{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:23:00Z","timestamp":1779286980906,"version":"3.51.4"},"reference-count":131,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. In addition, significant efforts have been placed at algorithm level rather than system level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize RAI from a system perspective, in this article, we present an RAI Pattern Catalogue based on the results of a multivocal literature review. Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The RAI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and RAI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement RAI.<\/jats:p>","DOI":"10.1145\/3626234","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T15:43:05Z","timestamp":1696520585000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":79,"title":["Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9466-1672","authenticated-orcid":false,"given":"Qinghua","family":"Lu","sequence":"first","affiliation":[{"name":"Data61, CSIRO, Eveleigh, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5839-3765","authenticated-orcid":false,"given":"Liming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Eveleigh, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2273-1862","authenticated-orcid":false,"given":"Xiwei","family":"Xu","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Eveleigh, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9342-7809","authenticated-orcid":false,"given":"Jon","family":"Whittle","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Eveleigh, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6051-0155","authenticated-orcid":false,"given":"Didar","family":"Zowghi","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Eveleigh, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5947-7502","authenticated-orcid":false,"given":"Aurelie","family":"Jacquet","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Eveleigh, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491101.3519724"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3522664.3528600"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338937"},{"key":"e_1_3_2_5_2","article-title":"Opening the software engineering toolbox for the assessment of trustworthy AI","author":"Ahuja Mohit Kumar","year":"2020","unstructured":"Mohit Kumar Ahuja, Mohamed-Bachir Belaid, Pierre Bernab\u00e9, Mathieu Collet, Arnaud Gotlieb, Chhagan Lal, Dusica Marijan, Sagar Sen, Aizaz Sharif, and Helge Spieker. 2020. Opening the software engineering toolbox for the assessment of trustworthy AI. arXiv preprint arXiv:2007.07768 (2020).","journal-title":"arXiv preprint arXiv:2007.07768"},{"key":"e_1_3_2_6_2","unstructured":"Sulaiman Alsheiabni Yen Cheung and Chris Messom. 2019. Towards an artificial intelligence maturity model: From science fiction to business facts. In Proceedings of the Pacific Asia Conference on Information Systems (PACIS\u201919)."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10676-022-09634-1"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1515\/pjbr-2018-0024"},{"issue":"4","key":"e_1_3_2_10_2","first-page":"Article 6, 13 p","article-title":"FactSheets: Increasing trust in AI services through supplier\u2019s declarations of conformity","volume":"63","author":"Arnold Matthew","year":"2019","unstructured":"Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovi\u0107, Ravi Nair, K. Natesan Ramamurthy, D. Reimer, Alexandra Olteanu, David Piorkowski, J. Tsay, and K. R. Varshney. 2019. FactSheets: Increasing trust in AI services through supplier\u2019s declarations of conformity. IBM Journal of Research and Development 63, 4-5 (2019), Article 6, 13 pages.","journal-title":"IBM Journal of Research and Development"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10878-022-00856-z"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6997"},{"key":"e_1_3_2_13_2","volume-title":"Proceedings of the International Workshop on Science Gateways","author":"Barclay Iain","year":"2019","unstructured":"Iain Barclay, Alun Preece, Ian Taylor, and Dinesh Verma. 2019. Towards traceability in data ecosystems using a bill of materials model. In Proceedings of the International Workshop on Science Gateways."},{"key":"e_1_3_2_14_2","volume-title":"Proceedings of the Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA\u201987)","author":"Beck Kent","year":"1987","unstructured":"Kent Beck and Ward Cunningham. 1987. Using pattern languages for object oriented programs. In Proceedings of the Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA\u201987)."},{"key":"e_1_3_2_15_2","article-title":"Responsible AI by design in practice","author":"Benjamins Richard","year":"2019","unstructured":"Richard Benjamins, Alberto Barbado, and Daniel Sierra. 