{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:11:11Z","timestamp":1780614671654,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"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":[],"published-print":{"date-parts":[[2022,7,26]]},"DOI":"10.1145\/3514094.3534137","type":"proceedings-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T22:25:13Z","timestamp":1658960713000},"page":"265-275","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["An Ontology for Fairness Metrics"],"prefix":"10.1145","author":[{"given":"Jade S.","family":"Franklin","sequence":"first","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karan","family":"Bhanot","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Ghalwash","sequence":"additional","affiliation":[{"name":"IBM Research, Yorktown, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin P.","family":"Bennett","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamie","family":"McCusker","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deborah L.","family":"McGuinness","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing  Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing"},{"key":"e_1_3_2_1_2_1","unstructured":"Solon Barocas Moritz Hardt and Arvind Narayanan. 2017. Fairness in machine learning. NIPS Tutorial. https:\/\/fairmlbook.org\/tutorial1.html  Solon Barocas Moritz Hardt and Arvind Narayanan. 2017. Fairness in machine learning. NIPS Tutorial. https:\/\/fairmlbook.org\/tutorial1.html"},{"key":"e_1_3_2_1_3_1","volume-title":"OWL Web Ontology Language Reference","author":"Bechhofer Sean","year":"2004","unstructured":"Sean Bechhofer , Frank van Harmelen , Jim Hendler , Ian Horrocks , Deborah L. McGuinness , Peter F. Patel-Schneider , and Lynn Andrea Stein . 2004. OWL Web Ontology Language Reference . World Wide Web Consortium . https:\/\/www.w3.org\/TR\/ 2004 \/REC-owl-ref-20040210\/ Sean Bechhofer, Frank van Harmelen, Jim Hendler, Ian Horrocks, Deborah L. McGuinness, Peter F. Patel-Schneider, and Lynn Andrea Stein. 2004. OWL Web Ontology Language Reference. World Wide Web Consortium. https:\/\/www.w3.org\/TR\/2004\/REC-owl-ref-20040210\/"},{"key":"e_1_3_2_1_4_1","volume-title":"John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang.","author":"Bellamy Rachel K. E.","year":"2018","unstructured":"Rachel K. E. Bellamy , Kuntal Dey , Michael Hind , Samuel C. Hoffman , Stephanie Houde , Kalapriya Kannan , Pranay Lohia , Jacquelyn Martino , Sameep Mehta , Aleksandra Mojsilovic , Seema Nagar , Karthikeyan Natesan Ramamurthy , John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018 . AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias . arxiv: 1810.01943 [cs.AI] Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. arxiv: 1810.01943 [cs.AI]"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14428\/esann\/2021.ES2021-108"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/e23091165"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00158"},{"key":"e_1_3_2_1_8_1","volume-title":"TED Conferences. Video. https:\/\/www.ted.com\/talks\/joy_buolamwini_how_i_m_fighting_bias_in_algorithms","author":"Buolamwini Joy A.","year":"2016","unstructured":"Joy A. Buolamwini . 2016 . How I'm fighting bias in algorithms . TED Conferences. Video. https:\/\/www.ted.com\/talks\/joy_buolamwini_how_i_m_fighting_bias_in_algorithms Joy A. Buolamwini. 