Data Science Colloquium

of the ENS

Welcome to the Data Science Colloquium of the ENS.

This colloquium is organized around data sciences in a broad sense with the goal of bringing together researchers with diverse backgrounds (including for instance mathematics, computer science, physics, chemistry and neuroscience) but a common interest in dealing with large scale or high dimensional data.

The colloquium is followed by an open buffet around which participants can meet and discuss collaborations.

These seminars are made possible by the support of the CFM-ENS Chair “Modèles et Sciences des Données”.

You can check the list of the next seminars below and the list of past seminars.

Videos of some of the past seminars are available online.

Organizers

The colloquium is organized by:

Next seminars

18 November 2025, 12h00-13h00 (Paris time), room Amphi Jaurès (29 Rue d'Ulm).
Étienne Ollion (École Polytechnique & CNRS)
Title: Machine Bias. How do generative LLMs Answer Opinion Polls?
Abstract: Generative AI is increasingly presented as a potential substitute for humans, including as human research subjects in various disciplines. Yet there is no scientific consensus on how closely these in-silico clones could represent their human counterparts. While some defend the use of these “synthetic users,” others point towards the biases in the responses provided by the LLMs. Through an experiment using survey questionnaires, we demonstrate that these latter critics are right to be wary of using generative AI to emulate respondents, but probably not for the right reason. Our results i) confirm that to date, models cannot replace research subjects for opinion or attitudinal research; ii) that they display a strong bias on each question (reaching only a small region of social space); and iii) that this bias varies randomly from one question to the other (reaching a different region every time). Besides the two existing competing theses (“representativity” and “social bias”), we propose a third one, which we call call “machine bias”. We detail this term and explore its consequences, for LLM research but also for studies on social biases.

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