Verifying Individual Fairness in Machine Learning Models

Verifying Individual Fairness in Machine Learning Models

21 June 2020

Philips George John

Deepak Vijaykeerthy

Diptikalyan Saha

Papers citing "Verifying Individual Fairness in Machine Learning Models"

12 / 12 papers shown

Title
FairSense: Long-Term Fairness Analysis of ML-Enabled Systems Yining She Sumon Biswas Christian Kastner Eunsuk Kang 42 0 0 03 Jan 2025
Software Doping Analysis for Human Oversight Sebastian Biewer Kevin Baum Sarah Sterz Holger Hermanns Sven Hetmank Markus Langer Anne Lauber-Rönsberg Franz Lehr 20 4 0 11 Aug 2023
To be Robust and to be Fair: Aligning Fairness with Robustness Junyi Chai Xiaoqian Wang 47 2 0 31 Mar 2023
Explainable Global Fairness Verification of Tree-Based Classifiers Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Federico Marcuzzi 24 2 0 27 Sep 2022
Verifying Fairness in Quantum Machine Learning J. Guan Wang Fang Mingsheng Ying FaML 16 11 0 22 Jul 2022
CertiFair: A Framework for Certified Global Fairness of Neural Networks Haitham Khedr Yasser Shoukry FedML 18 19 0 20 May 2022
Individual Fairness Guarantees for Neural Networks Elias Benussi A. Patané Matthew Wicker Luca Laurenti Marta Kwiatkowska University of Oxford 20 21 0 11 May 2022
Latent Space Smoothing for Individually Fair Representations Momchil Peychev Anian Ruoss Mislav Balunović Maximilian Baader Martin Vechev FaML 36 19 0 26 Nov 2021
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models Yunhan Zheng Shenhao Wang Jinhuan Zhao HAI 21 27 0 25 Sep 2021
Fair Normalizing Flows Mislav Balunović Anian Ruoss Martin Vechev AAML 13 36 0 10 Jun 2021
Learning Certified Individually Fair Representations Anian Ruoss Mislav Balunović Marc Fischer Martin Vechev FaML 15 92 0 24 Feb 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 228 1,835 0 03 Feb 2017