2016. How I'm fighting bias in algorithms. TED Conferences. Video. https:\/\/www.ted.com\/talks\/joy_buolamwini_how_i_m_fighting_bias_in_algorithms"},{"key":"e_1_3_2_1_10_1","unstructured":"Simon Caton and Christian Haas. 2020. Fairness in Machine Learning: A Survey. [arXiv]2010.04053 https:\/\/arxiv.org\/abs\/2010.04053  Simon Caton and Christian Haas. 2020. Fairness in Machine Learning: A Survey. [arXiv]2010.04053 https:\/\/arxiv.org\/abs\/2010.04053"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445879"},{"key":"e_1_3_2_1_12_1","volume-title":"Ethical Implications of AI Bias as a Result of Workforce Gender Imbalance. Final Report for UniBank (Teachers Mutual Bank Limited). CIS & The Policy Lab","author":"Cheong Marc","year":"1862","unstructured":"Marc Cheong , Reeva Lederman , Aidan McLoughney , Sheilla Njoto , Leah Ruppanner , and Anthony Wirth . 2020. Ethical Implications of AI Bias as a Result of Workforce Gender Imbalance. Final Report for UniBank (Teachers Mutual Bank Limited). CIS & The Policy Lab , University of Melbourne . https:\/\/about.unimelb.edu.au\/__data\/assets\/pdf_file\/0024\/ 1862 52\/NEW-RESEARCH-REPORT-Ethical-Implications-of-AI-Bias-as-a-Result-of-Workforce-Gender-Imbalance-UniMelb,-UniBank.pdf Marc Cheong, Reeva Lederman, Aidan McLoughney, Sheilla Njoto, Leah Ruppanner, and Anthony Wirth. 2020. Ethical Implications of AI Bias as a Result of Workforce Gender Imbalance. Final Report for UniBank (Teachers Mutual Bank Limited). CIS & The Policy Lab, University of Melbourne. https:\/\/about.unimelb.edu.au\/__data\/assets\/pdf_file\/0024\/186252\/NEW-RESEARCH-REPORT-Ethical-Implications-of-AI-Bias-as-a-Result-of-Workforce-Gender-Imbalance-UniMelb,-UniBank.pdf"},{"key":"e_1_3_2_1_13_1","unstructured":"Richard Cyganiak David Wood and Markus Lanthaler (Eds.). 2014. RDF 1.1 Concepts and Abstract Syntax. World Wide Web Consortium. http:\/\/www.w3.org\/TR\/2014\/REC-rdf11-concepts-20140225\/  Richard Cyganiak David Wood and Markus Lanthaler (Eds.). 2014. RDF 1.1 Concepts and Abstract Syntax. World Wide Web Consortium. http:\/\/www.w3.org\/TR\/2014\/REC-rdf11-concepts-20140225\/"},{"key":"e_1_3_2_1_14_1","volume-title":"Amazon scraps secret AI recruiting tool that showed bias against women","author":"Dastin Jeffrey","unstructured":"Jeffrey Dastin . 2018. Amazon scraps secret AI recruiting tool that showed bias against women . Reuters . https:\/\/www.reuters.com\/article\/us-amazon-com-jobs-automation-insight\/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G Jeffrey Dastin. 2018. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https:\/\/www.reuters.com\/article\/us-amazon-com-jobs-automation-insight\/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/2041-1480-5-14"},{"key":"e_1_3_2_1_16_1","volume-title":"Fairness Metrics: A Comparative Analysis. In 2020 IEEE International Conference on Big Data (Big Data). IEEE","author":"Garg Pratyush","year":"2020","unstructured":"Pratyush Garg , John Villasenor , and Virginia Foggo . 2020 . Fairness Metrics: A Comparative Analysis. In 2020 IEEE International Conference on Big Data (Big Data). IEEE , Atlanta, GA, USA, 3662--3666. https:\/\/doi.org\/10.1109\/BigData50022. 2020.9378025 10.1109\/BigData50022.2020.9378025 Pratyush Garg, John Villasenor, and Virginia Foggo. 2020. Fairness Metrics: A Comparative Analysis. In 2020 IEEE International Conference on Big Data (Big Data). IEEE, Atlanta, GA, USA, 3662--3666. https:\/\/doi.org\/10.1109\/BigData50022.2020.9378025"},{"key":"e_1_3_2_1_17_1","unstructured":"Alejandra Gonzalez-Beltran Philippe Rocca-Serra Orlaith Burke and Susanna-Assunta Sansone. 2012. STATO: an Ontology of Statistical Methods. ISA-tools. http:\/\/stato-ontology.org\/  Alejandra Gonzalez-Beltran Philippe Rocca-Serra Orlaith Burke and Susanna-Assunta Sansone. 2012. STATO: an Ontology of Statistical Methods. ISA-tools. http:\/\/stato-ontology.org\/"},{"key":"e_1_3_2_1_18_1","unstructured":"Aman Gupta Deepak Bhatt and Anubha Pandey. 