XAI

Explainable Artificial Intelligence (XAI) refers to methods and techniques in artificial intelligence that aim to make the outputs of AI systems understandable to humans. The goal is to provide transparency into how models arrive at predictions or decisions, to support trust, accountability, and governance, and to enable debugging, auditing, and compliance with regulations. XAI is particularly relevant for complex, data-driven models such as deep neural networks, which are often considered black boxes.

Explainability can be intrinsic or post hoc. Intrinsic interpretability uses models that are by design transparent, such as linear models or small decision trees. Post hoc explanations are produced after a model has made a prediction, using approaches such as feature-importance scores, surrogate models, counterfactuals, or example-based explanations. Explanations can be local (focused on a single decision) or global (describing the overall model behavior). Techniques are also categorized as model-agnostic, applicable to any model, or model-specific.

Applications include healthcare, finance, criminal justice, and autonomous systems, where explanations support decision review, user trust, compliance with laws and standards, debugging, and bias detection. Evaluation of explanations is an active area, using fidelity to the model, plausibility for users, and stability across inputs, often complemented by user studies and task-based metrics.

Challenges include balancing accuracy and interpretability, meeting diverse user needs, avoiding misleading or simplified explanations, and developing standards for evaluation and governance. Ongoing work in policy, ethics, and industry guidelines seeks to establish best practices for deploying explainable AI responsibly.