841 citations · 1.8k across the 15 of their papers we have counts for
4 papers · 1 filter
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes
Herman Yau, Chris Russell, Simon Hadfield
We present a novel form of explanation for Reinforcement Learning, based around the notion of intended outcome. These explanations describe the outcome an agent is trying to achiev…
Active Sampling for Min-Max Fairness
Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner +3
We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimiza…
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…
Adequate and fair explanations
Nicholas Asher, Soumya Paul, Chris Russell
Explaining sophisticated machine-learning based systems is an important issue at the foundations of AI. Recent efforts have shown various methods for providing explanations. These…