Fairness in Machine Learning
AI4WorkspaceSafetyAbstract
Ensuring fairness in AI is crucial as algorithms increasingly influence decisions in hiring, lending, and social services. This talk discusses key principles, fairness-aware machine learning techniques, and regulatory frameworks that help mitigate bias and promote equitable outcomes.
On November 28, 2022, I delivered an invited lecture at the seminar “Computational Intelligence and Computer Vision”, organized as part of the AI4WorkplaceSafety project. The lecture focused on Fairness in Machine Learning, exploring challenges, methodologies, and strategies to ensure fairness in algorithmic decision-making processes.
Sandro Radovanović
Assistant Professor at University of Belgrade
My research interests include machine learning, development and design of decision support systems, decision theory, and fairness and justice concepts in algorithmic decision making.