Xinru Wang|Ming Yin
Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
Purdue University System|Purdue University System
paper contributes growing literature empirical evaluation explainable ai xai methods presenting comparison effects set established xai methods aiassisted decision making specifically based review previous literature highlight three desirable properties ideal ai explanations satisfyimprove peoples understanding ai model help people recognize model uncertainty support peoples calibrated trust model randomized controlled experiments evaluate whether four types common modelagnostic explainable ai methods satisfy properties two types decision making tasks people perceive different levels domain expertise ie recidivism prediction forest cover prediction results show effects ai explanations largely different decision making tasks people varying levels domain expertise many ai explanations satisfy desirable properties tasks people little domain expertise decision making tasks people knowledgeable feature contribution explanation shown satisfy desiderata ai explanations explanation considered resemble human explain decisions ie counterfactual explanation seem improve calibrated trust conclude discussing implications study improving design xai methods better support human decision making