2019. Responsible AI by design in practice. arXiv preprint arXiv:1909.12838 (2019).","journal-title":"arXiv preprint arXiv:1909.12838"},{"key":"e_1_3_2_16_2","article-title":"PriMaL: A privacy-preserving machine learning method for event detection in distributed sensor networks","author":"Bennati Stefano","year":"2017","unstructured":"Stefano Bennati and Catholijn M. Jonker. 2017. PriMaL: A privacy-preserving machine learning method for event detection in distributed sensor networks. arXiv preprint arXiv:1703.07150 (2017).","journal-title":"arXiv preprint arXiv:1703.07150"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10506-020-09270-4"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357236.3395558"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Pal Boza and Theodoros Evgeniou. 2021. Implementing AI Principles: Frameworks Processes and Tools. Working Paper No. 2021\/04\/DSC\/TOM. INSEAD.","DOI":"10.2139\/ssrn.3783124"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3422337.3450325"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v16i1.19274"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTS.2021.3077595"},{"key":"e_1_3_2_24_2","unstructured":"World Wide Web Consortium. 2019. Verifiable Credentials Data Model 1.0: Expressing Verifiable Information on the Web. Retrieved October 14 2023 from https:\/\/www.w3.org\/TR\/vc-data-model\/?#core-data-model"},{"key":"e_1_3_2_25_2","article-title":"More reliable AI solution: Breast ultrasound diagnosis using multi-AI combination","author":"Dai Jian","year":"2021","unstructured":"Jian Dai, Shuge Lei, Licong Dong, Xiaona Lin, Huabin Zhang, Desheng Sun, and Kehong Yuan. 2021. More reliable AI solution: Breast ultrasound diagnosis using multi-AI combination. arXiv preprint arXiv:2101.02639 (2021).","journal-title":"arXiv preprint arXiv:2101.02639"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278765"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30371-6_6"},{"key":"e_1_3_2_28_2","unstructured":"DISER (Australian Government). 2020. Australia\u2019s AI Ethics Principles. Retrieved August 17 2022 from https:\/\/industry.gov.au\/data-and-publications\/australias-artificial-intelligence-ethics-framework\/australias-ai-ethics-principles"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-022-00205-0"},{"key":"e_1_3_2_30_2","first-page":"1","volume-title":"Proceedings of the Conference on Robot Learning","author":"Dosovitskiy Alexey","year":"2017","unstructured":"Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017. CARLA: An open urban driving simulator. In Proceedings of the Conference on Robot Learning. 1\u201316."},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2019.2921037"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-020-00011-6"},{"key":"e_1_3_2_33_2","unstructured":"Iker Esnaola-Gonzalez. 2021. An ontology-based approach for making machine learning systems accountable. Semantic Web 1 (2021) 1\u20135."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00370-7"},{"key":"e_1_3_2_35_2","article-title":"A distributed \u2018black box\u2019 audit trail design specification for connected and automated vehicle data and software assurance","author":"Falco Gregory","year":"2020","unstructured":"Gregory Falco and Joshua E. Siegel. 2020. A distributed \u2018black box\u2019 audit trail design specification for connected and automated vehicle data and software assurance. arXiv preprint arXiv:2002.02780 (2020).","journal-title":"arXiv preprint arXiv:2002.02780"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE5003.2020.00032"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462518"},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","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. HLS White Paper. Berkman Klein Center for Internet & Society.","DOI":"10.2139\/ssrn.3518482"},{"key":"e_1_3_2_39_2","volume-title":"Proceedings of the European Conference on Information Systems (ECIS\u201921)","author":"Fukas Philipp","year":"2021","unstructured":"Philipp Fukas, Jonas Rebstadt, Florian Remark, and Oliver Thomas. 2021. Developing an artificial intelligence maturity model for auditing. In Proceedings of the European Conference on Information Systems (ECIS\u201921)."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2018.09.006"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-54549-9_13"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3458723"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-05990-7_9"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-78098-2_3"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigComp54360.2022.00088"},{"key":"e_1_3_2_46_2","first-page":"5249","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI\u201921)","author":"Henderson Jette","year":"2021","unstructured":"Jette Henderson, Shubham Sharma, Alan Gee, Valeri Alexiev, Steve Draper, Carlos Marin, Yessel Hinojosa, Christine Draper, Michael Perng, Luis Aguirre, Michael Li, Sara Rouhani, Shorya Consul, Susan Michalski, Akarsh Prasad, Mayank Chutani, Aditya Kumar, Shahzad Alam, Prajna Kandarpa, Binnu Jesudasan, Colton Lee, Michael Criscolo, Sinead Williamson, Matt Sanchez, and Joydeep Ghosh. 