What is AI Explainability?

AI explainability is the bridge between technical capability and regulatory acceptability. UK regulators do not require firms to avoid AI. They require firms to understand and explain the AI they use. For any AI system that influences decisions affecting customers, patients, or regulatory obligations, you need to be able to articulate how it works and why it produced a particular output.

The level of explainability required depends on the context. A spam filter that occasionally miscategorises an internal email needs minimal explanation. An AI system that contributes to a lending decision, a clinical assessment, or a legal recommendation needs substantially more. The FCA has made clear that firms must be able to explain AI-driven decisions to consumers who are affected by them, and the ICO requires that individuals can obtain meaningful information about the logic involved in automated decision-making.

Explainability operates at multiple levels. Global explainability describes how the model works overall, what factors it considers, and what it is designed to do. Local explainability describes why a specific output was produced for a specific input. For regulated businesses, both matter. Global explainability supports your governance documentation and regulatory submissions. Local explainability supports complaint resolution and individual customer enquiries.

Techniques for achieving explainability vary by the type of AI system. For traditional machine learning models, feature importance scores and partial dependence plots show which input variables most influenced a prediction. For language models, attention visualisation and chain-of-thought prompting can reveal the reasoning process. For complex systems, surrogate models can approximate the behaviour of a black-box model in more interpretable terms.

The practical advice for mid-market firms is to build explainability in from the start rather than retrofitting it later. When evaluating AI solutions, ask the vendor how the system decisions can be explained. When building custom AI systems, choose architectures that support interpretation. When deploying any AI system, document how explanations will be generated, who will review them, and how they will be communicated to affected individuals.