Scrutable Adaptation: Because We Can and Must

By Judy Kay. 2006

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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, methodology): “User models have been used to support the "tailoring" of explanations in a number of ways (cf. Carberry, 1990 for a discussion of a 1990 workshop; see also Kay, 2000; 2006, Kieras and Polson, 1985; May, Barnard, and Blandford, 1993; Maida and Deng, 1989; Weiner, 1989)”
The effects of transparency on trust in and acceptance of a content-based art recommender (2008, background): “Other prior studies also suggest that making adaptive systems more transparent to the user could lead to a more positive user attitude towards using a system and increases in system performance (e.g. Kay 2006; Höök et al. 1996; Höök 2000; Carmagnola et al. 2006; Gregor and Benbasat 1999).”
Introduction to Recommender Systems Handbook (2011, background): “it is wrong [50]; trust - increasing user confidence in the system; effectiveness helping users make good decisions; persuasiveness - convincing users to try or buy;”