Effects of Algorithmic Transparency on User Experience and Physiological Responses in Affect-Aware Task Adaptation

Abstract

In affect-aware task adaptation, users’ psychological states are recognized with diverse measurements and used to adapt computer-based tasks. User experience with such adaptation improves as the accuracy of psychological state recognition and task adaptation increases. However, it is unclear how user experience is influenced by algorithmic transparency: the degree to which users understand the computer's decision-making process. We thus created an affect-aware task adaptation system with 4 algorithmic transparency levels (none/low/medium/high) and conducted a study where 93 participants first experienced adaptation with no transparency for 16 minutes, then with one of the other 3 levels for 16 minutes. User experience questionnaires and physiological measurements (respiration, skin conductance, heart rate) were analyzed with mixed 2×3 analyses of variance (time × transparency group). Self-reported interest/enjoyment and competence were lower with low transparency than with medium/high transparency, but did not differ between medium and high transparency. The transparency level may also influence participants’ respiratory responses to adaptation errors, but this finding is based on ad-hoc t-tests and should be considered preliminary. Overall, results show that the degree of algorithmic transparency does influence self-reported user experience. Since transparency information is relatively easy to provide, it may represent a worthwhile design element in affective computing.