{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:50:57Z","timestamp":1776113457228,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":192,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union's Horizon 2020 under the Marie Sklodowska-Curie grant","award":["955990"],"award-info":[{"award-number":["955990"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,21]]},"DOI":"10.1145\/3531146.3533118","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T14:27:10Z","timestamp":1655735230000},"page":"535-563","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Towards a multi-stakeholder value-based assessment framework for algorithmic systems"],"prefix":"10.1145","author":[{"given":"Mireia","family":"Yurrita","sequence":"first","affiliation":[{"name":"Human Information Communication Design, TU Delft, Netherlands"}]},{"given":"Dave","family":"Murray-Rust","sequence":"additional","affiliation":[{"name":"Human Information Communication Design, TU Delft, Netherlands"}]},{"given":"Agathe","family":"Balayn","sequence":"additional","affiliation":[{"name":"Web Information Systems, TU Delft, Netherlands"}]},{"given":"Alessandro","family":"Bozzon","sequence":"additional","affiliation":[{"name":"Knowledge and Intelligence Design, TU Delft, Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372871"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1116-3"},{"key":"e_1_3_2_1_3_1","unstructured":"AI\u00a0Ethics Impact\u00a0Group (AIEIG). 2020. From Principles to Practice An interdisciplinary framework to operationalise AI ethics. https:\/\/www.ai-ethics-impact.org\/resource\/blob\/1961130\/c6db9894ee73aefa489d6249f5ee2b9f\/aieig---report---download-hb-data.pdf"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/2053951720949566"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 16\u201324","author":"Ajmeri Nirav","year":"2020","unstructured":"Nirav Ajmeri, Hui Guo, Pradeep\u00a0K Murukannaiah, and Munindar\u00a0P Singh. 2020. Elessar: Ethics in Norm-Aware Agents. Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 16\u201324."},{"key":"e_1_3_2_1_6_1","volume-title":"Contestable City Algorithms. International Conference on Machine Learning Workshop.","author":"Alfrink Kars","year":"2020","unstructured":"Kars Alfrink, T. Turel, A.\u00a0I. Keller, N. Doorn, and G.\u00a0W. Kortuem. 2020. Contestable City Algorithms. International Conference on Machine Learning Workshop."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377325.3377519"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjds"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702509"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314243"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445736"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/01944363.2018.1559388"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.INFFUS.2019.12.012"},{"key":"e_1_3_2_1_14_1","unstructured":"Mission assigned by\u00a0the French Prime\u00a0Minister. 2019. For a Meaningful Artificial Intelligence: Toward a French and European Strategy. https:\/\/www.aiforhumanity.fr\/pdfs\/MissionVillani_Report_ENG-VF.pdf"},{"key":"e_1_3_2_1_15_1","volume-title":"Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. (6","author":"Assran Mahmoud","year":"2019","unstructured":"Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, and Michael Rabbat. 2019. Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. (6 2019)."},{"key":"e_1_3_2_1_16_1","volume-title":"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems. (3","author":"Bakalar Chlo\u00e9","year":"2021","unstructured":"Chlo\u00e9 Bakalar, Renata Barreto, Stevie Bergman, Miranda Bogen, Bobbie Chern, Sam Corbett-Davies, Melissa Hall, Isabel Kloumann, Michelle Lam, Joaquin\u00a0Qui\u00f1onero Candela, Manish Raghavan, Joshua Simons, Jonathan Tannen, Edmund Tong, Kate Vredenburgh, and Jiejing Zhao. 2021. Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems. (3 2021)."},{"key":"e_1_3_2_1_17_1","volume-title":"Beyond Debiasing: Regulating AI and its inequalities. https:\/\/edri.org\/our-work\/if-ai-is-the-problem-is-debiasing-the-solution\/","author":"Balayn Agathe","year":"2021","unstructured":"Agathe Balayn and Seda G\u00fcrses. 