Peters, Uwe and Carman, Mary
(2024)
Cultural Bias in Explainable AI Research: A Systematic Analysis.
[Preprint]
Abstract
For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often
need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user
studies. However, whether XAI researchers consider potential cultural differences in human
explanatory needs remains unexplored. We highlight psychological research that found significant
differences in human explanations between many people from Western, commonly individualist
countries and people from non-Western, often collectivist countries. We argue that XAI research
currently overlooks these variations and that many popular XAI designs implicitly and
problematically assume that Western explanatory needs are shared cross-culturally. Additionally, we
systematically reviewed over 200 XAI user studies and found that most studies did not consider
relevant cultural variations, sampled only Western populations, but drew conclusions about human-XAI interactions more generally. We also analyzed over 30 literature reviews of XAI studies. Most reviews did not mention cultural differences in explanatory needs or flag overly broad cross-cultural extrapolations of XAI user study results. Combined, our analyses provide evidence of a cultural bias toward Western populations in XAI research, highlighting an important knowledge gap regarding how culturally diverse users may respond to widely used XAI systems that future work can and should address.
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