{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T12:25:43Z","timestamp":1750767943735,"version":"3.41.0"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Data and Information Quality"],"published-print":{"date-parts":[[2022,12,31]]},"abstract":"<jats:p>Data have become a fundamental element of the management and productive infrastructures of our society, fuelling digitization of organizational and decision-making processes at an impressive speed. This transition shows lights and shadows, and the \u201cbias in-bias out\u201d problem is one of the most relevant issues, which encompasses technical, ethical, and social perspectives. We address this field of research by investigating how the balance of protected attributes in training data can be used to assess the risk of algorithmic unfairness. We identify four balance measures and test their ability to detect the risk of discriminatory classification by applying them to the training set. The results of this proof of concept show that the indexes can properly detect unfairness of software output. However, we found the choice of the balance measure has a relevant impact on the threshold to consider as risky; further work is necessary to deepen knowledge on this aspect.<\/jats:p>","DOI":"10.1145\/3530787","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T11:54:50Z","timestamp":1659700490000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Detecting Risk of Biased Output with Balance Measures"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0041-1809","authenticated-orcid":false,"given":"Mariachiara","family":"Mecati","sequence":"first","affiliation":[{"name":"Politecnico di Torino, Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2027-3308","authenticated-orcid":false,"given":"Antonio","family":"Vetr\u00f2","sequence":"additional","affiliation":[{"name":"Politecnico di Torino, Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5328-368X","authenticated-orcid":false,"given":"Marco","family":"Torchiano","sequence":"additional","affiliation":[{"name":"Politecnico di Torino, Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Fairness and Machine Learning","author":"Barocas Solon","year":"2019","unstructured":"Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. Retrieved April 7, 2022 from http:\/\/www.fairmlbook.org."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2477899"},{"key":"e_1_3_2_4_2","volume-title":"The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (reprint edition ed.)","author":"Brynjolfsson Erik","year":"2016","unstructured":"Erik Brynjolfsson and Andrew McAfee. 2016. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (reprint edition ed.). W. W. Norton & Company, New York London."},{"key":"e_1_3_2_5_2","unstructured":"Fabio Chiusi Sarah Fischer Nicolas Kayser-Bril and Matthias Spielkamp. 2020. Automating Society Report 2020. Retrieved April 7 2022 from https:\/\/automatingsociety.algorithmwatch.org."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3362121"},{"key":"e_1_3_2_7_2","unstructured":"European Union Agency for Fundamental Rights. 2007. EU Charter of Fundamental Rights - Article 21 - Non-discrimination. Retrieved from https:\/\/fra.europa.eu\/en\/eu-charter\/article\/21-non-discrimination."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/230538.230561"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287600"},{"key":"e_1_3_2_10_2","unstructured":"ISO. 2014. ISO\/IEC 25000:2014 Systems and software engineering \u2014 Systems and software Quality Requirements and Evaluation (SQuaRE) \u2014 Guide to SQuaRE. Retrieved from https:\/\/www.iso.org\/standard\/64764.html."},{"key":"e_1_3_2_11_2","unstructured":"ISO. 2018. ISO 31000:2018 Risk management \u2014 Guidelines. Retrieved from https:\/\/www.iso.org\/cms\/render\/live\/en\/sites\/isoorg\/contents\/data\/standard\/06\/56\/65694.html."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-016-0094-0"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445261"},{"key":"e_1_3_2_14_2","article-title":"RCModel, a Risk Chain Model for Risk Reduction in AI Services","author":"Matsumoto Takashi","year":"2020","unstructured":"Takashi Matsumoto and Arisa Ema. 2020. RCModel, a Risk Chain Model for Risk Reduction in AI Services. http:\/\/ arxiv.org\/abs\/2007.03215. Retrieved from https:\/\/arxiv.org\/abs\/2007.03215.","journal-title":"http:\/\/ arxiv.org\/abs\/2007.03215"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671443"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-012-0295-5"},{"key":"e_1_3_2_17_2","volume-title":"Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (reprint edition ed.)","author":"O\u2019Neil Cathy","year":"2017","unstructured":"Cathy O\u2019Neil. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (reprint edition ed.). Broadway Books, New York."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404193"},{"key":"e_1_3_2_19_2","article-title":"Seizing opportunity in data quality","volume":"29","author":"Redman Thomas C.","year":"2017","unstructured":"Thomas C. Redman. 2017. Seizing opportunity in data quality. MIT Sloan Management Review 29 (2017). Retrieved April 7, 2022 from https:\/\/sloanreview.mit.edu\/article\/seizing-opportunity-in-data-quality\/.","journal-title":"MIT Sloan Management Review"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.5795184"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.giq.2021.101619"}],"container-title":["Journal of Data and Information Quality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530787","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3530787","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:25Z","timestamp":1750183765000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3530787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,23]]},"references-count":20,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12,31]]}},"alternative-id":["10.1145\/3530787"],"URL":"https:\/\/doi.org\/10.1145\/3530787","relation":{},"ISSN":["1936-1955","1936-1963"],"issn-type":[{"type":"print","value":"1936-1955"},{"type":"electronic","value":"1936-1963"}],"subject":[],"published":{"date-parts":[[2022,11,23]]},"assertion":[{"value":"2021-07-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-05","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-11-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}