626 citations · 1.7k across the 31 of their papers we have counts for
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On the Fairness of Causal Algorithmic Recourse
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt +3
Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individua…
Scaling Guarantees for Nearest Counterfactual Explanations
Kiarash Mohammadi, Amir-Hossein Karimi, Gilles Barthe +1
Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e.g., loan approval or pretrial bail…
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf +1
Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addi…
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf +1
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships betwe…
Lipschitz standardization for multivariate learning
Adrián Javaloy, Isabel Valera
Probabilistic learning is increasingly being tackled as an optimization problem, with gradient-based approaches as predominant methods. When modelling multivariate likelihoods, a u…
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera
As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at…