Controls.
Each entry is a specific control: what it does, where it fits in the prevent, detect, correct and direct lifecycle, and which risks it addresses, graded primary, supporting or compensating. 105 controls.
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6 of 105 controls shown.
| ID | Control | Family | Type |
|---|---|---|---|
| C-018 | AI transparency and disclosure labelling People are told, clearly and at the point of contact, when they are dealing with an AI system and when content they are shown was generated by one. | 08 Transparency & Explainability | Directive |
| C-098 | Explanation method validation The techniques used to explain a model are tested to confirm that they reflect the model's actual behaviour before explanations are relied on internally or given to anyone. | 08 Transparency & Explainability | Detective |
| C-097 | Explanations for affected individuals People subject to an AI-supported decision receive a meaningful explanation of what was decided, the main reasons in their case and how to challenge it. | 08 Transparency & Explainability | Directive |
| C-099 | Information for deployers and users Those who operate and use an AI system receive current, usable instructions covering its purpose, performance, limitations, the meaning of its outputs and the oversight it needs. | 08 Transparency & Explainability | Directive |
| C-100 | Public AI transparency record The organisation publishes and maintains a record of its significant AI uses, describing what each is for, what data it uses and what safeguards apply. | 08 Transparency & Explainability | Directive |
| C-096 | Synthetic content marking and provenance Generated and manipulated media carries machine-readable marking and provenance data, a visible disclosure where one is required, and the marking is tested for robustness. | 08 Transparency & Explainability | Preventive |