Explainable AI (XAI) refers to a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. XAI is used to describe an AI model, its expected impact, and potential biases. It characterizes model accuracy, fairness, transparency, and outcomes in AI-powered decision making. As AI models, particularly deep neural networks, become more complex ('black boxes'), understanding 'why' a decision was made becomes crucial for accountability, especially in high-stakes fields like medicine and finance.
The need for explainability has existed since the early days of expert systems (1970s), but the term and the field exploded in importance with the rise of deep learning in the 2010s and regulations like GDPR which mandate a 'right to explanation'.
XAI is now a critical requirement in regulated industries. Techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are standard tools for data scientists to debug models and explain predictions to stakeholders.