BackLack of model transparency
Lack of model transparency
Risk Domain
Challenges in understanding or explaining the decision-making processes of AI systems, which can lead to mistrust, difficulty in enforcing compliance standards or holding relevant actors accountable for harms, and the inability to identify and correct errors.
"Lack of model transparency is due to insufficient documentation of the model design, development, and evaluation process and the absence of insights into the inner workings of the model."
Entity: Who or what caused the harm
Intent: Whether the harm was intentional or accidental
Timing: Whether the risk is pre- or post-deployment
Supporting Evidence (1)
1.
"Transparency is important for legal compliance, AI ethics, and guiding appropriate use of models. Missing information might make it more difficult to evaluate risks, change the model, or reuse it. Knowledge about who built a model can also be an important factor in deciding whether to trust it. Additionally, transparency regarding how the model’s risks were determined, evaluated, and mitigated also play a role in determining model risks, identifying model suitability, and governing model usage."
Other risks from IBM (2025) (63)
Lack of training data transparency
6.5 Governance failureHumanUnintentionalPre-deployment
Uncertain data provenance
6.5 Governance failureHumanOtherPre-deployment
Data usage restrictions
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Data acquisition restrictions
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Data transfer restrictions
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Personal information in data
2.1 Compromise of privacy by leaking or correctly inferring sensitive informationAI systemUnintentionalPost-deployment