Human-Centered Evaluation of Explanations in AI-Assisted Decision-Making

AI explanations have been increasingly used to help people better utilize AI recommendations in AI-assisted decision making. While numerous technical transparency approaches have been established, a human-centered perspective is needed for understanding how human decision makers use and process AI explanations. In my thesis, I start with an empirical exploration of how AI explanations shape the way people understand and utilize AI decision aids. Next, I move to the time-evolving nature of AI explanations, exploring how explanation changes due to AI model updates affect human decision makers' perception and usage of AI models. Lastly, I construct computational human behavior models to gain a more quantitative understandings of human decision makers' cognitive interactions with AI explanations. I conclude with future work on carefully identifying user needs for explainable AI in an era when AI models are becoming more complex and human-AI collaboration scenarios are increasingly diversified.