The paper discusses how Large Language Models can produce natural language self‑explanations that appear plausible but may not accurately reflect the model’s reasoning. It critiques current evaluation methods for such explanations and offers practical guidelines to assess their plausibility and faithfulness. Additionally, it argues that evaluation should also consider the actionability of these explanations, showing how they can aid decision‑making for various stakeholders.
By Elize Herrewijnen, Benedetta Muscato, Gizem Gezici, Fosca Giannotti
arXiv:2606. 14838v1 Announce Type: new Abstract: How to define a good explanation is a long-standing philosophical debate which has found recent renewed interest in the context of AI outputs.
By Louis Mahon, Elliot Ford, Callum Hackett
arXiv:2606. 16786v1 Announce Type: new Abstract: Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short.
By Eric G\"unther, Bal\'azs Szabados, Kristof Meding, Gunnar K\"onig, Sebastian Bordt, Ulrike von Luxburg
Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not effective in practice. We argue that, given the si...
arXiv:2607. 21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal.
By Heather Merhout (Miami University), Daniela Inclezan (Miami University)
arXiv:2608.22356v1 Announce Type: new
Abstract: Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not e...
By Claire Vlases, Katelyn Morrison