2021. Beyond Debiasing: Regulating AI and its inequalities. https:\/\/edri.org\/our-work\/if-ai-is-the-problem-is-debiasing-the-solution\/"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445875"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942287"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00041"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","unstructured":"Emily\u00a0M Bender Timnit Gebru Angelina McMillan-Major and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?Proceedings of the 2021 ACM Conference on Fairness Accountability and Transparency 610\u2013623. https:\/\/doi.org\/10.1145\/3442188.3445922","DOI":"10.1145\/3442188.3445922"},{"key":"e_1_3_2_1_22_1","volume-title":"Fairness in Criminal Justice Risk Assessments: The State of the Art. (3","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. (3 2017)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.023"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173951"},{"key":"e_1_3_2_1_25_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. Issue MSR-TR-2020-32. https:\/\/www.microsoft.com\/en-us\/research\/publication\/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai\/"},{"key":"e_1_3_2_1_26_1","volume-title":"The Values Encoded in Machine Learning Research. (6","author":"Birhane Abeba","year":"2021","unstructured":"Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao. 2021. The Values Encoded in Machine Learning Research. (6 2021)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3776353"},{"key":"e_1_3_2_1_28_1","volume-title":"NLP. (5","author":"Blodgett Su\u00a0Lin","year":"2020","unstructured":"Su\u00a0Lin Blodgett, Solon Barocas, Hal Daum\u00e9, and Hanna Wallach. 2020. Language (Technology) is Power: A Critical Survey of \u201dBias\u201d in NLP. (5 2020)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287583"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10506-017-9214-9"},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 1st Conference on Fairness, Accountability and Transparency 81","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification, Sorelle\u00a0A Friedler and Christo Wilson (Eds.). Proceedings of the 1st Conference on Fairness, Accountability and Transparency 81, 77\u201391. https:\/\/proceedings.mlr.press\/v81\/buolamwini18a.html"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3302289"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1080\/1463922X.2019.1697390"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445872"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300789"},{"key":"e_1_3_2_1_36_1","volume-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. (10","author":"Chouldechova Alexandra","year":"2016","unstructured":"Alexandra Chouldechova. 2016. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. (10 2016)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376660"},{"key":"e_1_3_2_1_38_1","unstructured":"European Commission. 2018. 2018 reform of EU data protection rules. https:\/\/ec.europa.eu\/commission\/sites\/beta-political\/files\/data-protection-factsheet-changes_en.pdf"},{"key":"e_1_3_2_1_39_1","unstructured":"European Commission. 2019. Ethics guidelines for trustworthy AI. https:\/\/www.aepd.es\/sites\/default\/files\/2019-12\/ai-ethics-guidelines.pdf"},{"key":"e_1_3_2_1_40_1","unstructured":"Telia Company. 2019. Guiding Principles on Trusted AI Ethics. https:\/\/www.teliacompany.com\/globalassets\/telia-company\/documents\/about-telia-company\/public-policy\/2018\/guiding-principles-on-trusted-ai-ethics.pdf"},{"key":"e_1_3_2_1_41_1","unstructured":"Technology Council\u00a0for Science and Innovation Japanese\u00a0Cabinet Office. 2019. Social Principles of Human-Centric Artificial Intelligence. https:\/\/www8.cao.go.jp\/cstp\/english\/humancentricai.pdf"},{"key":"e_1_3_2_1_42_1","volume-title":"Excavating AI: The Politics of Training Sets for Machine Learning.","author":"Crawford Kate","year":"2019","unstructured":"Kate Crawford and Trevor Paglen. 2019. Excavating AI: The Politics of Training Sets for Machine Learning."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2008.11"},{"key":"e_1_3_2_1_44_1","volume-title":"Accelerating Reinforcement Learning through GPU Atari Emulation. (7","author":"Dalton Steven","year":"2019","unstructured":"Steven Dalton, Iuri Frosio, and Michael Garland. 2019. Accelerating Reinforcement Learning through GPU Atari Emulation. (7 2019)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372878"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","unstructured":"Janet Davis and Lisa\u00a0P. Nathan. 2015. Value Sensitive Design: Applications Adaptations and Critiques. 11-40\u00a0pages. https:\/\/doi.org\/10.1007\/978-94-007-6970-0_3","DOI":"10.1007\/978-94-007-6970-0_3"},{"key":"e_1_3_2_1_48_1","volume-title":"Hilary Nicole, and Morgan\u00a0Klaus Scheuerman.","author":"Denton Emily","year":"2020","unstructured":"Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, Hilary Nicole, and Morgan\u00a0Klaus Scheuerman. 