Brent Mittelstadt — accepted AI-conference papers

2026

The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning

Will Hawkins, Kai Rawal, Jonathan Rystrøm, Stratis Tsirtsis, Zihao Fu, Greta Warren +5

ICML 2026poster

Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task. However, prior work has shown that this increase in capability comes with a cost: it can increase a model's tendency to respond to unsafe adversarial prompts, even when fine-tuning w…

Cited by 0 Source ↗Scholar ↗

2024

OxonFair: A Flexible Toolkit for Algorithmic Fairness

Eoin D. Delaney, Zihao Fu, Sandra Wachter, Brent Mittelstadt, Chris Russell

NeurIPS 2024poster

We present OxonFair, a new open source toolkit for enforcing fairness in binary classification. Compared to existing toolkits: (i) We support NLP and Computer Vision classification as well as standard tabular problems. (ii) We support enforcing fairness on validation data, making us robust to a wide…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