9
March
2022
11:30
Machine learning models are being used more and more often to support important decisions - from assisting doctors to diagnose a health problem, to recommending suitable candidates for a job opening, or detecting fraudulent transactions in the financial industry. It becomes crucial to be able to understand and interpret the inner workings and predictions of such models, in order to reduce errors, build trust and gain new insights. This is the goal of Explainable AI, also often called XAI, a widely used term describing processes and methods to make machine learning models and their outputs understandable to humans.
What you’ll learn during this webinar