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Many ethical frameworks require artificial intelligence (AI) systems to beexplainable. Explainable AI (XAI) models are frequently tested for theiradequacy in user studies. Since different people may have different explanatoryneeds, it is important that participant samples in user studies are largeenough to represent the target population to enable generalizations. However,it is unclear to what extent XAI researchers reflect on and justify theirsample sizes or avoid broad generalizations across people. We analyzed XAI userstudies (n = 220) published between 2012 and 2022. Most studies did not offerrationales for their sample sizes. Moreover, most papers generalized theirconclusions beyond their target population, and there was no evidence thatbroader conclusions in quantitative studies were correlated with largersamples. These methodological problems can impede evaluations of whether XAIsystems implement the explainability called for in ethical frameworks. Weoutline principles for more inclusive XAI user studies.

Unjustified Sample Sizes and Generalizations in Explainable AI Research: Principles for More Inclusive User Studies