{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T17:33:55Z","timestamp":1755797635621,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,27]]},"DOI":"10.1145\/3491101.3519878","type":"proceedings-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T16:49:48Z","timestamp":1651250988000},"page":"1-3","source":"Crossref","is-referenced-by-count":5,"title":["A Tool Bundle for AI Fairness in Practice"],"prefix":"10.1145","author":[{"given":"Boris","family":"Ruf","sequence":"first","affiliation":[{"name":"AI Research, AXA, France"}]},{"given":"Marcin","family":"Detyniecki","sequence":"additional","affiliation":[{"name":"AI Research, AXA, France"}]}],"member":"320","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Rachel K.\u00a0E. Bellamy Kuntal Dey Michael Hind Samuel\u00a0C. Hoffman Stephanie Houde Kalapriya Kannan Pranay Lohia Jacquelyn Martino Sameep Mehta Aleksandra Mojsilovic Seema Nagar Karthikeyan\u00a0Natesan Ramamurthy John Richards Diptikalyan Saha Prasanna Sattigeri Moninder Singh Kush\u00a0R. Varshney and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting Understanding and Mitigating Unwanted Algorithmic Bias. https:\/\/arxiv.org\/abs\/1810.01943  Rachel K.\u00a0E. Bellamy Kuntal Dey Michael Hind Samuel\u00a0C. Hoffman Stephanie Houde Kalapriya Kannan Pranay Lohia Jacquelyn Martino Sameep Mehta Aleksandra Mojsilovic Seema Nagar Karthikeyan\u00a0Natesan Ramamurthy John Richards Diptikalyan Saha Prasanna Sattigeri Moninder Singh Kush\u00a0R. Varshney and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting Understanding and Mitigating Unwanted Algorithmic Bias. https:\/\/arxiv.org\/abs\/1810.01943"},{"key":"e_1_3_2_2_2_1","volume-title":"Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research (Mar","author":"Berk Richard","year":"2017","unstructured":"Richard Berk , Hoda Heidari , Shahin Jabbari , Michael Kearns , and Aaron Roth . 2017. Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research (Mar 2017 ). Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2017. Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research (Mar 2017)."},{"key":"e_1_3_2_2_3_1","volume-title":"Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research (03","author":"Berk Richard","year":"2017","unstructured":"Richard Berk , Hoda Heidari , Shahin Jabbari , Michael Kearns , and Aaron Roth . 2017. Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research (03 2017 ). https:\/\/doi.org\/10.1177\/0049124118782533 Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2017. Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research (03 2017). https:\/\/doi.org\/10.1177\/0049124118782533"},{"key":"e_1_3_2_2_4_1","unstructured":"Sarah Bird Miro Dud\u00edk Richard Edgar Brandon Horn Roman Lutz Vanessa Milan Mehrnoosh Sameki Hanna Wallach and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report MSR-TR-2020-32. Microsoft. https:\/\/www.microsoft.com\/en-us\/research\/publication\/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai\/  Sarah Bird Miro Dud\u00edk Richard Edgar Brandon Horn Roman Lutz Vanessa Milan Mehrnoosh Sameki Hanna Wallach and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report MSR-TR-2020-32. Microsoft. https:\/\/www.microsoft.com\/en-us\/research\/publication\/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai\/"},{"key":"e_1_3_2_2_5_1","unstructured":"Tolga Bolukbasi Kai-Wei Chang James\u00a0Y. Zou Venkatesh Saligrama and Adam Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. CoRR abs\/1607.06520(2016). arxiv:1607.06520  Tolga Bolukbasi Kai-Wei Chang James\u00a0Y. Zou Venkatesh Saligrama and Adam Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. CoRR abs\/1607.06520(2016). arxiv:1607.06520"},{"key":"e_1_3_2_2_6_1","unstructured":"Sam Corbett-Davies and Sharad Goel. 2018. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. CoRR abs\/1808.00023(2018). arxiv:1808.00023http:\/\/arxiv.org\/abs\/1808.00023  Sam Corbett-Davies and Sharad Goel. 2018. