{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-confidence-based-approach-for-balancing","title":"A Confidence-Based Approach for Balancing Fairness and Accuracy","arxiv_id":"1601.05764","date":"2016-01-21","proceeding":null,"authors":["Benjamin Fish","Jeremy Kun","Ádám D. Lelkes"],"abstract":"We study three classical machine learning algorithms in the context of\nalgorithmic fairness: adaptive boosting, support vector machines, and logistic\nregression. Our goal is to maintain the high accuracy of these learning\nalgorithms while reducing the degree to which they discriminate against\nindividuals because of their membership in a protected group.\n  Our first contribution is a method for achieving fairness by shifting the\ndecision boundary for the protected group. The method is based on the theory of\nmargins for boosting. Our method performs comparably to or outperforms previous\nalgorithms in the fairness literature in terms of accuracy and low\ndiscrimination, while simultaneously allowing for a fast and transparent\nquantification of the trade-off between bias and error.\n  Our second contribution addresses the shortcomings of the bias-error\ntrade-off studied in most of the algorithmic fairness literature. We\ndemonstrate that even hopelessly naive modifications of a biased algorithm,\nwhich cannot be reasonably said to be fair, can still achieve low bias and high\naccuracy. To help to distinguish between these naive algorithms and more\nsensible algorithms we propose a new measure of fairness, called resilience to\nrandom bias (RRB). We demonstrate that RRB distinguishes well between our naive\nand sensible fairness algorithms. RRB together with bias and accuracy provides\na more complete picture of the fairness of an algorithm.","url_abs":"http://arxiv.org/abs/1601.05764v1","url_pdf":"http://arxiv.org/pdf/1601.05764v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-confidence-based-approach-for-balancing","repo_url":"https://github.com/j2kun/fkl-SDM16","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1601.05764","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}