Improving User Mental Models of XAI Systems with an Inclusive Design Approach

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. To our surprise, the inclusivity fixes did not improve participants' prediction performance. However, the fixes did improve inclusivity support for women and promoted equity by reducing the gender gap.