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. CoRR abs\/1808.00023(2018). arxiv:1808.00023http:\/\/arxiv.org\/abs\/1808.00023"},{"key":"e_1_3_2_2_7_1","unstructured":"Aylin\u00a0Caliskan Islam Joanna\u00a0J. Bryson and Arvind Narayanan. 2016. Semantics derived automatically from language corpora necessarily contain human biases. CoRR abs\/1608.07187(2016). arxiv:1608.07187  Aylin\u00a0Caliskan Islam Joanna\u00a0J. Bryson and Arvind Narayanan. 2016. Semantics derived automatically from language corpora necessarily contain human biases. CoRR abs\/1608.07187(2016). arxiv:1608.07187"},{"key":"e_1_3_2_2_8_1","unstructured":"Jon\u00a0M. Kleinberg Sendhil Mullainathan and Manish Raghavan. 2016. Inherent Trade-Offs in the Fair Determination of Risk Scores. CoRR abs\/1609.05807(2016). arxiv:1609.05807http:\/\/arxiv.org\/abs\/1609.05807  Jon\u00a0M. Kleinberg Sendhil Mullainathan and Manish Raghavan. 2016. Inherent Trade-Offs in the Fair Determination of Risk Scores. CoRR abs\/1609.05807(2016). arxiv:1609.05807http:\/\/arxiv.org\/abs\/1609.05807"},{"key":"e_1_3_2_2_9_1","unstructured":"Ninareh Mehrabi Fred Morstatter Nripsuta Saxena Kristina Lerman and Aram Galstyan. 2019. A Survey on Bias and Fairness in Machine Learning. arxiv:1908.09635\u00a0[cs.LG]  Ninareh Mehrabi Fred Morstatter Nripsuta Saxena Kristina Lerman and Aram Galstyan. 2019. A Survey on Bias and Fairness in Machine Learning. arxiv:1908.09635\u00a0[cs.LG]"},{"key":"e_1_3_2_2_10_1","volume-title":"ACM CHI 2020 Workshop on Human-Centered Approaches to Fair and Responsible AI.","author":"Ruf Boris","year":"2020","unstructured":"Boris Ruf , Chaouki Boutharouite , and Marcin Detyniecki . 2020 . Getting Fairness Right: Towards a Toolbox for Practitioners . In ACM CHI 2020 Workshop on Human-Centered Approaches to Fair and Responsible AI. Boris Ruf, Chaouki Boutharouite, and Marcin Detyniecki. 2020. Getting Fairness Right: Towards a Toolbox for Practitioners. In ACM CHI 2020 Workshop on Human-Centered Approaches to Fair and Responsible AI."},{"key":"e_1_3_2_2_11_1","volume-title":"ACM CHI 2021 Workshop on Operationalizing Human-Centered Perspectives in Explainable AI.","author":"Ruf Boris","year":"2021","unstructured":"Boris Ruf and Marcin Detyniecki . 2021 . Explaining How Your AI System is Fair . In ACM CHI 2021 Workshop on Operationalizing Human-Centered Perspectives in Explainable AI. Boris Ruf and Marcin Detyniecki. 2021. Explaining How Your AI System is Fair. In ACM CHI 2021 Workshop on Operationalizing Human-Centered Perspectives in Explainable AI."},{"key":"e_1_3_2_2_12_1","unstructured":"Boris Ruf and Marcin Detyniecki. 2021. Towards the Right Kind of Fairness in AI. In ECML\/PKDD 2021 Industry Track. arxiv:2102.08453\u00a0[cs.AI]  Boris Ruf and Marcin Detyniecki. 2021. Towards the Right Kind of Fairness in AI. In ECML\/PKDD 2021 Industry Track. arxiv:2102.08453\u00a0[cs.AI]"},{"key":"e_1_3_2_2_13_1","unstructured":"Latanya Sweeney. 2013. Discrimination in Online Ad Delivery. CoRR abs\/1301.6822(2013). arxiv:1301.6822  Latanya Sweeney. 2013. Discrimination in Online Ad Delivery. CoRR abs\/1301.6822(2013). arxiv:1301.6822"}],"event":{"name":"CHI '22: CHI Conference on Human Factors in Computing Systems","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"],"location":"New Orleans LA USA","acronym":"CHI '22"},"container-title":["CHI Conference on Human Factors in Computing Systems Extended Abstracts"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3491101.3519878","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3491101.3519878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:51Z","timestamp":1750188651000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3491101.3519878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":13,"alternative-id":["10.1145\/3491101.3519878","10.1145\/3491101"],"URL":"https:\/\/doi.org\/10.1145\/3491101.3519878","relation":{},"subject":[],"published":{"date-parts":[[2022,4,27]]}}}