AI Explainability: Definition and Examples

AI Explainability is a fundamental field that aims to make the decision-making processes of artificial intelligence systems transparent. While many models, particularly deep neural networks, operate as "black boxes," explainability seeks to lift the veil on their internal mechanisms to allow users, developers, and regulators to understand why a particular decision was made.

This concept has become crucial with the democratization of AI in sensitive fields such as healthcare, finance, justice, and human resources. When an algorithm denies a bank loan, recommends a medical treatment, or evaluates a job candidate, it is essential to be able to justify that decision. Explainability meets an ethical, legal (notably with the GDPR and the European AI Act), and practical need.

Several techniques achieve different levels of explainability. Post-hoc methods like LIME or SHAP analyze a model after training to identify factors that influenced each prediction. Other approaches favor intrinsically interpretable models, such as decision trees or logistic regression, which are transparent by design. The choice between performance and explainability is often a central trade-off in designing AI systems.

In prompt engineering, explainability takes on a particular dimension: one can explicitly ask a language model to detail its reasoning, justify its choices, or present the steps of its thought process. This practice, known as chain-of-thought prompting, improves both the transparency of responses and often their quality.