AI systems are usually trained to optimize a goal. Accuracy. Reward. Performance. But Luiz Chamon argues that trustworthy AI requires something more explicit: requirements that define, from the beginning, what a system must also satisfy, from robustness and fairness to safety and prior knowledge. Luiz ChamonAssistant Professor and Hi! PARIS Chair Holder, École polytechnique (Institut Polytechnique de […]
As machine learning systems become embedded in critical decisions, from finance to infrastructure, the need for trustworthy, interpretable predictions has never been greater. Aymeric Dieuleveut, Professor of Statistics and Machine Learning at École polytechnique and scientific co-director of the Hi! PARIS Center, believes the key lies not in the models themselves, but in how we communicate their uncertainty.