Will We Trust What We Don't Understand? Impact of Model Interpretability and Outcome Feedback on Trust in AI

Understand

Despite AI's superhuman performance in a variety of domains, humans are often unwilling to adopt AI systems.

• The lack of interpretability inherent in many modern AI techniques is believed to be hurting their adoption, as users may not trust systems whose decision processes they do not understand.
• We investigate this proposition with a novel experiment in which we use an interactive prediction task to analyze the impact of interpretability and outcome feedback on trust in AI and on human performance in AI-assisted prediction tasks.
• We find that interpretability led to no robust improvements in trust, while outcome feedback had a significantly greater and more reliable effect.