Read why AI explainability is crucial to production AI.

What is explainable AI and why does it matter?

Explainable AI is a set of capabilities and methods used to describe an AI model, its expected impact and potential biases. These features help identify model accuracy, fairness, transparency and outcomes. Explainable AI is crucial to promote trust and transparency when putting AI models into operation.

Monitor and manage models to improve AI trust and transparency

Most AI systems are black boxes with no model explainability. Bias based on race, gender or other factors impair AI model training. The answer is continuous monitoring and management of models themselves along with the effects of algorithms based on model data. In addition, Explainable AI helps mitigate compliance, legal, security and reputational risks of production AI.

Deploy AI with continuous model evaluation

Scaling AI requires constant monitoring of model deployment status, fairness, quality and drift. Continuous model evaluation helps you understand model risk and optimize model performance. IBM Cloud Pak® for Data offers data and AI services in a unified environment that lets you assess impact and relationships of both data and models to improve AI explainability.