Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To
Reduce Model Bias

Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias

20 October 2021

Papers citing "Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias"

9 / 9 papers shown

Title
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models Sharat Agarwal 16 0 0 04 Nov 2024
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models Moreno DÍncà E. Peruzzo Massimiliano Mancini Xingqian Xu Humphrey Shi N. Sebe 39 0 0 29 Aug 2024
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes Yusuke Hirota Jerone T. A. Andrews Dora Zhao Orestis Papakyriakopoulos Apostolos Modas Yuta Nakashima Alice Xiang 36 4 0 04 Jul 2024
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models Moreno DÍncà E. Peruzzo Massimiliano Mancini Dejia Xu Vidit Goel Xingqian Xu Zhangyang Wang Humphrey Shi N. Sebe 53 31 0 11 Apr 2024
Improving Fairness using Vision-Language Driven Image Augmentation Moreno DÍncà Christos Tzelepis Ioannis Patras N. Sebe 32 12 0 02 Nov 2023
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play J. Liu Krishnamurthy Dvijotham Jihyeon Janel Lee Quan Yuan Martin Strobel Balaji Lakshminarayanan Deepak Ramachandran 21 5 0 11 Feb 2023
Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey Otávio Parraga Martin D. Móre C. M. Oliveira Nathan Gavenski L. S. Kupssinskü Adilson Medronha L. V. Moura Gabriel S. Simões Rodrigo C. Barros 34 11 0 10 Nov 2022
Men Also Do Laundry: Multi-Attribute Bias Amplification Dora Zhao Jerone T. A. Andrews Alice Xiang FaML 28 20 0 21 Oct 2022
Fairness and Bias in Robot Learning Laura Londoño Juana Valeria Hurtado Nora Hertz P. Kellmeyer S. Voeneky Abhinav Valada FaML 21 9 0 07 Jul 2022