Matching Consumer Fairness Objectives & Strategies for RecSys

6 September 2022

Papers citing "Matching Consumer Fairness Objectives & Strategies for RecSys"

6 / 6 papers shown

Title
The Impossibility of Fair LLMs Jacy Reese Anthis Kristian Lum Michael Ekstrand Avi Feller Alexander D’Amour Chenhao Tan FaML 45 11 0 28 May 2024
Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics Elisa Kreiss Cynthia L. Bennett Shayan Hooshmand E. Zelikman Meredith Ringel Morris Christopher Potts 48 27 0 21 May 2022
User-oriented Fairness in Recommendation Yunqi Li H. Chen Zuohui Fu Yingqiang Ge Yongfeng Zhang FaML 102 230 0 21 Apr 2021
Achieving User-Side Fairness in Contextual Bandits Wen Huang Kevin Labille Xintao Wu Dongwon Lee Neil T. Heffernan FaML 84 18 0 22 Oct 2020
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 192 743 0 13 Dec 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 233 674 0 17 Feb 2018