ART-AI Seminar
We are pleased to have Silvia Milano, who is a Humboldt Fellow in the Munich Center for Mathematical Philosophy at LMU Munich and a Senior Lecturer in Philosophy at the University of Exeter (UK), join us for this ART-AI seminar entitled ‘Algorithmic Recommendations: What’s the problem?’.
This seminar will take place in person in 1W 3.103, on Tuesday 17th June 2025, 13.15pm-14.15pm (GMT). Cohort 4 student Eoin Cremen will be chairing this seminar. There is also an option to join online. For more information, please e-mail [email protected].
Title
Algorithmic Recommendations: What’s the problem? with Silvia Milano
Abstract
We live in an increasingly datafied world, where information is abundant but difficult to navigate. In this context, we have become accustomed to interacting with recommender systems that personalise and structure the information we access in digital environments, which have emerged as one of the foremost applications of machine learning today. After introducing the basic features and techniques for algorithmic recommenders, I consider a currently dominant problem formulation for algorithmic recommendations in terms of a prediction task and critically assess its epistemological and normative assumptions.
Bio
Silvia is a Humboldt Fellow in the Munich Center for Mathematical Philosophy at LMU Munich and a Senior Lecturer in Philosophy at the University of Exeter (UK). She was previously a Golding Junior Research Fellow at Brasenose College and Research Fellow in Philosophy of AI at the University of Oxford, and Postdoctoral Researcher at the Oxford Internet Institute. She obtained a PhD in Philosophy from the London School of Economics and Political Science with a thesis on the epistemology of de se beliefs. Her research interests are in Epistemology and Ethics of AI. Her recent work has focused on AI and epistemic injustice, the ethical challenges of recommender systems, polarisation on social networks, and the impacts of LLMs on education. Silvia is currently developing a philosophical analysis of recommender systems, encompassing their social, epistemological, and ethical dimensions.
