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Social Science and Generative AI: Inquiries, Instruments, Consequences

This one-day conference will reflexively examine generative AI as sociotechnical devices, discussing its effects on scientific work and its epistemological and social implications. The presentations will make a particular point of considering the epistemological diversity of the social sciences by giving equal attention to quantitative, qualitative, and participatory approaches.

Event, Conference

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Amphi Simone Veil, 28 rue des Saints-Pères, 75007 Paris

Generative AI is gaining increasing attention in the social sciences, serving both as a research focus and a methodological instrument. These rapidly advancing technologies enable the creation of synthetic data, the implementation of large-scale experiments, and the analysis of interviews and ethnographic narratives. Conversely, researchers utilize these technologies to explore their social risks, transformative effects, and political implications.

These emerging practices raise epistemological and ethical questions. How do novel research tools interact with existing approaches to studying social life? Confronted with the opacity of the processes involved in such technologies, what options do researchers have: reclaim control, experiment with, or develop alternative tools from scratch? What changes occur in the relationship between social research and societies concerning positionality in fieldwork, the role of civil society, and the impact on governance institutions?

This conference will explore these critical issues by bringing together prominent international scholars who engage with generative AI through empirical research. They will share their perspectives to explore the interconnected roles of generative AI in academia and society, which are continually linked in its development.

Program

  • 8:30 : Welcome & Coffee
  • 9:00 - 9:15 : Foreword by Luis Vassy (Director of Sciences Po)
  • 9:15 - 9:30 : Introduction by Sylvain Parasie (Director of the medialab)

Panel 1: Data and Models

  • 9:30 - 10:20 :  “AI Errors and the Illusion of Artificial Life: Ethnographic Encounters with Generative AI” by Veronica Barassi (Professor in Media and Communication Studies at the University of St. Gallen)
  • 10:20 - 11:10 : “Preventing Language Models Weaponization: What if their Training Data were to be Compromised?” by Djamé Seddah (Researcher at INRIA Paris’ Almanach)
  • 11:10 - 11:30 : Break
  • 11:30 - 12:20 : “Machine Bias. How Do Generative Language Models Answer Opinion Polls?” by Etienne Ollion (Director of Research at CNRS)

Panel 2: Methods and Approaches

  • 2:00 - 2:50 : “From Stories to Sonnets: Data-Centered NLP for Creative Works” by Maria Antoniak (Assistant Professor in Computer Science at the University of Colorado Boulder)
  • 2:50 - 3:40 : “‘Conversing’ with Large Language Models: Enhancing Qualitative Data Using LLMs” by Adam Hayes (Professor of Sociology at the University of Lucerne)

Panel 3: Public Value and Consequences 

  • 4:00 - 4:50 : “Conditional Computing: Reimagining Europe’s Role in the AI Race” by Fabian Ferrari (Assistant Professor in Cultural AI at the Utrecht University)
  • 4:50 - 5:40 : “Language, Culture, Archive: Provocations from the Humanities for Generative AI Research” by Lauren Frederica Klein (Associate Professor in the Departments of Quantitative Theory and Methods and English at Emory University)
  • 5:40 - 6:00 : Conclusive words

Abstracts

  • Veronica Barassi : “AI Errors and the Illusion of Artificial Life: Ethnographic Encounters with Generative AI”

In this talk, I interrogate how ethnographic engagement with AI reveals the instability of machine "truths" and the social fictions we create around bot-generated knowledge. I ask: What does it mean when we believe we can learn from entities that cannot learn in the human sense? How do we make sense of what bots "tell" us, when their narratives are shaped by probabilistic association rather than contextual understanding? Bringing together reflection from The Human Error Project (2020-2024) with the ones gathered through a more recent ethnographic study on abusive digital bots and the growing terrain of Generative AI Violence, I discuss the role of gnerative AI as a destabilizing object for the social sciences and the reflect on the methods we can use to conduct meaningful ethnographic research.

  • Etienne Ollion : “Machine Bias. How Do Generative Language Models Answer Opinion Polls?”

