Sofia is a DPhil student in Social Data Science at the OII. Her research focuses on algorithmic fairness, machine learning, and interactive data visualisation. Sofia’s research on algorithmic fairness has been published in academic journals and conference proceedings, including AI & Society and ACM FAccT.
Sofia holds an undergraduate degree in Computer Science and Public Policy from the University of Glasgow and a master’s in Social Data Science from the OII. She has held multiple research positions, including as a visiting scholar in Computer Science at the American University, as an intern at the Urban Big Data Centre, and as a research assistant for the Synthetic Society Lab.
Algorithmic Fairness, Machine Learning, Interactive Data Visualisation, Recommendation Systems
With Franziska Sofia Hafner
Schwartzman Centre
A new study from Oxford Internet Institute researchers finds that training chatbots to sound warmer makes them up to 30% less accurate, and 40% more likely to validate users' false beliefs.
15 June 2026
Researchers and DPhil students from the Oxford Internet Institute are set to attend the Association of Computing Machinery (ACM) Conference on Fairness, Accountability and Transparency (FAccT) in Montréal, from 25-28 June 2026.
29 April 2026
New Oxford research shows that training chatbots to sound warmer makes them up to 30% less accurate, and 40% more likely to validate users' false beliefs.
25 November 2025
Researchers from the Oxford Internet Institute at the University of Oxford will be at NeurIPS 2025 in San Diego from 1- 7 December, 2025, contributing to one of the world’s leading AI conferences.
20 June 2025
OII researchers are set to attend the Association of Computing Machinery (ACM) Conference on Fairness, Accountability and Transparency (FAccT) 2025.
franceinfo, 06 June 2026
Sans précautions particulières, les modèles d'intelligence artificielle entraînés pour être plus empathiques généreraient davantage d'erreurs et de recommandations inappropriées.
PsyPost, 29 May 2026
Artificial intelligence models trained to act friendly and empathetic tend to sacrifice factual accuracy and become more likely to agree with a user’s incorrect beliefs, according to new research.
The Verge, 29 April 2026
The researchers found AI chatbots trained to be warmer were significantly more likely to make factual errors and agree with false beliefs than the originals.
