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Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML

17 November 2024

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Papers citing "Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML"

9 / 9 papers shown

Title
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges Usman Gohar Zeyu Tang Jialu Wang Kun Zhang Peter Spirtes Yang Liu Lu Cheng FaML 117 4 0 10 Jun 2024
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools Maximilian Schambach Rakshit Naidu Rayid Ghani Kit T. Rodolfa Daniel E. Ho Hoda Heidari FaML 70 17 0 29 Sep 2023
Towards Understanding Fairness and its Composition in Ensemble Machine Learning Usman Gohar Sumon Biswas Hridesh Rajan FaML FedML 67 26 0 08 Dec 2022
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition Samuel Dooley R. Sukthanker John P. Dickerson Colin White Frank Hutter Micah Goldblum CVBM 121 23 0 18 Oct 2022
Predictive Multiplicity in Classification Charles Marx Flavio du Pin Calmon Berk Ustun 136 147 0 14 Sep 2019
Rényi Fair Inference Sina Baharlouei Maher Nouiehed Ahmad Beirami Meisam Razaviyayn FaML 64 67 0 28 Jun 2019
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 384 685 0 17 Feb 2018
A comparative study of fairness-enhancing interventions in machine learning Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian Sonam Choudhary Evan P. Hamilton Derek Roth FaML 109 648 0 13 Feb 2018
Learning to Pivot with Adversarial Networks Gilles Louppe Michael Kagan Kyle Cranmer 76 227 0 03 Nov 2016