Inaccessible training data
Without access to the training data, the types of explanations a model can provide are limited and more likely to be incorrect.
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit1311
Domain lineage
7. AI System Safety, Failures, & Limitations
7.4 > Lack of transparency or interpretability
Mitigation strategy
1. Implement robust post-hoc, model-agnostic explainability (PHE) techniques (e.g., LIME or SHAP) to generate understandable justifications for individual black-box decisions, thereby creating the necessary transparency and traceability that the inaccessible training data precludes. 2. Establish a continuous monitoring and auditing framework that utilizes disaggregated performance metrics to assess equitable outcomes and detect functional disparities across various user groups, which may reveal biases embedded by opaque training data. 3. Conduct systematic, model-agnostic bias and fairness audits on the model's outputs using external toolkits to identify and mitigate discrimination that cannot be detected via internal data inspection due to inaccessibility.