MIT AI Risk Repository · Risk Category · 05.14.00

Evaluation - Auditing

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Description

Closely related to other clusters like AI safety, fairness, or harmful content, papers stress the importance of evaluating generative AI systems both in a narrow technical way as well as in a broader sociotechnical impact assessment focusing on pre-release audits as well as post-deployment monitoring. Ideally, these evaluations should be conducted by independent third parties. In terms of technical LLM or text-to-image model audits, papers furthermore criticize a lack of safety benchmarking for languages other than English.

From Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: —
Subdomain: —
Causal entity: Not coded
Intent: Not coded
Timing: Not coded

Other entries from Hagendorff2024

Fairness - Bias Risk Category · 1.1
Safety Risk Category · 7.1
Harmful Content - Toxicity Risk Category · 1.2
Hallucinations Risk Category · 3.1
Privacy Risk Category · 2.1
Interaction risks Risk Category · 5.1
Security - Robustness Risk Category · 2.2
Education - Learning Risk Category · 4.3
Alignment Risk Category · 7.1
Cybercrime Risk Category · 4.2
Governance - Regulation Risk Category · 6.5
Labor displacement - Economic impact Risk Category · 6.2