2020. Bringing the People Back In: Contesting Benchmark Machine Learning Datasets. (7 2020). https:\/\/arxiv.org\/abs\/2007.07399"},{"key":"e_1_3_2_1_49_1","volume-title":"Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. (2","author":"Dhurandhar Amit","year":"2018","unstructured":"Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018. Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. (2 2018)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278729"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3302310"},{"key":"e_1_3_2_1_52_1","volume-title":"Value-laden Disciplinary Shifts in Machine Learning. (12","author":"Dotan Ravit","year":"2019","unstructured":"Ravit Dotan and Smitha Milli. 2019. Value-laden Disciplinary Shifts in Machine Learning. (12 2019)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1609\/hcomp.v9i1.18939"},{"key":"e_1_3_2_1_54_1","volume-title":"Fairness Through Awareness. (4","author":"Dwork Cynthia","year":"2011","unstructured":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel. 2011. Fairness Through Awareness. (4 2011)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","unstructured":"Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. 265-284\u00a0pages. https:\/\/doi.org\/10.1007\/11681878_14","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3046055.3046062"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-019-00181-5"},{"key":"e_1_3_2_1_58_1","volume-title":"Are Privacy Dashboards Good for End Users? Evaluating User Perceptions and Reactions to Google\u2019s My Activity (Extended Version). (5","author":"Farke M.","year":"2021","unstructured":"Florian\u00a0M. Farke, David\u00a0G. Balash, Maximilian Golla, Markus D\u00fcrmuth, and Adam\u00a0J. Aviv. 2021. Are Privacy Dashboards Good for End Users? Evaluating User Perceptions and Reactions to Google\u2019s My Activity (Extended Version). (5 2021)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","unstructured":"Simone Fischer-H\u00fcbner Julio Angulo Farzaneh Karegar and Tobias Pulls. 2016. Transparency Privacy and Trust \u2013 Technology for Tracking and Controlling My Data Disclosures: Does This Work? 3-14\u00a0pages. https:\/\/doi.org\/10.1007\/978-3-319-41354-9_1","DOI":"10.1007\/978-3-319-41354-9_1"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","unstructured":"Jessica Fjeld Nele Achten Hannah Hilligoss Adam Nagy and Madhulika Srikumar. 2020. Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI. SSRN Electronic Journal(2020). https:\/\/doi.org\/10.2139\/ssrn.3518482","DOI":"10.2139\/ssrn.3518482"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-019-00354-x"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-018-9482-5"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1093\/iwc"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1561\/1100000015"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.3917\/cnx.106.0071"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417050"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2019.07.007"},{"key":"e_1_3_2_1_68_1","unstructured":"Timnit Gebru Google\u00a0Jamie Morgenstern Briana Vecchione and Jennifer\u00a0Wortman Vaughan. 2020. Datasheets for Datasets."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","unstructured":"R\u00a0Stuart Geiger Kevin Yu Yanlai Yang Mindy Dai Jie Qiu Rebekah Tang and Jenny Huang. 2020. Garbage in Garbage out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?Proceedings of the 2020 Conference on Fairness Accountability and Transparency 325\u2013336. https:\/\/doi.org\/10.1145\/3351095.3372862","DOI":"10.1145\/3351095.3372862"},{"key":"e_1_3_2_1_70_1","volume-title":"Measuring Social Biases of Crowd Workers using Counterfactual Queries. (4","author":"Ghai Bhavya","year":"2020","unstructured":"Bhavya Ghai, Q.\u00a0Vera Liao, Yunfeng Zhang, and Klaus Mueller. 2020. Measuring Social Biases of Crowd Workers using Counterfactual Queries. (4 2020)."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00208-9"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143889"},{"key":"e_1_3_2_1_73_1","volume-title":"Explaining and Harnessing Adversarial Examples. (12","author":"Goodfellow J.","year":"2014","unstructured":"Ian\u00a0J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and Harnessing Adversarial Examples. (12 2014)."},{"key":"e_1_3_2_1_74_1","unstructured":"Google. 