Summary of Safes: Sequential Privacy and Fairness Enhancing Data Synthesis For Responsible Ai, by Spencer Giddens et al.

SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI

by Spencer Giddens, Fang Liu

First submitted to arxiv on: 14 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper proposes SAFES, a novel approach to simultaneously ensuring data privacy and decision fairness in machine learning tasks. The authors introduce a sequential method that combines differential privacy (DP) data synthesis with fairness-aware data transformation, allowing for tunable control over the privacy-fairness-utility trade-off. The proposed method, which combines AIM’s graphical model-based DP data synthesizer with a popular fairness-aware data pre-processing transformation, is evaluated on two benchmark datasets: Adult and COMPAS. Results show that SAFES-generated synthetic data can achieve significantly improved fairness metrics while incurring reasonable utility loss.
Low	GrooveSquid.com (original content)	Low Difficulty Summary SAFES is a new way to make sure that both our privacy and the fairness of decisions are protected when using artificial intelligence and big data. Right now, most people just focus on one or the other, but this method lets you control how much privacy you want to give up for fairness, or vice versa. The researchers tested this idea with two real-life datasets and found that it can make decisions more fair without sacrificing too much accuracy.