2021. Certifai: A toolkit for building trust in AI systems. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI\u201921). 5249\u20135251."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462564"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3064663.3064703"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278753"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/RE.2019.00050"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/MITP.2020.2973852"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3140230"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445918"},{"key":"e_1_3_2_54_2","first-page":"1663","article-title":"Operationalising AI ethics: How are companies bridging the gap between practice and principles? An exploratory study","author":"Ib\u00e1\u00f1ez Javier Camacho","year":"2021","unstructured":"Javier Camacho Ib\u00e1\u00f1ez and M\u00f3nica Villas Olmeda. 2021. Operationalising AI ethics: How are companies bridging the gap between practice and principles? An exploratory study. AI & SOCIETY 37 (2021), 1663\u20131687.","journal-title":"AI & SOCIETY"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1177\/2374289521990784"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445923"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30391-4_5"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0088-2"},{"key":"e_1_3_2_59_2","first-page":"14","volume-title":"Proceedings of the International Conference on Software Business","author":"John Meenu Mary","year":"2020","unstructured":"Meenu Mary John, Helena Holmstr\u00f6m Olsson, and Jan Bosch. 2020. Architecting AI deployment: A systematic review of state-of-the-art and state-of-practice literature. In Proceedings of the International Conference on Software Business. 14\u201329."},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278774"},{"key":"e_1_3_2_61_2","volume-title":"Guidelines for Performing Systematic Literature Reviews in Software Engineering","author":"Kitchenham B. A.","year":"2007","unstructured":"B. A. Kitchenham and S. Charters. 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report. EBSE."},{"key":"e_1_3_2_62_2","article-title":"N-version programming","author":"Knight John C.","year":"2002","unstructured":"John C. Knight. 2002. N-version programming. In Encyclopedia of Software Engineering. Wiley.","journal-title":"Encyclopedia of Software Engineering."},{"key":"e_1_3_2_63_2","volume-title":"ACMIUI-WS 2021: Joint Proceedings of the ACM IUI 2021 Workshops","volume":"2903","author":"Larasati Retno","year":"2021","unstructured":"Retno Larasati, Anna De Liddo, and Enrico Motta. 2021. AI healthcare system interface: Explanation design for non-expert user trust. In ACMIUI-WS 2021: Joint Proceedings of the ACM IUI 2021 Workshops. Vol. 2903. CEUR."},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3457335.3461705"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462572"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME52107.2021.00071"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376590"},{"key":"e_1_3_2_68_2","article-title":"Question-driven design process for Explainable AI user experiences","author":"Liao Q. Vera","year":"2021","unstructured":"Q. Vera Liao, Milena Pribi\u0107, Jaesik Han, Sarah Miller, and Daby Sow. 2021. Question-driven design process for Explainable AI user experiences. arXiv preprint arXiv:2104.03483 (2021).","journal-title":"arXiv preprint arXiv:2104.03483"},{"key":"e_1_3_2_69_2","article-title":"Blockchain-based trustworthy federated learning architecture","author":"Lo Sin Kit","year":"2021","unstructured":"Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang, Xiwei Xu, Hye-Young Paik, and Liming Zhu. 2021. Blockchain-based trustworthy federated learning architecture. arXiv preprint arXiv:2108.06912 (2021).","journal-title":"arXiv preprint arXiv:2108.06912"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86044-8_6"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111357"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3522664.3528607"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2014.06.004"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/3148541"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.5555\/273448"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75018-3_14"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa085"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_79_2","article-title":"HAX Toolkit","year":"2022","unstructured":"Microsoft. 2022. HAX Toolkit. Retrieved August 22, 2022 from https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/","journal-title":"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/"},{"key":"e_1_3_2_80_2","first-page":"5276","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI\u201921)","author":"Miguel Beatriz San","year":"2021","unstructured":"Beatriz San Miguel, Aisha Naseer, and Hiroya Inakoshi. 