Different people need different explanations of an AI system. A single technical explanation rarely serves everyone.
Executives and Sponsors
They need to know what the system does, what value it delivers, how well it works compared with the current process, the main risks and who is accountable. Use plain language and business metrics.
Regulators and Auditors
They need documentation: data sources, model design, evaluation results including fairness testing, governance processes, monitoring and incident handling. Model cards and decision logs are essential.
Operators and Reviewers
People working with the system daily need to know when to trust it, what its confidence means, common failure modes and how to override it.
Affected Individuals
People subject to a decision need to know that AI was involved, the main reasons for the outcome in understandable terms, and how to challenge it. Explanations like "your application was declined mainly because of a short credit history" are more useful than feature-importance charts.
Techniques That Help
- Simple, interpretable models where the accuracy cost is small.
- Local explanations (such as SHAP) translated into plain-language reasons.
- Counterfactual explanations: "if your income were higher by X, the outcome would change."
- Example-based explanations: similar past cases.
Be Honest About Limits
Explanations of complex models are approximations. Don't overstate how well the system's reasoning is understood.