Rafal Kocielnik, Saleema Amershi, & Paul N. Bennett (2019)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '19), 1-14.
DOI: https://doi.org/10.1145/3290605.3300641
Abstract. Empirical study showing that pre-emptive expectation-setting (accuracy indicators, example-based onboarding, control-rich review interfaces) measurably increases user acceptance of an AI with known limitations. Foundational reference for "calibrated trust" design moves in AI-driven products.
Tags: ai-usability trust expectation-setting