Reliable explainability for current AI models

Develop robust, reliable methods to properly explain the decisions of current artificial intelligence systems—especially deep learning and large language models—beyond existing post-hoc interpretability techniques whose outputs can be inconsistent or misleading.

Background

After reviewing the limitations of existing explainability approaches, the text highlights persistent opacity in deep learning–based systems and the inadequacy of current post-hoc methods for trustworthy explanations in critical domains.

The authors explicitly state that we still do not know how to properly explain the decisions of contemporary AI models, framing this as an ongoing, unresolved challenge for interpretability and trust.