2018. AI at Google: Our Principles. https:\/\/www.blog.google\/technology\/ai\/ai-principles\/"},{"key":"e_1_3_2_1_75_1","volume-title":"Machine Learning: The Debates workshop at the 35th International Conference on Machine Learning (ICML).","author":"Green Ben","year":"2018","unstructured":"Ben Green and Lily Hu. 2018. The Myth in the Methodology: Towards a Recontextualization of Fairness in Machine Learning. Machine Learning: The Debates workshop at the 35th International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"crossref","unstructured":"Nina Grgic-Hlaca Muhammad\u00a0Bilal Zafar Krishna\u00a0P Gummadi and Adrian Weller. 2018. Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning.","DOI":"10.1609\/aaai.v32i1.11296"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11569-015-0238-x"},{"key":"e_1_3_2_1_78_1","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems, 3323\u20133331","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nathan Srebro. 2016. Equality of Opportunity in Supervised Learning. Proceedings of the 30th International Conference on Neural Information Processing Systems, 3323\u20133331."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372831"},{"key":"e_1_3_2_1_80_1","unstructured":"Katrina Heijne and Han van\u00a0der Meer. 2019. Road Map for Creative Problem Solving Techniques Organizing and facilitating group sessions. Boom Uitgevers Amsterdam."},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","unstructured":"Drew Hemment Ruth Aylett Vaishak Belle Dave Murray-Rust Ewa Luger Jane Hillston Michael Rovatsos and Frank Broz. 2019. Experiential AI. AI Matters 5 (4 2019) 25\u201331. Issue 1. https:\/\/doi.org\/10.1145\/3320254.3320264","DOI":"10.1145\/3320254.3320264"},{"key":"e_1_3_2_1_82_1","volume-title":"Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. (1","author":"Henderson Peter","year":"2020","unstructured":"Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, and Joelle Pineau. 2020. Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. (1 2020)."},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00146-021-01251-8"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","unstructured":"Eelco Herder and Olaf van Maaren. 2020. Privacy Dashboards: The Impact of the Type of Personal Data and User Control on Trust and Perceived Risk. Adjunct Publication of the 28th ACM Conference on User Modeling Adaptation and Personalization 169\u2013174. https:\/\/doi.org\/10.1145\/3386392.3399557","DOI":"10.1145\/3386392.3399557"},{"key":"e_1_3_2_1_85_1","volume-title":"How Humans Judge Machines","author":"Orghian Diana","unstructured":"C{\u00e9}sar Hidalgo, Diana Orghian, Jordi Albo-Canals, Filipa de Almeida, and Natalia Martin. 2021. How Humans Judge Machines. MIT Press. https:\/\/hal.archives-ouvertes.fr\/hal-03058652"},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/3064663.3064703"},{"key":"e_1_3_2_1_87_1","volume-title":"The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards. (5","author":"Holland Sarah","year":"2018","unstructured":"Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski. 2018. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards. (5 2018)."},{"key":"e_1_3_2_1_88_1","doi-asserted-by":"publisher","unstructured":"Leif-Erik Holtz Katharina Nocun and Marit Hansen. 2011. Towards Displaying Privacy Information with Icons. 338-348\u00a0pages. https:\/\/doi.org\/10.1007\/978-3-642-20769-3_27","DOI":"10.1007\/978-3-642-20769-3_27"},{"key":"e_1_3_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445735"},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300637"},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445918"},{"key":"e_1_3_2_1_92_1","unstructured":"IBM. 2019. IBM Everyday Ethics for AI. https:\/\/www.ibm.com\/watson\/assets\/duo\/pdf\/everydayethics.pdf"},{"key":"e_1_3_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEEESTD.2008.4601584"},{"key":"e_1_3_2_1_94_1","unstructured":"China Electronics\u00a0Standardization Institute. 2018. Original CSET Translation of \u201dArtificial Intelligence Standardization White Paper\u201d. https:\/\/cset.georgetown.edu\/research\/artificial-intelligence-standardization-white-paper\/"},{"key":"e_1_3_2_1_95_1","volume-title":"Toronto Declaration: Protecting the Right to Equality and Non-Discrimination in Machine Learning Systems. https:\/\/www.accessnow.org\/cms\/assets\/uploads\/2018\/08\/The-Toronto-Declaration_ENG_08-2018.pdf","author":"Now\u00a0Amnesty International Access","year":"2018","unstructured":"Access Now\u00a0Amnesty International. 