Proponents of explainable AI have often argued that it constitutes an
essential path towards algorithmic fairness. Prior works examining these claims
have primarily evaluated explanations based on their effects on humans'
perceptions, but there is scant research on the relationship between
explanations and distributive fairness of AI-assisted decisions. In this paper,
we conduct an empirical study to examine the relationship between feature-based
explanations and distributive fairness, mediated by human perceptions and
reliance on AI recommendations. Our findings show that explanations influence
fairness perceptions, which, in turn, relate to humans' tendency to adhere to
AI recommendations. However, our findings suggest that such explanations do not
enable humans to discern correct and wrong AI recommendations. Instead, we show
that they may affect reliance irrespective of the correctness of AI
recommendations. Depending on which features an explanation highlights, this
can foster or hinder distributive fairness: when explanations highlight
features that are task-irrelevant and evidently associated with the sensitive
attribute, this prompts overrides that counter stereotype-aligned AI
recommendations. Meanwhile, if explanations appear task-relevant, this induces
reliance behavior that reinforces stereotype-aligned errors. These results show
that feature-based explanations are not a reliable mechanism to improve
distributive fairness, as their ability to do so relies on a human-in-the-loop
operationalization of the flawed notion of "fairness through unawareness".
Finally, our study design provides a blueprint to evaluate the suitability of
other explanations as pathways towards improved distributive fairness of
AI-assisted decisions.

研究了基于 AI 解释和分布式公平性之间的关系，发现解释会影响公平感知，从而与人类对 AI 建议的依赖关系发生关联。该研究表明，基于特征的解释并不是改善分布式公正性的可靠机制。

人工智能决策中的解释、公平性和适当依赖

On Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making

It is known that recommendations of AI-based systems can be incorrect or
unfair. Hence, it is often proposed that a human be the final decision-maker.
Prior work has argued that explanations are an essential pathway to help human
decision-makers enhance decision quality and mitigate bias, i.e., facilitate
human-AI complementarity. For these benefits to materialize, explanations
should enable humans to appropriately rely on AI recommendations and override
the algorithmic recommendation when necessary to increase distributive fairness
of decisions. The literature, however, does not provide conclusive empirical
evidence as to whether explanations enable such complementarity in practice. In
this work, we (a) provide a conceptual framework to articulate the
relationships between explanations, fairness perceptions, reliance, and
distributive fairness, (b) apply it to understand (seemingly) contradictory
research findings at the intersection of explanations and fairness, and (c)
derive cohesive implications for the formulation of research questions and the
design of experiments.

研究人员探讨了 AI 系统推荐错误或不公平的情况下，人工干预的必要性，并提供了关于解释、公平感知、依赖和分布公平性之间关系的理论框架，但文献并没有提供确凿证据证明解释在实践中实现了这种补充关系。