770 citations · 857 across the 11 of their papers we have counts for
12 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…
Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It
Philipp Hacker
As generative Artificial Intelligence (genAI) technologies proliferate across sectors, they offer significant benefits but also risk exacerbating discrimination. This chapter explo…
The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
Brent Mittelstadt, Sandra Wachter, Chris Russell
In recent years fairness in machine learning (ML) has emerged as a highly active area of research and development. Most define fairness in simple terms, where fairness means reduci…
The Theory of Artificial Immutability: Protecting Algorithmic Groups Under Anti-Discrimination Law
Sandra Wachter
Artificial Intelligence (AI) is increasingly used to make important decisions about people. While issues of AI bias and proxy discrimination are well explored, less focus has been…
Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
Sandra Wachter, Brent Mittelstadt, Chris Russell
This article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. First, we review the evidential require…
Explaining Explanations in AI
Brent Mittelstadt, Chris Russell, Sandra Wachter
Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models…
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, Chris Russell
There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to expla…