2018. Toronto Declaration: Protecting the Right to Equality and Non-Discrimination in Machine Learning Systems. https:\/\/www.accessnow.org\/cms\/assets\/uploads\/2018\/08\/The-Toronto-Declaration_ENG_08-2018.pdf"},{"key":"e_1_3_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445904"},{"key":"e_1_3_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363201"},{"key":"e_1_3_2_1_98_1","volume-title":"EUCA: A Practical Prototyping Framework towards End-User-Centered Explainable Artificial Intelligence. (2","author":"Jin Weina","year":"2021","unstructured":"Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, and Ghassan Hamarneh. 2021. EUCA: A Practical Prototyping Framework towards End-User-Centered Explainable Artificial Intelligence. (2 2021). https:\/\/arxiv.org\/abs\/2102.02437"},{"key":"e_1_3_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1177\/2056305120969914"},{"key":"e_1_3_2_1_100_1","doi-asserted-by":"publisher","unstructured":"Pratyusha Kalluri. 2020. Don\u2019t ask if artificial intelligence is good or fair ask how it shifts power.Nature 583(2020). Issue 7815. https:\/\/doi.org\/10.1038\/d41586-020-02003-2","DOI":"10.1038\/d41586-020-02003-2"},{"key":"e_1_3_2_1_101_1","volume-title":"On the Effectiveness of Regularization Against Membership Inference Attacks. (6","author":"Kaya Yigitcan","year":"2020","unstructured":"Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitras. 2020. On the Effectiveness of Regularization Against Membership Inference Attacks. (6 2020)."},{"key":"e_1_3_2_1_102_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning 80","author":"Kearns Michael","year":"2018","unstructured":"Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei\u00a0Steven Wu. 2018. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness, Jennifer Dy and Andreas Krause (Eds.). Proceedings of the 35th International Conference on Machine Learning 80, 2564\u20132572. https:\/\/proceedings.mlr.press\/v80\/kearns18a.html"},{"key":"e_1_3_2_1_103_1","volume-title":"The Ethical Algorithm: The Science of Socially Aware Algorithm Design","author":"Kearns Michael","unstructured":"Michael Kearns and Aaron Roth. 2019. The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Oxford University Press, Inc."},{"key":"e_1_3_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290607.3310433"},{"key":"e_1_3_2_1_105_1","volume-title":"Inherent Trade-Offs in the Fair Determination of Risk Scores. (9","author":"Kleinberg Jon","year":"2016","unstructured":"Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent Trade-Offs in the Fair Determination of Risk Scores. (9 2016)."},{"key":"e_1_3_2_1_106_1","doi-asserted-by":"publisher","unstructured":"Daniel Kluttz Nitin Kohli and Deirdre\u00a0K. Mulligan. 2018. Contestability and Professionals: From Explanations to Engagement with Algorithmic Systems. SSRN Electronic Journal(2018). https:\/\/doi.org\/10.2139\/ssrn.3311894","DOI":"10.2139\/ssrn.3311894"},{"key":"e_1_3_2_1_107_1","doi-asserted-by":"publisher","unstructured":"kobi leins Jey\u00a0Han Lau and Timothy Baldwin. 2020. Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate and on What Basis?Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.261","DOI":"10.18653\/v1"},{"key":"e_1_3_2_1_108_1","unstructured":"TD Krafft and K Zweig. 2019. Transparenz und Nachvollziehbarkeit algorithmenbasierter Entscheidungsprozesse. Ein Regulierungsvorschlag(2019)."},{"key":"e_1_3_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173699"},{"key":"e_1_3_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372853"},{"key":"e_1_3_2_1_111_1","unstructured":"Matt\u00a0J Kusner Joshua Loftus Chris Russell and Ricardo Silva. 2017. Counterfactual Fairness I\u00a0Guyon U\u00a0V Luxburg S\u00a0Bengio H\u00a0Wallach R\u00a0Fergus S\u00a0Vishwanathan and R\u00a0Garnett (Eds.). Advances in Neural Information Processing Systems 30. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf"},{"key":"e_1_3_2_1_112_1","doi-asserted-by":"crossref","unstructured":"Claire Larsonneur. 2021. Intelligence artificielle ET\/OU diversit\u00e9 linguistique : les paradoxes du traitement automatique des langues. http:\/\/www.hybrid.univ-paris8.fr\/lodel\/index.php?id=1542","DOI":"10.4000\/hybrid.650"},{"key":"e_1_3_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1080\/01944367308977851"},{"key":"e_1_3_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.1145\/2998181.2998230"},{"key":"e_1_3_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359283"},{"key":"e_1_3_2_1_116_1","doi-asserted-by":"publisher","unstructured":"Michelle Seng\u00a0Ah Lee and Jatinder Singh. 