{"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/aequitas-a-bias-and-fairness-audit-toolkit","title":"Aequitas: A Bias and Fairness Audit Toolkit","arxiv_id":"1811.05577","date":"2018-11-14","proceeding":null,"authors":["Pedro Saleiro","Benedict Kuester","Loren Hinkson","Jesse London","Abby Stevens","Ari Anisfeld","Kit T. Rodolfa","Rayid Ghani"],"abstract":"Recent work has raised concerns on the risk of unintended bias in AI systems\nbeing used nowadays that can affect individuals unfairly based on race, gender\nor religion, among other possible characteristics. While a lot of bias metrics\nand fairness definitions have been proposed in recent years, there is no\nconsensus on which metric/definition should be used and there are very few\navailable resources to operationalize them. Therefore, despite recent\nawareness, auditing for bias and fairness when developing and deploying AI\nsystems is not yet a standard practice. We present Aequitas, an open source\nbias and fairness audit toolkit that is an intuitive and easy to use addition\nto the machine learning workflow, enabling users to seamlessly test models for\nseveral bias and fairness metrics in relation to multiple population\nsub-groups. Aequitas facilitates informed and equitable decisions around\ndeveloping and deploying algorithmic decision making systems for both data\nscientists, machine learning researchers and policymakers.","url_abs":"http://arxiv.org/abs/1811.05577v2","url_pdf":"http://arxiv.org/pdf/1811.05577v2.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":"aequitas-a-bias-and-fairness-audit-toolkit","repo_url":"https://github.com/dssg/aequitas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"aequitas-a-bias-and-fairness-audit-toolkit","repo_url":"https://github.com/MatDupas/Fairness-audit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1811.05577","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}