3 citations · 3 across the 4 of their papers we have counts for
5 papers
Recommender system in X inadvertently profiles ideological positions of users
Paul Bouchaud, Pedro Ramaciotti
Several data protection laws restrict processing that reveals political opinions, irrespective of the controller's intent. Whether recommender systems do so as a by-product of opti…
Linear socio-demographic representations emerge in Large Language Models from indirect cues
Paul Bouchaud, Pedro Ramaciotti
We investigate how LLMs encode sociodemographic attributes of human conversational partners inferred from indirect cues such as names and occupations. We show that LLMs develop lin…
Web Crawler Restrictions, AI Training Datasets \& Political Biases
Paul Bouchaud, Pedro Ramaciotti
Large language models rely on web-scraped text for training; concurrently, content creators are increasingly blocking AI crawlers to retain control over their data. We analyze craw…
Community Notes undermoderate polarizing content by design creating risks in electoral processes
Paul Bouchaud, Pedro Ramaciotti
Community Notes (CNs) of X enables users to collaboratively moderate misleading content. To resolve conflicting moderation, CNs infers a latent ideological dimension and selects no…
Can Few Lines of Code Change Society ? Beyond fack-checking and moderation : how recommender systems toxifies social networking sites
David Chavalarias, Paul Bouchaud, Maziyar Panahi
As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social…