Explore the intersection of machine learning and decision-making in this comprehensive lecture from the Alan Turing Institute's conference on decision support and recommender systems. Delve into topics such as majority class decision rules, ROC curves, logistic and beta calibration, precision-recall-gain curves, and F-score calibration. Learn how to adapt machine learning models to deployment contexts and gain insights into the development of a measurement theory for ML. Discover the potential of AI techniques in supporting complex decision-making processes across various domains, including management, health, urban planning, and sustainability.