53 citations · 74 across the 3 of their papers we have counts for
6 papers
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…
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…
Efficient Search for Diverse Coherent Explanations
Chris Russell
This paper proposes new search algorithms for counterfactual explanations based upon mixed integer programming. We are concerned with complex data in which variables may take any v…
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…
Causal Interventions for Fairness
Matt J. Kusner, Chris Russell, Joshua R. Loftus +1
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help pr…
Causal Reasoning for Algorithmic Fairness
Joshua R. Loftus, Chris Russell, Matt J. Kusner +1
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in…