2021. Risk Identification Questionnaire for Unintended Bias in Machine Learning Development Lifecycle. SSRN Electronic Journal(2021). https:\/\/doi.org\/10.2139\/ssrn.3777093","DOI":"10.2139\/ssrn.3777093"},{"key":"e_1_3_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376590"},{"key":"e_1_3_2_1_118_1","volume-title":"Question-Driven Design Process for Explainable AI User Experiences. (4","author":"Liao Vera","year":"2021","unstructured":"Q.\u00a0Vera Liao, Milena Pribi\u0107, Jaesik Han, Sarah Miller, and Daby Sow. 2021. Question-Driven Design Process for Explainable AI User Experiences. (4 2021)."},{"key":"e_1_3_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.5555\/3463952.3464048"},{"key":"e_1_3_2_1_120_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376727"},{"key":"e_1_3_2_1_121_1","doi-asserted-by":"publisher","DOI":"10.1145\/3449180"},{"key":"e_1_3_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.84"},{"key":"e_1_3_2_1_123_1","volume-title":"Analysis of memory consumption by neural networks based on hyperparameters. (10","author":"Mahendran N.","year":"2021","unstructured":"N. Mahendran. 2021. Analysis of memory consumption by neural networks based on hyperparameters. (10 2021)."},{"key":"e_1_3_2_1_124_1","volume-title":"Jr\u00a0Google Vinodkumar Prabhakaran Google\u00a0Jill Kuhlberg, and Andrew S Smart Google William\u00a0Isaac DeepMind","author":"Martin Donald","year":"2020","unstructured":"Donald Martin, Jr\u00a0Google Vinodkumar Prabhakaran Google\u00a0Jill Kuhlberg, and Andrew S Smart Google William\u00a0Isaac DeepMind. 2020. Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context. (2020)."},{"key":"e_1_3_2_1_125_1","unstructured":"M. Mehldau. 2007. Iconset for data-privacy declarations v 0.1. https:\/\/netzpolitik.org\/wp-upload\/data-privacy-icons-v01.pdf"},{"key":"e_1_3_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_1_127_1","unstructured":"Microsoft. 2018. AI Principles. https:\/\/www.microsoft.com\/en-us\/ai\/responsible-ai?activetab=pivot1%3aprimaryr6"},{"key":"e_1_3_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445096"},{"key":"e_1_3_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_2_1_130_1","volume-title":"Proceedings of the First Workshop on Bridging Human{\u2013}Computer Interaction and Natural Language Processing, 96\u2013100","author":"Mitra Tanushree","year":"2021","unstructured":"Tanushree Mitra. 2021. Provocation: Contestability in Large-Scale Interactive {NLP} Systems. Proceedings of the First Workshop on Bridging Human{\u2013}Computer Interaction and Natural Language Processing, 96\u2013100."},{"key":"e_1_3_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0114-4"},{"key":"e_1_3_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11948-019-00165-5"},{"key":"e_1_3_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_2_1_134_1","volume-title":"Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, 309\u2013316","author":"Murukannaiah K","year":"2014","unstructured":"Pradeep\u00a0K Murukannaiah and Munindar\u00a0P Singh. 2014. Xipho: Extending Tropos to Engineer Context-Aware Personal Agents. Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, 309\u2013316."},{"key":"e_1_3_2_1_135_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243855"},{"key":"e_1_3_2_1_136_1","volume-title":"InterpretML: A Unified Framework for Machine Learning Interpretability. (9","author":"Nori Harsha","year":"2019","unstructured":"Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019. InterpretML: A Unified Framework for Machine Learning Interpretability. (9 2019)."},{"key":"e_1_3_2_1_137_1","doi-asserted-by":"publisher","DOI":"10.1145\/3475716.3475770"},{"key":"e_1_3_2_1_138_1","unstructured":"OECD. 2019. Recommendation of the Council on Artificial Intelligence. https:\/\/legalinstruments.oecd.org\/en\/instruments\/OECD-LEGAL-0406"},{"key":"e_1_3_2_1_139_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CLSR.2020.105474"},{"key":"e_1_3_2_1_140_1","volume-title":"Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems","author":"Ethics\u00a0of Autonomous The IEEE","unstructured":"The IEEE Global\u00a0Initiative on\u00a0Ethics\u00a0of Autonomous and Intelligent Systems.2019. Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems(first edition ed.). IEEE."