Preregistered study with 418 UK participants shows that disclosing model limitations during onboarding improves case-wise trust calibration in an XAI skin-lesion classifier, while short-term experience does not progressively improve it and stimulus quality explains more variance than the onboarding.
Communications of the ACM64(12), 86–92 (2021)
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Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users
Preregistered study with 418 UK participants shows that disclosing model limitations during onboarding improves case-wise trust calibration in an XAI skin-lesion classifier, while short-term experience does not progressively improve it and stimulus quality explains more variance than the onboarding.