770 citations · 857 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…