@inproceedings{0f78637861794ebdbec9d551e3ad3ff1,
title = "Bias analysis and mitigation in data-driven tools using provenance",
abstract = "Fairness and bias mitigation in data-driven systems has been extensively studied in recent years. In this paper, we suggest a novel approach towards fairness analysis and bias mitigation utilizing the notion of provenance, which was shown to be useful for similar tasks in the context of data and process analyses. We illustrate the idea using a simple use-case demonstrating a scenario of mitigating bias caused by inadequate minority group representation. We conclude with an outline of opportunities and challenges in developing provenance-based solutions for bias analysis and mitigation in data-driven systems.",
author = "Yuval Moskovitch and Jinyang Li and Jagadish, \{H. V.\}",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 14th International Workshop on the Theory and Practice of Provenance, TaPP 2022, held in conjunction with SIGMOD 2022 ; Conference date: 17-06-2022 Through 17-06-2022",
year = "2022",
month = jun,
day = "12",
doi = "10.1145/3530800.3534528",
language = "الإنجليزيّة",
series = "Proceedings of 14th International Workshop on the Theory and Practice of Provenance, TaPP 2022",
pages = "1--4",
booktitle = "Proceedings of 14th International Workshop on the Theory and Practice of Provenance, TaPP 2022",
}