In this session, we will explore how cultural taste and everyday choices are modeled, shaped and shifted through data, platforms and AI. Presenters will examine how digital traces and recommender systems reconfigure classical understandings of taste and how algorithmic mediation intervenes in what people come to like and choose. Drawing on work in cultural sociology, consumer research and recommender-system design, the session brings together perspectives on algorithmically mediated taste and recommendations, from music to food, highlighting how algorithms not only reflect but also transform preferences. The session invites reflection on how abstract notions of “taste” are operationalized, nudged and negotiated in AI-driven environments.
The event if followed by drinks!
Abstract
An index is a function that, given an election, outputs a value between 0 and 1, indicating the extent to which this election has a particular feature. We seek indices that capture agreement, diversity, and polarization among voters in approval elections, and that are normalized with respect to saturation. By the latter we mean that if two elections differ by the fraction of candidates approved by an average voter, but otherwise are of similar nature, then they should have similar index values. We propose several indices, analyze their properties, and use them to (a) derive a new map of approval elections, and (b) show similarities and differences between various real-life elections (including those from Pabulib, Preflib and other sources). This is joint work with Piotr Faliszewski, Jitka Mertlová, Krzysztof Sornat, and Stanisław Szufa.
For more information visit the Computational Social Choice Seminar website.
Are you looking for a postdoc position at the intersection of logic, philosophy and semantics? Would you like to be part of a research team developing new foundations for intensional notions such as property, content, proof, possibility and necessity? If so, come and join the GOOD INTENSIONS project!
Are you looking for a postdoc position at the intersection of logic, philosophy and mathematics? Would you like to be part of a research team developing new foundations for intensional notions such as property, content, proof, possibility and necessity? If so, come and join the GOOD INTENSIONS project!
The position is part of the DFG-funded project Learning Linguistic Inferences and Their Alternatives . The project investigates how language models learn linguistic inferences — including implicatures, presuppositions, implicated presuppositions, free choice, and distributive inferences — and whether training on one inference type facilitates learning of others.
The postdoc will play a central role across the project's work packages, contributing to the computational components of the project as well as to the design and implementation of online behavioral experiments, dataset construction, and data analysis. A successful candidate should hold a PhD degree in Linguistics or a closely related field, have expertise in computational linguistics and be able to engage with both the experimental and computational aspects of the project.