I humanize, therefore I understand? Effects of explanations and humanization of intelligent systems on perceived and objective user understanding

The functioning of intelligent systems can be opaque to users. Yet, users need to make informed choices about them. This work compares two knowledge mechanisms, i.e., ways for users to achieve an understanding of intelligent systems: explanation and humanization. In online experiment (N = 416), we compared the effects of a control condition without any explanation against a) aneutral and b) a humanized how-explanations as well as c) active humanization on (perceived and objective) user understanding and systems perceptions (trust, transparency, satisfaction, perceived usefulness). Our main finding: Explanations increased transparency and perceived understanding but not objective understanding. Active humanization, surprisingly, decreased objective understanding compared to the control condition suggesting inhibition of knowledge retrieval. Humanized how-explanation increased the perceived usefulness. We found no effects for trust and satisfaction. We conclude that explanations lead to a deceptive feeling of understanding. Explanations shouldconsider the prior understanding to affect objective understanding.