DIR 2020 is inviting Belgium and Netherlands-based
researchers to talk about their work in a poster session.
Want to present your recent publication? Have an idea that you want feedback on? Come talk to your peers about it!
You can expect vibrant discussions and plenty of social interactions. Don't hesitate and claim your spot in our virtual poster room.
Application deadline: November 30
Poster deadline: December 1
Lightning Talks — eCommerce & Bandit Learning ⚡
Time for coffee ☕
Lightning Talks — Natural Language Processing ⚡
Lunch Break 🥙
Lightning Talks — Fairness ⚡
(BIAS'20, ECML-PKDD Workshop)
Fair Ranking with Biased Data
Thorsten Joachims, Cornell University
Search engines and recommender systems have become the dominant matchmaker for a wide range of human endeavors -- from online retail to finding romantic partners. Consequently, they carry immense power in shaping markets and allocating opportunity to the participants. In this talk, I will discuss how the machine learning algorithms underlying these system can produce unfair or undesirable ranking policies for both exogenous and endogenous reasons. Exogenous reasons often manifest themselves as biases in the training data, which then get reflected in the learned ranking policy and lead to rich-get-richer dynamics. But even when trained with unbiased data, reasons endogenous to the algorithms can lead to unfair or undesirable allocation of opportunity. To overcome these challenges, I will present new machine learning algorithms that directly address some forms of endogenous and exogenous unfairness.
Thorsten Joachims is a Professor in the Department of Computer Science and in the Department of Information Science at Cornell University. His research interests center on a synthesis of theory and system building in machine learning, with applications in information access, language technology, and recommendation. His past research focused on counterfactual and causal inference, support vector machines, text classification, structured output prediction, convex optimization, learning to rank, learning with preferences, and learning from implicit feedback. He is an ACM Fellow, AAAI Fellow, and Humboldt Fellow.
Bart Goethals (University of Antwerp & Froomle)
Jan Van Balen (University of Antwerp)
Joey de Pauw (University of Antwerp)
Olivier Jeunen (University of Antwerp)
Lien Michiels (University of Antwerp & Froomle)
Robin Verachtert (University of Antwerp & Froomle)