Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces

no code implementations • 17 Aug 2023 • Ahmad-Reza Ehyaei, Kiarash Mohammadi, Amir-Hossein Karimi, Samira Samadi, Golnoosh Farnadi

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.

Adversarial Robustness Fairness +2

FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks

no code implementations • 1 Jun 2022 • Kiarash Mohammadi, Aishwarya Sivaraman, Golnoosh Farnadi

Empirical evaluation on real-world datasets indicates that FETA is not only able to guarantee fairness on-the-fly at prediction time but also is able to train accurate models exhibiting a much higher degree of individual fairness.

Decision Making Fairness +1

Post-processing Counterexample-guided Fairness Guarantees in Neural Networks

no code implementations • AAAI Workshop CLeaR 2022 • Kiarash Mohammadi, Aishwarya Sivaraman, Golnoosh Farnadi

There is an increasing interest in adopting high-capacity machine learning models such as deep neural networks to semi-automate human decisions.

Fairness

Scaling Guarantees for Nearest Counterfactual Explanations

no code implementations • 10 Oct 2020 • Kiarash Mohammadi, Amir-Hossein Karimi, Gilles Barthe, Isabel Valera

Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e. g., loan approval or pretrial bail).

counterfactual Decision Making