2021. Transitioning from Real to Synthetic data: Quantifying the bias in model. arxiv: 2105.04144 [cs.LG]  Aman Gupta Deepak Bhatt and Anubha Pandey. 2021. Transitioning from Real to Synthetic data: Quantifying the bias in model. arxiv: 2105.04144 [cs.LG]"},{"key":"e_1_3_2_1_19_1","unstructured":"Steve Harris and Andy Seaborne (Eds.). 2013. SPARQL 1.1 Query Language. World Wide Web Consortium. https:\/\/www.w3.org\/TR\/2013\/REC-sparql11-query-20130321\/  Steve Harris and Andy Seaborne (Eds.). 2013. SPARQL 1.1 Query Language. World Wide Web Consortium. https:\/\/www.w3.org\/TR\/2013\/REC-sparql11-query-20130321\/"},{"key":"e_1_3_2_1_20_1","volume-title":"50 Years of Test (Un)fairness: Lessons for Machine Learning. CoRR","author":"Hutchinson Ben","year":"2018","unstructured":"Ben Hutchinson and Margaret Mitchell . 2018. 50 Years of Test (Un)fairness: Lessons for Machine Learning. CoRR , Vol. abs\/ 1811 .10104 ( 2018 ). showeprint[arXiv]1811.10104 http:\/\/arxiv.org\/abs\/1811.10104 Ben Hutchinson and Margaret Mitchell. 2018. 50 Years of Test (Un)fairness: Lessons for Machine Learning. CoRR, Vol. abs\/1811.10104 (2018). showeprint[arXiv]1811.10104 http:\/\/arxiv.org\/abs\/1811.10104"},{"key":"e_1_3_2_1_21_1","unstructured":"Plotly Technologies Inc. 2015. Collaborative data science. Montreal QC. https:\/\/plot.ly  Plotly Technologies Inc. 2015. Collaborative data science. Montreal QC. https:\/\/plot.ly"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/NIR52917.2021.9666131"},{"key":"e_1_3_2_1_23_1","volume-title":"Model-Agnostic Characterization of Fairness Trade-offs. CoRR","author":"Kim Joon Sik","year":"2020","unstructured":"Joon Sik Kim , Jiahao Chen , and Ameet Talwalkar . 2020. Model-Agnostic Characterization of Fairness Trade-offs. CoRR , Vol. abs\/ 2004 .03424 ( 2020 ). [arXiv]2004.03424 https:\/\/arxiv.org\/abs\/2004.03424 Joon Sik Kim, Jiahao Chen, and Ameet Talwalkar. 2020. Model-Agnostic Characterization of Fairness Trade-offs. CoRR, Vol. abs\/2004.03424 (2020). [arXiv]2004.03424 https:\/\/arxiv.org\/abs\/2004.03424"},{"key":"e_1_3_2_1_24_1","volume-title":"Mohammad","author":"Kiritchenko Svetlana","year":"2018","unstructured":"Svetlana Kiritchenko and Saif M . Mohammad . 2018 . Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems . arxiv: 1805.04508 [cs.CL] Svetlana Kiritchenko and Saif M. Mohammad. 2018. Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. arxiv: 1805.04508 [cs.CL]"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468507.3468511"},{"key":"e_1_3_2_1_26_1","volume-title":"International Semantic Web Conference (P&D\/Industry\/BlueSky). CEUR-WS.org","author":"McCusker Jamie P.","year":"2018","unstructured":"Jamie P. McCusker , Sabbir M Rashid , Nkechinyere Agu , Kristin P Bennett , and Deborah L McGuinness . 2018 . The Whyis Knowledge Graph Framework in Action .. In International Semantic Web Conference (P&D\/Industry\/BlueSky). CEUR-WS.org , Monterey, CA, USA, 1--4. Jamie P. McCusker, Sabbir M Rashid, Nkechinyere Agu, Kristin P Bennett, and Deborah L McGuinness. 2018. The Whyis Knowledge Graph Framework in Action.. In International Semantic Web Conference (P&D\/Industry\/BlueSky). CEUR-WS.org, Monterey, CA, USA, 1--4."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372855"},{"key":"e_1_3_2_1_29_1","volume-title":"Quantifying representativeness in randomized clinical trials using machine learning fairness metrics. JAMIA open","author":"Qi Miao","year":"2021","unstructured":"Miao Qi , Owen Cahan , Morgan A Foreman , Daniel M Gruen , Amar K Das , and Kristin P Bennett . 