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Found 4 papers, 1 papers with code
Collective Counterfactual Explanations via Optimal Transport
To address these issues, our work proposes a collective approach for formulating counterfactual explanations, with an emphasis on utilizing the current density of the individuals to inform the recommended actions.
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation.
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes.
Robustness Implies Fairness in Causal Algorithmic Recourse
Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations to empower beneficiaries to achieve a more favorable outcome.
