Fundamental Limitation in Explaining AI
A recent paper discusses the inherent limitations in explaining AI systems, particularly large-scale models. The authors present a quadrilemma that highlights the challenges of achieving complexity, performance, interpretability, and faithfulness in AI explanations simultaneously. This suggests that AI governance should acknowledge the incomplete nature of explanations provided by AI systems.
- ▪The paper mathematically proves a fundamental quadrilemma in explaining AI.
- ▪The quadrilemma states that AI explanations cannot satisfy complexity, performance, interpretability, and faithfulness at the same time.
- ▪The authors recommend focusing on explaining only the important parts of AI systems for practical applications.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24727 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | MD0a0N6-vTfK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24727 (cs) [Submitted on 23 May 2026] Title:Fundamental Limitation in Explaining AI Authors:Atsushi Suzuki, Jing Wang View a PDF of the paper titled Fundamental Limitation in Explaining AI, by Atsushi Suzuki and 1 other authors View PDF HTML (experimental) Abstract:While large-scale models such as LLMs and diffusion models have achieved practical success, public institutions have emphasized the importance of explainability in AI. Existing methods for explaining AI, however, are not designed to provide completely faithful explanations of the behavior of large-scale AI systems.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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