Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias

12 March 2024

Papers citing "Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias"

6 / 6 papers shown

Title
Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data Chih-Cheng Rex Yuan Bow-Yaw Wang 40 0 0 30 Apr 2025
The erasure of intensive livestock farming in text-to-image generative AI Kehan Sheng Frank A.M. Tuyttens Marina A.G. von Keyserlingk 62 0 0 27 Feb 2025
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model Lifan Jiang Zhihui Wang Siqi Yin Guangxiao Ma Peng Zhang Boxi Wu DiffM 51 0 0 28 Aug 2024
Data Feedback Loops: Model-driven Amplification of Dataset Biases Rohan Taori Tatsunori B. Hashimoto 64 42 0 08 Sep 2022
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 24 57 0 23 Sep 2021
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,079 0 24 Oct 2016