  This paper reviews our previous trials of Nudge-XAI, an approach that
introduces automatic biases into explanations from explainable AIs (XAIs) with
the aim of leading users to better decisions, and it discusses the benefits and
challenges. Nudge-XAI uses a user model that predicts the influence of
providing an explanation or emphasizing it and attempts to guide users toward
AI-suggested decisions without coercion. The nudge design is expected to
enhance the autonomy of users, reduce the risk associated with an AI making
decisions without users' full agreement, and enable users to avoid AI failures.
To discuss the potential of Nudge-XAI, this paper reports a post-hoc
investigation of previous experimental results using cluster analysis. The
results demonstrate the diversity of user behavior in response to Nudge-XAI,
which supports our aim of enhancing user autonomy. However, it also highlights
the challenge of users who distrust AI and falsely make decisions contrary to
AI suggestions, suggesting the need for personalized adjustment of the strength
of nudges to make this approach work more generally.
