What is AI Explainability in Machine Learning

AI Explainability (XAI) allows machine learning algorithms to create outputs and results that can be understood and trusted by humans. A model that is explainable AI describes its impact, biases, and the expected impact of the model. With its help, AI-powered decision-making can be assessed for fairness, transparency, and accuracy. 

When putting AI models into production, building trust and confidence is crucial for an organization. An organization can likewise use AI explainability to adopt a responsible AI development approach.

Understanding the process that led an AI-enabled system to produce a specific outcome is very beneficial. In addition to helping developers monitor a system’s performance, explaining how a decision was made is important for meeting regulatory standards and allowing those affected to challenge the decision.