Beyond fairness, AI audits must assess whether systems are transparent, explainable, and safe.

Explainability Assessment

Evaluating how well AI decisions can be understood:

• Global Explanations — how does the model work overall? (feature importance, decision rules)
• Local Explanations — why did the model make THIS specific decision?
• Counterfactual Explanations — what would need to change to get a different outcome?
• Explanation Quality — are explanations faithful, stable, and understandable?

Explainability Tools

• SHAP — game-theoretic feature attribution values
• LIME — local interpretable model-agnostic explanations
• InterpretML — Microsoft's interpretable ML library
• Captum — PyTorch model interpretability library
• ELI5 — simple explanations for scikit-learn models

Safety Testing

Ensuring AI systems don't cause harm:

1. Red Teaming — adversarial testing to find harmful outputs and failure modes
2. Stress Testing — evaluate performance under extreme or unusual inputs
3. Edge Case Analysis — test behavior on unusual but possible inputs
4. Robustness Testing — evaluate sensitivity to small input perturbations
5. Failure Mode Analysis — systematically identify how and when the system fails

LLM-Specific Safety

Large language models require specialized safety assessment:

• Harmful Content Generation — test for violence, illegal activity, and dangerous information
• Bias & Stereotypes — evaluate outputs for social biases and stereotyping
• Hallucination Rate — measure frequency of factually incorrect claims
• Jailbreak Resistance — test prompt injection and system prompt extraction
• Privacy Leakage — check for memorized training data exposure

Documentation Standards

Proper documentation is essential for auditing:

• Model Cards — standardized model documentation including intended use and limitations
• Datasheets for Datasets — documenting data collection, composition, and preprocessing
• System Cards — documenting entire AI systems, not just individual models
• Impact Assessments — documenting potential societal impacts and mitigations
• Audit Reports — structured findings, evidence, and recommendations