# Explainability

`kaal:entity:explainability`

**Status.** derived

This node is assembled mechanically from the 6 claims that carry the concept tag `explainability`. 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

6 claims across 3 works, 2024 to 2025.

**2024**

- [4796714-007](https://wulfkaal.github.io/claims/4796714-007) [failure/evidenced] *(failure mode)* -- The black box character of deep learning models is a governance failure and not merely a technical inconvenience: opacity obstructs debugging, obscures bias detection and mitigation, and prevents comprehension of how inputs become outputs.
  > This opacity can obstruct the debugging process, obscure bias detection and mitigation, and hinder comprehension of AI decision-making.
  Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714
- [4941807-008](https://wulfkaal.github.io/claims/4941807-008) [failure/evidenced] *(failure mode)* -- The opacity of deep learning models obstructs debugging, obscures the detection and mitigation of bias, and prevents comprehension of how AI decisions are reached.
  > This opacity can obstruct the debugging process, obscure bias detection and mitigation, and hinder comprehension of AI decision-making.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807
- [4941807-009](https://wulfkaal.github.io/claims/4941807-009) [empirical/evidenced] *(failure mode)* -- Concrete cases show the cost of AI opacity: Nvidia self driving cars that learn from human behavior might confuse the moon for a traffic light, and the DeepPatient project predicted disease onset accurately from medical records while offering no explanation for its predictions.
  > For example, Nvidia's self-driving cars learn from human behavior but might confuse the moon for a traffic light, and the DeepPatient project accurately predicted disease onset from medical records without providing explanations for its predictions
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807
- [4941807-016](https://wulfkaal.github.io/claims/4941807-016) [failure/argued] *(failure mode)* -- Legal and ethical challenges intensify when AI is deployed in critical decision making roles that significantly affect human lives and the reasoning behind the AI decision is opaque.
  > Legal and ethical challenges are heightened when AI is deployed in critical decision-making roles that significantly impact human lives, particularly when the reasoning behind AI's decisions is opaque.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

**2025**

- [5541658-003](https://wulfkaal.github.io/claims/5541658-003) [design/argued] -- The distinctive strength of case-based reasoning systems, exemplified by HYPO, is that they model legal argumentation in a detailed and realistic manner rather than merely producing an outcome.
  > A clear strength of the HYPO program was to model legal argumentation in a detailed and realistic manner. This reflects the strength of case-based reasoning systems.
  Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658
- [5541658-011](https://wulfkaal.github.io/claims/5541658-011) [normative/argued] -- Accuracy alone is insufficient for legal AI: a model must also be explainable before its outputs can be trusted in judicial settings.
  > Accuracy alone is insufficient without explainability.
  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/explainability.md | sha256sum

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