activity
20182023
most citedFairness in Machine Learning

303 citations · 593 across the 10 of their papers we have counts for

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Showing cs.LGShow all

8 papers · 1 filter

cs.LG2023

Additive Causal Bandits with Unknown Graph

Alan Malek, Virginia Aglietti, Silvia Chiappa

We explore algorithms to select actions in the causal bandit setting where the learner can choose to intervene on a set of random variables related by a causal graph, and the learn…

cs.LG20223 cited

Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness

Carolyn Ashurst, Ryan Carey, Silvia Chiappa +1

In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects. We refer to this as int…

cs.LG20211 cited

Statistical discrimination in learning agents

Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao +9

Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One pr…

cs.LG2021

Prequential MDL for Causal Structure Learning with Neural Networks

Jorg Bornschein, Silvia Chiappa, Alan Malek +1

Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology. We show that the prequential min…

cs.LG20214 cited

Fairness with Continuous Optimal Transport

Silvia Chiappa, Aldo Pacchiano

Whilst optimal transport (OT) is increasingly being recognized as a powerful and flexible approach for dealing with fairness issues, current OT fairness methods are confined to the…

cs.LG2020303 cited

Fairness in Machine Learning

Luca Oneto, Silvia Chiappa

Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that ma…