systems (01/11/2022–31/10/2025)
Assessing and Enforcing Fairness in the AI Lifecycle (Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), 23/08/2023) — Roberta Calegari (Roberta Calegari, Gabriel Gonzalez Castane, Michela Milano, Barry O'Sullivan)