In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making
CoRR(2023)
摘要
The current literature on AI-advised decision making – involving explainable
AI systems advising human decision makers – presents a series of inconclusive
and confounding results. To synthesize these findings, we propose a simple
theory that elucidates the frequent failure of AI explanations to engender
appropriate reliance and complementary decision making performance. We argue
explanations are only useful to the extent that they allow a human decision
maker to verify the correctness of an AI's prediction, in contrast to other
desiderata, e.g., interpretability or spelling out the AI's reasoning process.
Prior studies find in many decision making contexts AI explanations do not
facilitate such verification. Moreover, most tasks fundamentally do not allow
easy verification, regardless of explanation method, limiting the potential
benefit of any type of explanation. We also compare the objective of
complementary performance with that of appropriate reliance, decomposing the
latter into the notions of outcome-graded and strategy-graded reliance.
更多查看译文
关键词
verifiability,decision making,explanations,ai-advised
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要