Assessing and Enforcing Fairness in the AI Lifecycle

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.

keywords   AI Ethics, Trust, Fairness

funding project

 AEQUITAS — Assessment and Engineering of eQuitable, Unbiased, Impartial and Trustworthy Ai Systems (01/11/2022–31/10/2025)

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)

 XAI: Current Frontiers and the Path Ahead Towards Trustworthy AI (XLoKR 2023 - Explainable Logic-Based Knowledge Representation KR 2023, 02/09/2023) — Roberta Calegari (Roberta Calegari)