},{"key":"e_1_3_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"e_1_3_2_1_142_1","doi-asserted-by":"publisher","DOI":"10.1080\/10714421.2019.1704111"},{"key":"e_1_3_2_1_143_1","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-021-02693-2"},{"key":"e_1_3_2_1_144_1","volume-title":"Data and its (dis)contents: A survey of dataset development and use in machine learning research. (12","author":"Paullada Amandalynne","year":"2020","unstructured":"Amandalynne Paullada, Inioluwa\u00a0Deborah Raji, Emily\u00a0M. Bender, Emily Denton, and Alex Hanna. 2020. Data and its (dis)contents: A survey of dataset development and use in machine learning research. (12 2020). http:\/\/arxiv.org\/abs\/2012.05345"},{"key":"e_1_3_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10676-011-9282-6"},{"key":"e_1_3_2_1_146_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372828"},{"key":"e_1_3_2_1_147_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372873"},{"key":"e_1_3_2_1_148_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372879"},{"key":"e_1_3_2_1_149_1","doi-asserted-by":"publisher","unstructured":"Arianna Rossi and Monica Palmirani. 2017. A Visualization Approach for Adaptive Consent in the European Data Protection Framework. 2017 Conference for E-Democracy and Open Government (CeDEM) 159\u2013170. https:\/\/doi.org\/10.1109\/CeDEM.2017.23","DOI":"10.1109\/CeDEM.2017.23"},{"key":"e_1_3_2_1_150_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v36i4.2577"},{"key":"e_1_3_2_1_151_1","volume-title":"Aequitas: A Bias and Fairness Audit Toolkit.","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."},{"key":"e_1_3_2_1_152_1","unstructured":"Christian Sandvig Kevin Hamilton Karrie Karahalios and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms.Data and discrimination: converting critical concerns into productive inquiry 22."},{"key":"e_1_3_2_1_153_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1"},{"key":"e_1_3_2_1_154_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROMAN.2009.5326244"},{"key":"e_1_3_2_1_155_1","doi-asserted-by":"publisher","DOI":"10.1145\/3476058"},{"key":"e_1_3_2_1_156_1","doi-asserted-by":"publisher","DOI":"10.9707\/2307-0919.1116"},{"key":"e_1_3_2_1_157_1","unstructured":"National Science and United States Executive Office of the\u00a0President Technology Council Committee\u00a0on Technology. 2016. Preparing for the Future of Artificial Intelligence. https:\/\/obamawhitehouse.archives.gov\/sites\/default\/files\/whitehouse_files\/microsites\/ostp\/NSTC\/preparing_for_the_future_of_ai.pdf"},{"key":"e_1_3_2_1_158_1","volume-title":"Operationalizing Human Values in Software Engineering: A Survey. (8","author":"Shahin Mojtaba","year":"2021","unstructured":"Mojtaba Shahin, Waqar Hussain, Arif Nurwidyantoro, Harsha Perera, Rifat Shams, John Grundy, and Jon Whittle. 2021. Operationalizing Human Values in Software Engineering: A Survey. (8 2021)."},{"key":"e_1_3_2_1_159_1","doi-asserted-by":"publisher","DOI":"10.1145\/3479577"},{"key":"e_1_3_2_1_160_1","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2021.2014547"},{"key":"e_1_3_2_1_161_1","doi-asserted-by":"publisher","DOI":"10.1145\/3419764"},{"key":"e_1_3_2_1_162_1","volume-title":"Membership Inference Attacks against Machine Learning Models. (10","author":"Shokri Reza","year":"2016","unstructured":"Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2016. Membership Inference Attacks against Machine Learning Models. (10 2016)."},{"key":"e_1_3_2_1_163_1","unstructured":"Dasha Simons. 2019. Design for fairness in AI: Cooking a fair AI Dish. http:\/\/resolver.tudelft.nl\/uuid:5a116c17-ce0a-4236-b283-da6b8545628c"},{"key":"e_1_3_2_1_164_1","doi-asserted-by":"publisher","unstructured":"The\u00a0Royal Society. 2019. Explainable AI: the basics. https:\/\/doi.org\/10.1177\/1461444816676645","DOI":"10.1177\/1461444816676645"},{"key":"e_1_3_2_1_165_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372870"},{"key":"e_1_3_2_1_166_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330664"},{"key":"e_1_3_2_1_167_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445088"},{"key":"e_1_3_2_1_168_1","doi-asserted-by":"publisher","unstructured":"Harini Suresh and John Guttag. 2021. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. Equity and Access in Algorithms Mechanisms and Optimization 1\u20139. https:\/\/doi.org\/10.1145\/3465416.3483305","DOI":"10.1145\/3465416.3483305"},{"key":"e_1_3_2_1_169_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00766-017-0273-y"},{"key":"e_1_3_2_1_170_1","doi-asserted-by":"publisher","DOI":"10.1145\/3322640.3326705"},{"key":"e_1_3_2_1_171_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.314"},{"key":"e_1_3_2_1_172_1","doi-asserted-by":"publisher","DOI":"10.1177\/2053951719879468"},{"key":"e_1_3_2_1_173_1","doi-asserted-by":"publisher","DOI":"10.1145\/3415238"},{"key":"e_1_3_2_1_174_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445365"},{"key":"e_1_3_2_1_175_1","doi-asserted-by":"publisher","unstructured":"Ibo van\u00a0de Poel. 2013. Translating Values into Design Requirements. 