Explainable AI

Explainable AI (XAI) encompasses methods and techniques that make Artificial Intelligence systems understandable to humans, including domain experts, by providing explanations, evidence, or reasons for their outputs and processes. It is particularly relevant to complex Machine Learning models, including Deep Learning models and large Neural Networks, that often operate as Black Box systems. Explainability can address both individual outputs and overall model behavior, supporting Interpretability rather than merely justifying specific predictions. Common approaches include Feature Attribution and Post Hoc Explanation methods such as LIME and SHAP. Attention Mechanisms, by contrast, are model components; their weights may inform some analyses but do not inherently provide faithful explanations. IBM and Google offer explainability tools, while DARPA has sponsored research through its Explainable AI program, rather than acting as a comparable framework provider. In sectors such as healthcare and finance, these methods can support Model Governance, Model Accountability, and the identification and mitigation of Algorithmic Bias, although explanations alone do not ensure transparency, fairness, or warranted trust.