Improving User Mental Models of XAI Systems with Inclusive Design Approaches
arxiv(2024)
摘要
Explainable Artificial Intelligence (XAI) systems aim to improve users'
understanding of AI but rarely consider the inclusivity aspects of XAI. Without
inclusive approaches, improving explanations might not work well for everyone.
This study investigates leveraging users' diverse problem-solving styles as an
inclusive strategy to fix an XAI prototype, with the ultimate goal of improving
users' mental models of AI. We ran a between-subject study with 69
participants. Our results show that the inclusivity fixes increased
participants' engagement with explanations and produced significantly improved
mental models. Analyzing differences in mental model scores further highlighted
specific inclusivity fixes that contributed to the significant improvement in
the mental model.
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