253-266\u00a0pages. https:\/\/doi.org\/10.1007\/978-94-007-7762-0_20","DOI":"10.1007\/978-94-007-7762-0_20"},{"key":"e_1_3_2_1_176_1","doi-asserted-by":"publisher","DOI":"10.1108\/03090569410075786"},{"key":"e_1_3_2_1_177_1","doi-asserted-by":"publisher","DOI":"10.1145\/3194770.3194776"},{"key":"e_1_3_2_1_178_1","first-page":"494","article-title":"Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI.","volume":"2","author":"Wachter Sandra","year":"2019","unstructured":"Sandra Wachter and Brent Mittelstadt. 2019. Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI.Columbia Business Law Review 2 (2019), 494\u2013620.","journal-title":"Columbia Business Law Review"},{"key":"e_1_3_2_1_179_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376813"},{"key":"e_1_3_2_1_180_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2934619"},{"key":"e_1_3_2_1_181_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445928"},{"key":"e_1_3_2_1_182_1","unstructured":"Langdon Winner. 1980. Do Artifacts Have Politics?Daedalus 109(1980) 121\u2013136. Issue 1. http:\/\/www.jstor.org\/stable\/20024652"},{"key":"e_1_3_2_1_183_1","volume-title":"Towards a Robust and Trustworthy Machine Learning System Development. (1","author":"Xiong Pulei","year":"2021","unstructured":"Pulei Xiong, Scott Buffett, Shahrear Iqbal, Philippe Lamontagne, Mohammad Mamun, and Heather Molyneaux. 2021. Towards a Robust and Trustworthy Machine Learning System Development. (1 2021)."},{"key":"e_1_3_2_1_184_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622525"},{"key":"e_1_3_2_1_185_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5458"},{"key":"e_1_3_2_1_186_1","volume-title":"Responsibility Research for Trustworthy Autonomous Systems. 20th International Conference on Autonomous Agents and Multiagent Systems (03\/05\/21 - 07\/05\/21)","author":"Yazdanpanah Vahid","year":"2021","unstructured":"Vahid Yazdanpanah, Enrico Gerding, Sebastian Stein, Mehdi Dastani, Catholijn\u00a0M Jonker, and Timothy Norman. 2021. Responsibility Research for Trustworthy Autonomous Systems. 20th International Conference on Autonomous Agents and Multiagent Systems (03\/05\/21 - 07\/05\/21), 57\u201362. https:\/\/eprints.soton.ac.uk\/447511\/"},{"key":"e_1_3_2_1_187_1","volume-title":"Enhanced Membership Inference Attacks against Machine Learning Models. (11","author":"Ye Jiayuan","year":"2021","unstructured":"Jiayuan Ye, Aadyaa Maddi, Sasi\u00a0Kumar Murakonda, and Reza Shokri. 2021. Enhanced Membership Inference Attacks against Machine Learning Models. (11 2021)."},{"key":"e_1_3_2_1_188_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"e_1_3_2_1_189_1","unstructured":"Angela Zhou David Madras Inioluwa\u00a0Raji Raji Bogdan Kulynych Smitha Mili and Richard Zemel. [n. d.]. Call for participation: Participatory Approaches to Machine Learning. https:\/\/participatoryml.github.io\/"},{"key":"e_1_3_2_1_190_1","volume-title":"AI and Ethics \u2013 Operationalising Responsible AI. (5","author":"Zhu Liming","year":"2021","unstructured":"Liming Zhu, Xiwei Xu, Qinghua Lu, Guido Governatori, and Jon Whittle. 2021. AI and Ethics \u2013 Operationalising Responsible AI. (5 2021)."},{"key":"e_1_3_2_1_191_1","doi-asserted-by":"publisher","DOI":"10.1109\/ARES.2014.27"},{"key":"e_1_3_2_1_192_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2019.2958393"}],"event":{"name":"FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency","location":"Seoul Republic of Korea","acronym":"FAccT '22","sponsor":["ACM Association for Computing Machinery"]},"container-title":["2022 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533118","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3531146.3533118","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:08Z","timestamp":1750186928000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":192,"alternative-id":["10.1145\/3531146.3533118","10.1145\/3531146"],"URL":"https:\/\/doi.org\/10.1145\/3531146.3533118","relation":{},"subject":[],"published":{"date-parts":[[2022,6,20]]},"assertion":[{"value":"2022-06-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}