2021. Putting accountability of AI systems into practice. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI\u201921). 5276\u20135278."},{"key":"e_1_3_2_81_2","volume-title":"Proceedings of the 1st Workshop on Pre-Training: Perspectives, Pitfalls, and Paths Forward at ICML 2022","author":"Mitchell Eric","year":"2002","unstructured":"Eric Mitchell, Peter Henderson, Christopher D. Manning, Dan Jurafsky, and Chelsea Finn. 2002. Self-destructing models: Increasing the costs of harmful dual uses in foundation models. In Proceedings of the 1st Workshop on Pre-Training: Perspectives, Pitfalls, and Paths Forward at ICML 2022."},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/WAIN52551.2021.00026"},{"key":"e_1_3_2_84_2","unstructured":"Christian Murphy Gail E. Kaiser and Marta Arias. 2007. An Approach to Software Testing of Machine Learning Applications. Columbia University."},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/RAMS48030.2020.9153718"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-77385-4_10"},{"key":"e_1_3_2_87_2","article-title":"The Minimum Elements for a Software Bill of Materials (SBOM)","year":"2021","unstructured":"NTIA. 2021. The Minimum Elements for a Software Bill of Materials (SBOM). Retrieved August 18, 2022 from https:\/\/www.ntia.doc.gov\/files\/ntia\/publications\/sbom_minimum_elements_report.pdf","journal-title":"https:\/\/www.ntia.doc.gov\/files\/ntia\/publications\/sbom_minimum_elements_report.pdf"},{"key":"e_1_3_2_88_2","unstructured":"OECD. 2021. Tools for Trustworthy AI. Retrieved October 14 2023 from https:\/\/www.oecd-ilibrary.org\/content\/paper\/008232ec-en"},{"key":"e_1_3_2_89_2","article-title":"The Minimum Elements for a Software Bill of Materials (SBOM)","author":"Commerce The United States Department of","year":"2021","unstructured":"The United States Department of Commerce. 2021. The Minimum Elements for a Software Bill of Materials (SBOM). Retrieved August 17, 2022 from https:\/\/www.ntia.doc.gov\/files\/ntia\/publications\/sbom_minimum_elements_report.pdf","journal-title":"https:\/\/www.ntia.doc.gov\/files\/ntia\/publications\/sbom_minimum_elements_report.pdf"},{"key":"e_1_3_2_90_2","article-title":"People + AI Guidebook","author":"PAIR Google","year":"2021","unstructured":"Google PAIR. 2021. People + AI Guidebook. Retrieved August 17, 2022 from https:\/\/pair.withgoogle.com\/guidebook","journal-title":"https:\/\/pair.withgoogle.com\/guidebook"},{"key":"e_1_3_2_91_2","volume-title":"CKGSemStats@ISWC","author":"Pandit Harshvardhan Jitendra","year":"2018","unstructured":"Harshvardhan Jitendra Pandit, Declan O\u2019Sullivan, and Dave Lewis. 2018. Towards knowledge-based systems for GDPR compliance. In Proceedings ofCKGSemStats@ISWC."},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-85447-8_19"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/52.219617"},{"key":"e_1_3_2_94_2","article-title":"The impact of considering human values during requirements engineering activities","author":"Perera Harsha","year":"2021","unstructured":"Harsha Perera, Rashina Hoda, Rifat Ara Shams, Arif Nurwidyantoro, Mojtaba Shahin, Waqar Hussain, and Jon Whittle. 2021. The impact of considering human values during requirements engineering activities. arXiv preprint arXiv:2111.15293 (2021).","journal-title":"arXiv preprint arXiv:2111.15293"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372873"},{"issue":"2","key":"e_1_3_2_96_2","first-page":"91","article-title":"Risk analysis\u2014A subjective process","volume":"12","author":"Redmill Felix","year":"2002","unstructured":"Felix Redmill. 2002. Risk analysis\u2014A subjective process. Engineering Management Journal 12, 2 (2002), 91\u201396.","journal-title":"Engineering Management Journal"},{"key":"e_1_3_2_97_2","volume-title":"Proceedings of the Software Engineering 2022 Workshops","author":"Regli Christoph","year":"2022","unstructured":"Christoph Regli and Bj\u00f6rn Annighoefer. 2022. An anthropomorphic approach to establish an additional layer of trustworthiness of an AI pilot. In Proceedings of the Software Engineering 2022 Workshops."},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1111\/bioe.12887"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1177\/20539517211040197"},{"key":"e_1_3_2_100_2","article-title":"Principles to practices for responsible AI: Closing the gap","author":"Schiff Daniel","year":"2020","unstructured":"Daniel Schiff, Bogdana Rakova, Aladdin Ayesh, Anat Fanti, and Michael Lennon. 