2024-04-18-abroshan24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Imposing Fairness Constraints in Synthetic Data Generation	In several real-world applications (e.g., online advertising, item recommendations, etc.) it may not be possible to release and share the real dataset due to privacy concerns. As a result, synthetic data generation (SDG) has emerged as a promising solution for data sharing. While the main goal of private SDG is to create a dataset that preserves the privacy of individuals contributing to the dataset, the use of synthetic data also creates an opportunity to improve fairness. Since there often exist historical biases in the datasets, using the original real data for training can lead to an unfair model. Using synthetic data, we can attempt to remove such biases from the dataset before releasing the data. In this work, we formalize the definition of fairness in synthetic data generation and provide a general framework to achieve fairness. Then we consider two notions of counterfactual fairness and information filtering fairness and show how our framework can be used for these definitions.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	abroshan24a	0	Imposing Fairness Constraints in Synthetic Data Generation	2269	2277	2269-2277	2269	false	Abroshan, Mahed and Elliott, Andrew and Mahdi Khalili, Mohammad	given family Mahed Abroshan given family Andrew Elliott given family Mohammad Mahdi Khalili	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/abroshan24a/abroshan24a.pdf