AIM: Attributing, Interpreting, Mitigating Data Unfairness

13 June 2024

Papers citing "AIM: Attributing, Interpreting, Mitigating Data Unfairness"

6 / 6 papers shown

Title
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics Zhichen Zeng Ruizhong Qiu Wenxuan Bao Tianxin Wei Xiao Lin Yuchen Yan Tarek Abdelzaher Jiawei Han Hanghang Tong OOD AI4CE 12 0 0 19 May 2025
How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark Ruizhong Qiu Weiliang Will Zeng Hanghang Tong James Ezick Christopher Lott 90 16 0 20 Feb 2025
SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence Zhining Liu Rana Ali Amjad Ravinarayana Adkathimar Tianxin Wei Hanghang Tong RALM HILM 105 0 0 12 Feb 2025
Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization Ruizhong Qiu Hanghang Tong 44 3 0 27 May 2024
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 341 4,230 0 23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,091 0 24 Oct 2016