Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle
This work addresses the problem of improving fairness in AI systems for researchers and policymakers, but it is incremental as it synthesizes existing concepts without introducing new methods or data.
The paper tackles the unclear connections between explainable AI (XAI) and algorithmic fairness by distilling eight fairness desiderata and mapping them along the AI lifecycle, aiming to guide practical applications and inspire targeted XAI research.
The widespread use of artificial intelligence (AI) systems across various domains is increasingly surfacing issues related to algorithmic fairness, especially in high-stakes scenarios. Thus, critical considerations of how fairness in AI systems might be improved -- and what measures are available to aid this process -- are overdue. Many researchers and policymakers see explainable AI (XAI) as a promising way to increase fairness in AI systems. However, there is a wide variety of XAI methods and fairness conceptions expressing different desiderata, and the precise connections between XAI and fairness remain largely nebulous. Besides, different measures to increase algorithmic fairness might be applicable at different points throughout an AI system's lifecycle. Yet, there currently is no coherent mapping of fairness desiderata along the AI lifecycle. In this paper, we we distill eight fairness desiderata, map them along the AI lifecycle, and discuss how XAI could help address each of them. We hope to provide orientation for practical applications and to inspire XAI research specifically focused on these fairness desiderata.