{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:30:29Z","timestamp":1730327429694,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,8]]},"DOI":"10.1145\/3600211.3604677","type":"proceedings-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T18:41:37Z","timestamp":1693334497000},"page":"775-785","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Perceived Algorithmic Fairness using Organizational Justice Theory: An Empirical Case Study on Algorithmic Hiring"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0009-0003-5250-602X","authenticated-orcid":false,"given":"Guusje","family":"Juijn","sequence":"first","affiliation":[{"name":"Utrecht University, Netherlands"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8251-7235","authenticated-orcid":false,"given":"Niya","family":"Stoimenova","sequence":"additional","affiliation":[{"name":"DEUS, Netherlands"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1986-8366","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Reis","sequence":"additional","affiliation":[{"name":"DEUS, Portugal"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6062-3117","authenticated-orcid":false,"given":"Dong","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Utrecht University, Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/make4020026"},{"volume-title":"Ethics of Data and Analytics","author":"Angwin Julia","key":"e_1_3_2_1_2_1","unstructured":"Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. In Ethics of Data and Analytics. Auerbach Publications, 254\u2013264."},{"key":"e_1_3_2_1_3_1","unstructured":"Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http:\/\/www.fairmlbook.org."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942287"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173951"},{"key":"e_1_3_2_1_6_1","volume-title":"Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSR-TR-2020-32","author":"Bird Sarah","year":"2020","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. Microsoft, Tech. Rep. MSR-TR-2020-32 (2020)."},{"key":"e_1_3_2_1_7_1","volume-title":"The statistical fairness field guide: perspectives from social and formal sciences. AI and Ethics","author":"Carey N","year":"2022","unstructured":"Alycia\u00a0N Carey and Xintao Wu. 2022. The statistical fairness field guide: perspectives from social and formal sciences. AI and Ethics (2022), 1\u201323."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07939-1"},{"key":"e_1_3_2_1_9_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2","author":"Chouldechova Alexandra","year":"2017","unstructured":"Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2 (2017), 153\u2013163."},{"key":"e_1_3_2_1_10_1","volume-title":"On the dimensionality of organizational justice: a construct validation of a measure.Journal of applied psychology 86, 3","author":"Colquitt A","year":"2001","unstructured":"Jason\u00a0A Colquitt. 2001. On the dimensionality of organizational justice: a construct validation of a measure.Journal of applied psychology 86, 3 (2001), 386."},{"key":"e_1_3_2_1_11_1","volume-title":"Retrieved","author":"European Commission","year":"2023","unstructured":"European Commission. 2023. Regulatory framework proposal on Artificial Intelligence. Retrieved March 13, 2023 from https:\/\/digital-strategy.ec.europa.eu\/en\/policies\/regulatory-framework-ai"},{"key":"e_1_3_2_1_12_1","volume-title":"NeurIPS 2020 Workshop: ML Retrospectives, Surveys & Meta-Analyses (ML-RSA).","author":"Dasch T","year":"2020","unstructured":"Sophia\u00a0T Dasch, Vincent Rice, Venkat\u00a0R Lakshminarayanan, Taiwo\u00a0A Togun, C\u00a0Malik Boykin, and Sarah\u00a0M Brown. 2020. Opportunities for a More Interdisciplinary Approach to Perceptions of Fairness in Machine Learning. In NeurIPS 2020 Workshop: ML Retrospectives, Surveys & Meta-Analyses (ML-RSA)."},{"key":"e_1_3_2_1_13_1","unstructured":"Jeffrey Dastin. 2018. Amazon scraps secret AI recruiting tool that showed bias against women. https:\/\/www.reuters.com\/article\/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3302310"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/isj.12370"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330691"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.2307\/257990"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186138"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11296"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Nina Grgi\u0107-Hla\u010da Gabriel Lima Adrian Weller and Elissa\u00a0M. Redmiles. 2022. Dimensions of diversity in human perceptions of algorithmic fairness. In Equity and Access in Algorithms Mechanisms and Optimization(EAAMO \u201922). Article 21 12\u00a0pages.","DOI":"10.1145\/3551624.3555306"},{"key":"e_1_3_2_1_21_1","volume-title":"Equality of opportunity in supervised learning. Advances in neural information processing systems 29","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372831"},{"key":"e_1_3_2_1_23_1","first-page":"290","article-title":"Can AI solve the diversity problem in the tech industry: Mitigating noise and bias in employment decision-making","volume":"22","author":"Houser A","year":"2019","unstructured":"Kimberly\u00a0A Houser. 2019. Can AI solve the diversity problem in the tech industry: Mitigating noise and bias in employment decision-making. Stan. Tech. L. Rev. 22 (2019), 290.","journal-title":"Stan. Tech. L. Rev."