Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making
Proceedings of the CHI Conference on Human Factors in Computing Systems(2022)
摘要
In this work, we study the effects of feature-based explanations on
distributive fairness of AI-assisted decisions, specifically focusing on the
task of predicting occupations from short textual bios. We also investigate how
any effects are mediated by humans' fairness perceptions and their 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, we see that such explanations do not enable humans to
discern correct and incorrect 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 AI recommendations that align with gender
stereotypes. Meanwhile, if explanations appear task-relevant, this induces
reliance behavior that reinforces stereotype-aligned errors. These results
imply that feature-based explanations are not a reliable mechanism to improve
distributive fairness.
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