What is XAI (explainable AI)?

Explainable AI (XAI) refers to methods and tools that make a model’s predictions understandable to humans without materially reducing predictive performance. Common variants include interpretable machine learning and model explainability.

XAI work gained momentum with DARPA’s XAI program and the broader push for transparent, auditable AI in regulated contexts. The program set goals for techniques that reveal what a model has learned and why it predicts a given outcome, while balancing accuracy and explainability. Subsequent research has expanded the toolbox of techniques and emphasized usability by end users.

Representative techniques include:

1. Saliency maps and Grad-CAM for vision models that highlight influential regions.
2. SHAP and LIME for tabular and text models that estimate feature contributions.
3. Rule lists and anchors that provide human-readable decision logic for some models.
4. Counterfactual explanations that show minimal changes needed to flip a prediction.

How it connects to labeling: explanations help identify weak spots in the current dataset. If a saliency map shows a traffic-sign model focusing on background poles, the team can add counterexamples and refine the ontology to decouple objects from context.

In human-in-the-loop review, explanations help triage cases and justify automatic acceptance for routine items [HITL]. XAI also supports compliance by making reviewer rationales and model factors auditable alongside labels. Government summaries describe XAI as a portfolio that maintains accuracy while improving transparency and operator trust.

Example:

A fintech team uses SHAP to explain why loan applications were declined. Analysts notice the model overweights a proxy for employment gaps. They collect additional data, rebalance the training set, and adjust the feature pipeline. Rejections now rely on clearer risk drivers, and reviewers can audit decisions more easily.

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