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Mixed-initiative systems allow users to interactively provide feedback topotentially improve system performance. Human feedback can correct model errorsand update model parameters to dynamically adapt to changing data.Additionally, many users desire the ability to have a greater level of controland fix perceived flaws in systems they rely on. However, how the ability toprovide feedback to autonomous systems influences user trust is a largelyunexplored area of research. Our research investigates how the act of providingfeedback can affect user understanding of an intelligent system and itsaccuracy. We present a controlled experiment using a simulated object detectionsystem with image data to study the effects of interactive feedback collectionon user impressions. The results show that providing human-in-the-loop feedbacklowered both participants' trust in the system and their perception of systemaccuracy, regardless of whether the system accuracy improved in response totheir feedback. These results highlight the importance of considering theeffects of allowing end-user feedback on user trust when designing intelligentsystems.

Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy