{"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/fairness-under-unawareness-assessing","title":"Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved","arxiv_id":"1811.11154","date":"2018-11-27","proceeding":null,"authors":["Jiahao Chen","Nathan Kallus","Xiaojie Mao","Geoffry Svacha","Madeleine Udell"],"abstract":"Assessing the fairness of a decision making system with respect to a\nprotected class, such as gender or race, is challenging when class membership\nlabels are unavailable. Probabilistic models for predicting the protected class\nbased on observable proxies, such as surname and geolocation for race, are\nsometimes used to impute these missing labels for compliance assessments.\nEmpirically, these methods are observed to exaggerate disparities, but the\nreason why is unknown. In this paper, we decompose the biases in estimating\noutcome disparity via threshold-based imputation into multiple interpretable\nbias sources, allowing us to explain when over- or underestimation occurs. We\nalso propose an alternative weighted estimator that uses soft classification,\nand show that its bias arises simply from the conditional covariance of the\noutcome with the true class membership. Finally, we illustrate our results with\nnumerical simulations and a public dataset of mortgage applications, using\ngeolocation as a proxy for race. We confirm that the bias of threshold-based\nimputation is generally upward, but its magnitude varies strongly with the\nthreshold chosen. Our new weighted estimator tends to have a negative bias that\nis much simpler to analyze and reason about.","url_abs":"http://arxiv.org/abs/1811.11154v1","url_pdf":"http://arxiv.org/pdf/1811.11154v1.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":"fairness-under-unawareness-assessing","repo_url":"https://github.com/KrishnaRJ422/Explainability_Bias_Fairness-in-AI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"missing-labels","task_name":"Missing Labels"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.11154","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}