Explainable Artificial Intelligence (XAI)
Explainable AI (XAI) is a set of processes and methods that allow human users to understand and trust the results and outputs created by machine learning algorithms.
XAI is used to describe AI models, their intended impacts, and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in AI decisions. Explainable AI is critical for organizations to build trust and confidence when putting AI models into production. AI explainability also helps organizations take a responsible approach to AI development.
As AI becomes more advanced, humans face the challenge of understanding and tracing back how the algorithms arrive at their results. The entire computational process becomes what is commonly known as an unexplainable “black box.” These black box models are created directly from data. Moreover, even the engineers or data scientists who created the algorithms cannot understand or explain what exactly happens inside them, or how the AI algorithms arrive at specific results.
Understanding how an AI system produced a particular output has many benefits. Explainability can help developers ensure that the system works as intended, may be necessary to meet regulatory standards, or may be important to allow those affected by a decision to question or change the outcome.