841 citations · 1.8k across the 15 of their papers we have counts for
16 papers
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
Junaid Ali, Matthaeus Kleindessner, Florian Wenzel +3
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then syst…
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…
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Yuejiang Liu, Alexandre Alahi, Chris Russell +4
Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited t…
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi +2
We show that deep networks trained to satisfy demographic parity often do so through a form of race or gender awareness, and that the more we force a network to be fair, the more a…
Pairwise Fairness for Ordinal Regression
Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar +2
We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that i…
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…
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…
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…
Counterfactual Fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell +1
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In…
Augmenting recommendation systems using a model of semantically-related terms extracted from user behavior
Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz +5
Common difficulties like the cold-start problem and a lack of sufficient information about users due to their limited interactions have been major challenges for most recommender s…