entity · derived
Explainability
Derived node: assembled mechanically from the claims carrying explainability. A roster, not an adjudicated definition.
Every claim under this term
- 4796714-007 : 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 comp
- 4941807-008 : The opacity of deep learning models obstructs debugging, obscures the detection and mitigation of bias, and prevents comprehension of how AI decisions are reached.
- 4941807-009 : 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 accu
- 4941807-016 : 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.
- 5541658-003 : 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.
- 5541658-011 : Accuracy alone is insufficient for legal AI: a model must also be explainable before its outputs can be trusted in judicial settings.