Title
Algorithmic Recommendations: What’s the problem?
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.
About Silvia
Silvia is Senior Lecturer in Philosophy in the SPA Department and Egenis centre at the University of Exeter, and a Humboldt Fellow at the LMU Munich, based at the Munich Centre for Mathematical Philosophy. Previously, she was a Research Fellow at the Future of Humanity Institute (FHI) in the Faculty of Philosophy, University of Oxford and William Golding Junior Research Fellow at Brasenose College. Before that, she was a Postdoctoral Researcher at the Oxford Internet Institute, where she remains affiliated with the Governance of Emerging Technologies (GET) research programme. She completed a PhD in Philosophy at the London School of Economics and Political Science in 2018. Her interests lie primarily in epistemology, ethics, and intersections between the two, especially in connection with Artificial Intelligence. Her current research investigates epistemological and ethical issues relating to recommender systems.