Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle

Fairness requires different solutions at different stages: The paper identifies eight distinct fairness goals that need to be addressed throughout an AI system's entire lifecycle—from initial design through deployment and monitoring—rather than treating fairness as a single problem to solve at one point in time.

Explainability alone isn't a fairness fix: While policymakers and researchers often view explainable AI (XAI) as a solution to algorithmic fairness problems, the paper reveals that "the precise connections between XAI and fairness remain largely nebulous," suggesting we need clearer understanding of when and how explanations actually help reduce bias and discrimination.

Multiple fairness definitions exist with different trade-offs: The paper identifies that there is "a wide variety of XAI methods and fairness conceptions expressing different desiderata," meaning organizations must carefully choose which fairness goals matter most for their specific context, as different approaches may conflict with each other.

Practical guidance is currently lacking: The authors note there is "no coherent mapping of fairness desiderata along the AI lifecycle," indicating that organizations deploying AI systems lack clear frameworks for understanding which fairness measures to implement and when—a gap this research aims to address.

High-stakes decisions demand urgent attention: The paper emphasizes that fairness issues are "especially" critical "in high-stakes scenarios," underscoring that AI systems used in consequential domains like hiring, lending, and criminal justice require particularly rigorous fairness safeguards.