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. Explainability is crucial for AI adoption in many contexts, but in order to produce good explanations of AI systems, we must first have an understanding of what good explanations are. In this paper we propose a definition inspired by the notion of counterfactual explanations, however we argue that one must also take into account the interlocutor's prior beliefs in each fact that could be offered in an expl
The rapid deployment and increasing complexity of LLMs necessitate a clearer understanding of what constitutes a 'good explanation' to foster trust and adoption.
A robust definition of 'good explanations' is critical for developing explainable AI (XAI) tools that can facilitate responsible AI deployment across sensitive sectors.
This research provides a foundational framework for evaluating and improving the explainability of AI systems, moving beyond purely technical metrics to incorporate human cognitive factors.
- · AI developers focused on explainability
- · AI ethicists and regulatory bodies
- · End-users of complex AI systems
- · AI systems lacking transparency features
- · Developers ignoring human-centered explanation design
Improved methodologies for generating and evaluating LLM explanations will emerge.
Increased trust and adoption of AI systems in regulated industries will be observed due to enhanced explainability.
New standards and regulations specifically addressing the quality and comprehensibility of AI explanations may be implemented globally.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI