Interpretability & Explainable AI

As neural networks grow more complex, understanding why they make certain predictions becomes increasingly important. Interpretability techniques help reveal which features drive a model’s decisions, enabling researchers and users to verify that algorithms rely on sensible patterns. Methods such as saliency maps, gradient‑based visualisations and feature attributions leverage underlying classification, regression and clustering frameworks to highlight influential inputs and probe internal representations.

Explainability extends beyond visualisations. Local interpretable model‑agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP) approximate the behaviour of black‑box models with simpler surrogates. Concept activation vectors quantify how neurons respond to abstract concepts like gender or colour. Attention mechanisms inherently provide a form of interpretability by weighting input tokens according to relevance. Together, these tools offer multiple lenses through which to inspect model reasoning.

Practical applications underscore the value of interpretability. In healthcare, clinicians can see which areas of a scan influenced a diagnosis or why a model flagged a patient as high risk. In finance, explanations help regulators assess fairness in loan approvals. In scientific discovery, interpretable models reveal relationships that inspire new hypotheses. Explainability also supports debugging by exposing spurious correlations and dataset biases.

Transparency, however, comes with caveats. Explanations are approximations and may misrepresent a model’s true behaviour. Overemphasis on individual features can obscure interactions across layers and modalities. In some cases, making a model interpretable may reduce accuracy or reveal sensitive information. A balanced approach integrates interpretability into the design pipeline, promotes open‑source tools and education, and recognises the limits of current methods while striving for fair and accountable AI.