# Explainable ai

`kaal:entity:explainable-ai`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `explainable-ai`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

3 claims across 2 works, 2024 to 2025.

**2024**

- [4855607-013](https://wulfkaal.github.io/claims/4855607-013) [failure/evidenced] *(failure mode)* -- Explainable reinforcement learning research has not yet produced usable explanations: the field relies on toy examples, omits user testing, produces explanations that are themselves complex, uses basic visualizations, and rarely open sources its code.
  > Current research in explainable RL, which aims to make RL models more transparent and interpretable, also has limitations. These include the use of "toy examples", lack of user testing, complexity of explanations, basic visualizations, and lack of open-sourced code.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**2025**

- [5541658-012](https://wulfkaal.github.io/claims/5541658-012) [failure/argued] *(failure mode)* -- Post hoc explainability techniques do not by themselves establish trustworthiness; the explanations they produce must additionally be verified against human knowledge.
  > However, post-hoc explainers still need to be verified for trustworthiness, in that the explanations comport with human knowledge.
  Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658
- [5541658-015](https://wulfkaal.github.io/claims/5541658-015) [design/evidenced] -- Transparent, explainable models such as those built for the European Court of Human Rights, which paired 97% accuracy with digestible explanations, provide the design template for addressing legal AI's transparency problem.
  > Transparent, explainable AI models, such as those developed for the ECHR, which achieved 97% accuracy with digestible explanations, offer a model for addressing these concerns.
  Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/explainable-ai.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.