Generative artificial intelligence (AI) is increasingly presented as a potential substitute for humans, including as research subjects. However, there is no scientific consensus on how closely these in silico clones can emulate survey respondents. While some defend the use of these “synthetic users,” others point toward social biases in the responses provided by large language models (LLMs). In this article, we demonstrate that these critics are right to be wary of using generative AI to emulate respondents, but probably not for the right reasons. Our results show (i) that to date, models cannot replace research subjects for opinion or attitudinal research; (ii) that they display a strong bias and a low variance on each topic; and (iii) that this bias randomly varies from one topic to the next. We label this pattern “machine bias,” a concept we define, and whose consequences for LLM-based research we further explore.

  • Maria Antoniak : “From Stories to Sonnets: Data-Centered NLP for Creative Works”

In this talk, I'll share two recent studies that use natural language processing (NLP) techniques, such as LLMs, to model creative works like stories and poetry. In the first part of the talk, I'll discuss NLP approaches for story detection and analysis, focusing on how NLP methods can help us study storytelling at large scales and across diverse contexts. In the second part, I'll discuss the poetic capabilities of large language models (LLMs), focusing on audits of the vast pretraining datasets used to build these models. Both studies will highlight the challenges in creating open evaluation datasets for creative works, the importance of interdisciplinary collaboration, and the new kinds of tools and resources required for such studies.

  • Adam Hayes : “‘Conversing’ with Large Language Models: Enhancing Qualitative Data Using LLMs”

Large Language Models (LLMs) are revolutionizing how qualitative researchers can work with textual data. Rather than relying only on codebooks or manual line-by-line analysis, scholars can now “converse” with their materials by asking targeted questions, probing for contextual insights, and refining theoretical connections. This dialogue-like process speeds up traditional tasks—transcription, coding, theme identification—while sparking broader possibilities for exploration. Researchers prompt the LLM to surface recurring patterns, detect subtle shifts in tone, or suggest new interpretive angles. The LLM becomes an active partner: it quickly identifies connections that might take days or weeks of manual work, yet the scholar remains responsible for selecting which prompts to use, verifying the outputs, and situating them within appropriate conceptual frameworks.

  • Fabian Ferrari : “Conditional Computing: Reimagining Europe’s Role in the AI Race”

The global AI race is often framed in terms of investment and compute power: a competition for deeper pockets and larger models. But the sheer scale of investments by the US and China far surpasses those of the EU, making it unrealistic to compete solely on monetary terms. This talk asks: What if the EU’s competitive advantage lies in values, not in budgets? It begins by outlining Europe’s spending disadvantage in AI. It then argues that focusing only on investments misses how Big Tech’s AI empires are also built on a philosophy of accelerationism and state-as-enterprise thinking. Finally, it proposes an alternative approach for the EU: conditional computing, a new governance framework that attaches public value conditions to AI infrastructure governance.

  • Lauren Frederica Klein : “Language, Culture, Archive: Provocations from the Humanities for Generative AI Research”

This talk derives from a working paper, "Provocations from the Humanities for Generative AI Research,” which presents a set of provocations for  considering the uses, impact, and harms of generative AI from the perspective of humanities researchers. In the paper, we provide a working definition of humanities research, summarize some of its most salient theories and methods, and apply these theories and methods to the current landscape of generative AI. We draw from foundational work in critical data studies, along with relevant humanities scholarship, in order to elaborate eight claims with broad applicability to current conversations about generative AI. In this talk, I will focus on three of these provocations: 1) Models make words, but people make meaning; 2) Generative AI requires an expanded definition of culture; and 3) Generative AI can never be representative; in order to show how core ideas from the humanities about language, culture, and the archive, can destabilize some of the current foundations of generative AI research and suggest alternative paths forward.

Practical information

Date : June 5th 2025

Hour : 8:30 AM - 6:00 PM

Location : Amphithéâtre Simone Veil, 28 rue des Saints-Pères, 75007 Paris

Language : english

The conference is open to the public, with mandatory registration.