What's Sex Got To Do With Fair Machine Learning?

2 June 2020

Papers citing "What's Sex Got To Do With Fair Machine Learning?"

32 / 32 papers shown

Title
Causal Feature Learning in the Social Sciences Jingzhou Huang Jiuyao Lu Alexander Williams Tolbert CML 57 0 0 17 Mar 2025
What is causal about causal models and representations? Frederik Hytting Jørgensen Luigi Gresele S. Weichwald CML 113 0 0 31 Jan 2025
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle Luca Deck Astrid Schomacker Timo Speith Jakob Schöffer Lena Kästner Niklas Kühl 48 4 0 29 Apr 2024
Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms Hilde Weerts Aislinn Kelly-Lyth Reuben Binns Jeremias Adams-Prassl 39 1 0 22 Apr 2024
A New Paradigm for Counterfactual Reasoning in Fairness and Recourse Lucius E.J. Bynum Joshua R. Loftus Julia Stoyanovich 38 3 0 25 Jan 2024
Causal Perception Jose M. Alvarez Salvatore Ruggieri CML 27 0 0 24 Jan 2024
Designing Long-term Group Fair Policies in Dynamical Systems Miriam Rateike Isabel Valera Patrick Forré 40 4 0 21 Nov 2023
A Critical Survey on Fairness Benefits of Explainable AI Luca Deck Jakob Schoeffer Maria De-Arteaga Niklas Kühl 38 11 0 15 Oct 2023
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments James Enouen Tianshu Sun Yan Liu FaML 29 0 0 04 Sep 2023
Insights From Insurance for Fair Machine Learning Christiane Fröhlich Robert C. Williamson FaML 31 6 0 26 Jun 2023
Unfair Utilities and First Steps Towards Improving Them Frederik Hytting Jorgensen S. Weichwald J. Peters FaML 61 0 0 01 Jun 2023
What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem Jose Alvarez Colmenares CML 26 0 0 16 May 2023
Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference Jose M. Alvarez Salvatore Ruggieri 30 13 0 23 Feb 2023
Designing Equitable Algorithms Alex Chohlas-Wood Madison Coots Sharad Goel Julian Nyarko FaML 16 13 0 17 Feb 2023
Simplicity Bias Leads to Amplified Performance Disparities Samuel J. Bell Levent Sagun 24 13 0 13 Dec 2022
Backtracking Counterfactuals Julius von Kügelgen Abdirisak Mohamed Sander Beckers LRM 48 17 0 01 Nov 2022
Lost in Translation: Reimagining the Machine Learning Life Cycle in Education Lydia T. Liu Serena Wang Tolani A. Britton Rediet Abebe AI4Ed 21 1 0 08 Sep 2022
Algorithmic Fairness in Business Analytics: Directions for Research and Practice Maria De-Arteaga Stefan Feuerriegel M. Saar-Tsechansky FaML 22 42 0 22 Jul 2022
Causal Conceptions of Fairness and their Consequences H. Nilforoshan Johann D. Gaebler Ravi Shroff Sharad Goel FaML 142 45 0 12 Jul 2022
Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making Miriam Rateike Ayan Majumdar Olga Mineeva Krishna P. Gummadi Isabel Valera OffRL 37 11 0 10 May 2022
Promises and Challenges of Causality for Ethical Machine Learning Aida Rahmattalabi Alice Xiang FaML CML 128 8 0 26 Jan 2022
The Fairness Field Guide: Perspectives from Social and Formal Sciences Alycia N. Carey Xintao Wu FaML 27 5 0 13 Jan 2022
A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions Brianna Richardson J. Gilbert FaML 29 35 0 10 Dec 2021
A Sociotechnical View of Algorithmic Fairness Mateusz Dolata Stefan Feuerriegel Gerhard Schwabe FaML 32 94 0 27 Sep 2021
Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects Katherine A. Keith Douglas Rice Brendan O'Connor CML 32 3 0 15 Sep 2021
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms Myra Cheng Maria De-Arteaga Lester W. Mackey Adam Tauman Kalai FaML 32 7 0 25 Aug 2021
Disaggregated Interventions to Reduce Inequality Lucius E.J. Bynum Joshua R. Loftus Julia Stoyanovich 42 13 0 01 Jul 2021
When Fair Ranking Meets Uncertain Inference Avijit Ghosh Ritam Dutt Christo Wilson 39 44 0 05 May 2021
A Ranking Approach to Fair Classification Jakob Schoeffer Niklas Kuehl Isabel Valera FaML 29 7 0 08 Feb 2021
Removing biased data to improve fairness and accuracy Sahil Verma Michael Ernst René Just FaML 16 24 0 05 Feb 2021
"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness Mckane Andrus Elena Spitzer Jeffrey Brown Alice Xiang 29 126 0 30 Oct 2020
A Hierarchy of Limitations in Machine Learning M. Malik 20 55 0 12 Feb 2020