2021. Quantifying representativeness in randomized clinical trials using machine learning fairness metrics. JAMIA open , Vol. 4 , 3 ( 2021 ), ooab077. Miao Qi, Owen Cahan, Morgan A Foreman, Daniel M Gruen, Amar K Das, and Kristin P Bennett. 2021. Quantifying representativeness in randomized clinical trials using machine learning fairness metrics. JAMIA open, Vol. 4, 3 (2021), ooab077."},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Machine Learning. PMLR, 8377--8387","author":"Saha Debjani","year":"2020","unstructured":"Debjani Saha , Candice Schumann , Duncan Mcelfresh , John Dickerson , Michelle Mazurek , and Michael Tschantz . 2020 . Measuring non-expert comprehension of machine learning fairness metrics . In International Conference on Machine Learning. PMLR, 8377--8387 . Debjani Saha, Candice Schumann, Duncan Mcelfresh, John Dickerson, Michelle Mazurek, and Michael Tschantz. 2020. Measuring non-expert comprehension of machine learning fairness metrics. In International Conference on Machine Learning. PMLR, 8377--8387."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Dagobert Soergel and Olivia Helfer. 2016. A Metrics Ontology. An intellectual infrastructure for defining managing and applying metrics. In Knowledge organization for a sustainable world: challenges and perspectives for cultural scientific and technological sharing in a connected society: proceedings of the fourteenth international ISKO conference 27--29 September 2016 Ri. Vol. 15. NIH Public Access Ergon Verlag Rio de Janeiro Brazil 333.  Dagobert Soergel and Olivia Helfer. 2016. A Metrics Ontology. An intellectual infrastructure for defining managing and applying metrics. In Knowledge organization for a sustainable world: challenges and perspectives for cultural scientific and technological sharing in a connected society: proceedings of the fourteenth international ISKO conference 27--29 September 2016 Ri. Vol. 15. NIH Public Access Ergon Verlag Rio de Janeiro Brazil 333.","DOI":"10.5771\/9783956504389-333"},{"key":"e_1_3_2_1_32_1","volume-title":"Fairness Definitions Explained. In 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare). IEEE","author":"Verma Sahil","year":"2018","unstructured":"Sahil Verma and Julia Rubin . 2018 . Fairness Definitions Explained. In 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare). IEEE , Gothenburg, Sweden, 1--7. https:\/\/doi.org\/10.23919\/FAIRWARE. 2018.8452913 10.23919\/FAIRWARE.2018.8452913 Sahil Verma and Julia Rubin. 2018. Fairness Definitions Explained. In 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare). IEEE, Gothenburg, Sweden, 1--7. https:\/\/doi.org\/10.23919\/FAIRWARE.2018.8452913"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61146-0_26"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.136"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445892"}],"event":{"name":"AIES '22: AAAI\/ACM Conference on AI, Ethics, and Society","location":"Oxford United Kingdom","acronym":"AIES '22","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","AAAI"]},"container-title":["Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534137","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514094.3534137","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:36Z","timestamp":1750186956000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":34,"alternative-id":["10.1145\/3514094.3534137","10.1145\/3514094"],"URL":"https:\/\/doi.org\/10.1145\/3514094.3534137","relation":{},"subject":[],"published":{"date-parts":[[2022,7,26]]},"assertion":[{"value":"2022-07-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}