Robust Visual Explainability: Addressing Distribution Shifts

This research explores a crucial area: ensuring the reliability of AI explanations when encountering data distribution changes. The focus on subset selection provides a potentially practical method for enhancing model robustness.

• •Addresses the challenge of maintaining visual explainability under distribution shifts.
• •Focuses on uncertainty-aware subset selection for increased robustness.
• •The research likely contributes to more reliable AI model interpretability.