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.
At this year’s Hi! PARIS Summer School, Solenne Gaucher (École polytechnique) shed light on the growing challenge of fairness in AI. As algorithms trained on biased data shape decisions at scale, she reminded us that fairness is neither only a mathematical problem nor only an ethical one. Instead, it sits at the intersection of both, and demands attention from scientists, policymakers, and society alike.