authors

Roberta Calegari

UNIBO

Gabriel G. Castañé

UCC

Michela Milano

UNIBO

Barry O’Sullivan

UCC

SCIENTIFIC PAPERS 25-08-2023

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:
Survey: AI Ethics, Trust, Fairness
Survey: Machine Learning
Survey: Humans and AI

authors

Roberta Calegari

UNIBO

Gabriel G. Castañé

UCC

Michela Milano

UNIBO

Barry O’Sullivan

UCC

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