},{"key":"e_1_3_2_1_24_1","volume-title":"8th Innovations in Theoretical Computer Science Conference (ITCS 2017)(Leibniz International Proceedings in Informatics (LIPIcs), Vol.\u00a067)","author":"Kleinberg Jon","year":"2017","unstructured":"Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2017. Inherent trade-offs in the fair determination of risk scores. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017)(Leibniz International Proceedings in Informatics (LIPIcs), Vol.\u00a067). 43:1\u201343:23."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40685-020-00134-w"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2021.06.023"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359284"},{"volume-title":"What should be done with equity theory? In Social exchange","author":"Leventhal S","key":"e_1_3_2_1_28_1","unstructured":"Gerald\u00a0S Leventhal. 1980. What should be done with equity theory? In Social exchange. Springer, 27\u201355."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462531"},{"key":"e_1_3_2_1_30_1","unstructured":"Trisha Mahoney Kush Varshney and Michael Hind. 2020. AI Fairness. O\u2019Reilly Media Incorporated."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_1_32_1","volume-title":"Yazeed Awwad, and Gerald\u00a0C Kane.","author":"Morse Lily","year":"2021","unstructured":"Lily Morse, Mike Horia\u00a0M Teodorescu, Yazeed Awwad, and Gerald\u00a0C Kane. 2021. Do the ends justify the means? Variation in the distributive and procedural fairness of machine learning algorithms. Journal of Business Ethics (2021), 1\u201313."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_34_1","volume-title":"Demographics and discussion influence views on algorithmic fairness. arXiv preprint arXiv:1712.09124","author":"Pierson Emma","year":"2017","unstructured":"Emma Pierson. 2017. Demographics and discussion influence views on algorithmic fairness. arXiv preprint arXiv:1712.09124 (2017)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372828"},{"key":"e_1_3_2_1_36_1","volume-title":"A framework for fairness: a systematic review of existing fair AI solutions. arXiv preprint arXiv:2112.05700","author":"Richardson Brianna","year":"2021","unstructured":"Brianna Richardson and Juan\u00a0E Gilbert. 2021. A framework for fairness: a systematic review of existing fair AI solutions. arXiv preprint arXiv:2112.05700 (2021)."},{"key":"e_1_3_2_1_37_1","volume-title":"Designing fair AI for managing employees in organizations: a review, critique, and design agenda. Human\u2013Computer Interaction 35, 5-6","author":"Robert P","year":"2020","unstructured":"Lionel\u00a0P Robert, Casey Pierce, Liz Marquis, Sangmi Kim, and Rasha Alahmad. 2020. Designing fair AI for managing employees in organizations: a review, critique, and design agenda. Human\u2013Computer Interaction 35, 5-6 (2020), 545\u2013575."},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Machine Learning. PMLR, 8377\u20138387","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\u20138387."},{"key":"e_1_3_2_1_39_1","volume-title":"Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577","author":"Saleiro Pedro","year":"2018","unstructured":"Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit\u00a0T Rodolfa, and Rayid Ghani. 2018. Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577 (2018)."},{"key":"e_1_3_2_1_40_1","volume-title":"International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).","author":"Schumann Candice","year":"2020","unstructured":"Candice Schumann, Jeffrey Foster, Nicholas Mattei, and John Dickerson. 2020. We need fairness and explainability in algorithmic hiring. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330664"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1177\/20539517221115189"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445365"},{"volume-title":"Fairness definitions explained. In 2018 ieee\/acm international workshop on software fairness (fairware)","author":"Verma Sahil","key":"e_1_3_2_1_44_1","unstructured":"Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In 2018 ieee\/acm international workshop on software fairness (fairware). IEEE, 1\u20137."},{"key":"e_1_3_2_1_45_1","volume-title":"The what-if tool: Interactive probing of machine learning models","author":"Wexler James","year":"2019","unstructured":"James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Vi\u00e9gas, and Jimbo Wilson. 2019. The what-if tool: Interactive probing of machine learning models. IEEE transactions on visualization and computer graphics 26, 1 (2019), 56\u201365."}],"event":{"name":"AIES '23: AAAI\/ACM Conference on AI, Ethics, and Society","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence"],"location":"Montr\\'{e}al QC Canada","acronym":"AIES '23"},"container-title":["Proceedings of the 2023 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3600211.3604677","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T10:37:20Z","timestamp":1723113440000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3600211.3604677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,8]]},"references-count":45,"alternative-id":["10.1145\/3600211.3604677","10.1145\/3600211"],"URL":"https:\/\/doi.org\/10.1145\/3600211.3604677","relation":{},"subject":[],"published":{"date-parts":[[2023,8,8]]},"assertion":[{"value":"2023-08-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}