What is Explainable AI?

Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making.

Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production. AI explainability also helps an organization adopt a responsible approach to AI development. There are many advantages to understanding how an AI-enabled system has led to a specific output.  Explainability can help developers ensure that the system is working as expected, it might be necessary to meet regulatory standards, or it might be important in allowing those affected by a decision to challenge or change that outcome. Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in organizations with fairness, model explainability and accountability.