6 papers
Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
Kaivalya Rawal, Daria Onitiu, Brent Mittelstadt +2
Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision. We propose ''Rule of Thumb'' (RoT) explanati…
The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
Will Hawkins, Kaivalya Rawal, Jonathan Rystrøm +8
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…
The Fallacy of Sustainable Generative AI: Limitations in EU Environmental Regulation of Data Centres and Paths Forward
Daria Onitiu, Sandra Wachter, Brent Mittelstadt
In the age of Artificial Intelligence (AI), Large Language Models, Generative AI and larger frontier AI models, data centres create a significant environmental burden on electricit…
AI-Mediated Communication Can Steer Collective Opinion
Stratis Tsirtsis, Kai Rawal, Chris Russell +2
Generative artificial intelligence (AI) is increasingly integrated into the online platforms where humans exchange opinions; large language models (LLMs) now polish users' posts on…
Evaluating the Ability of Explanations to Disambiguate Models in a Rashomon Set
Kaivalya Rawal, Eoin Delaney, Zihao Fu +2
Explainable artificial intelligence (XAI) is concerned with producing explanations indicating the inner workings of models. For a Rashomon set of similarly performing models, expla…
OxonFair: A Flexible Toolkit for Algorithmic Fairness
Eoin Delaney, Zihao Fu, Sandra Wachter +2
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…