2020. Principles to practices for responsible AI: Closing the gap. arXiv preprint arXiv:2006.04707 (2020).","journal-title":"arXiv preprint arXiv:2006.04707"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-022-00150-y"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00146-018-0867-z"},{"issue":"2012","key":"e_1_3_2_103_2","first-page":"1","article-title":"Modeling, simulation, information technology & processing roadmap","volume":"32","author":"Shafto Mike","year":"2012","unstructured":"Mike Shafto, Mike Conroy, Rich Doyle, Ed Glaessgen, Chris Kemp, Jacqueline LeMoigne, and Lui Wang. 2012. Modeling, simulation, information technology & processing roadmap. National Aeronautics and Space Administration 32, 2012 (2012), 1\u201338.","journal-title":"National Aeronautics and Space Administration"},{"key":"e_1_3_2_104_2","article-title":"Structured access to AI capabilities: An emerging paradigm for safe AI deployment","author":"Shevlane Toby","year":"2022","unstructured":"Toby Shevlane. 2022. Structured access to AI capabilities: An emerging paradigm for safe AI deployment. arXiv preprint arXiv:2201.05159 (2022).","journal-title":"arXiv preprint arXiv:2201.05159"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1145\/3419764"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/3445973"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00022"},{"key":"e_1_3_2_108_2","first-page":"67","volume-title":"Proceedings of the International Conference on Deep Learning, Big Data, and Blockchain","author":"Six Nicolas","year":"2021","unstructured":"Nicolas Six, Andrea Perrichon-Chr\u00e9tien, and Nicolas Herbaut. 2021. SAIaaS: A blockchain-based solution for secure artificial intelligence as-a-service. In Proceedings of the International Conference on Deep Learning, Big Data, and Blockchain. 67\u201374."},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533156"},{"key":"e_1_3_2_110_2","unstructured":"Koen Smit Martijn Zoet and John van Meerten. 2020. A review of AI principles in practice. In Proceedings of the 24th Pacific Asia Conference on Information Systems (PACIS\u201920)."},{"key":"e_1_3_2_111_2","unstructured":"IEEE Computer Society P. Bourque and R. Fairley. 2014. Guide to the Software Engineering Body of Knowledge. SWEBOK."},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1145\/2889160.2889207"},{"key":"e_1_3_2_113_2","article-title":"Privacy-preserving AI-enabled video surveillance for social distancing: Responsible design and deployment for public spaces","author":"Sugianto Nehemia","year":"2021","unstructured":"Nehemia Sugianto, Dian Tjondronegoro, Rosemary Stockdale, and Elizabeth Irenne Yuwono. 2021. Privacy-preserving AI-enabled video surveillance for social distancing: Responsible design and deployment for public spaces. Information Technology & People. E-pub ahead of print.","journal-title":"Information Technology & People."},{"issue":"1","key":"e_1_3_2_114_2","first-page":"22","article-title":"A novel approach to machine learning application to protection privacy data in healthcare: Federated learning","volume":"8","author":"S\u00fczen Ahmet Ali","year":"2020","unstructured":"Ahmet Ali S\u00fczen and Mehmet Ali \u015eim\u015fek. 2020. A novel approach to machine learning application to protection privacy data in healthcare: Federated learning. Nam\u0131k Kemal T\u0131p Dergisi 8, 1 (2020), 22\u201330.","journal-title":"Nam\u0131k Kemal T\u0131p Dergisi"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1080\/01691864.2019.1689168"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrt.2022.100031"},{"key":"e_1_3_2_117_2","volume-title":"Proceedings of the European Conference on Information Systems (ECIS\u201920)","author":"Vassilakopoulou Polyxeni","year":"2020","unstructured":"Polyxeni Vassilakopoulou. 2020. Sociotechnical approach for accountability by design in AI systems. In Proceedings of the European Conference on Information Systems (ECIS\u201920)."},{"key":"e_1_3_2_118_2","first-page":"329","volume-title":"Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV","author":"Verachtert Wilfried","year":"2021","unstructured":"Wilfried Verachtert, Thomas J. Ashby, Imen Chakroun, Roel Wuyts, Sayantan Das, Sandip Halder, and Philippe Leray. 2021. Privacy preserving amalgamated machine learning for process control. In Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV. Vol. 11611. SPIE, 329\u2013341."},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1109\/REW.2019.00050"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1109\/PuneCon50868.2020.9362382"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03583-3"},{"issue":"5","key":"e_1_3_2_122_2","first-page":"2438","article-title":"DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive","volume":"18","author":"Weng Jiasi","year":"2019","unstructured":"Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li, Yue Zhang, and Weiqi Luo. 2019. DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing 18, 5 (2019), 2438\u20132455.","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-64107-2_21"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom50675.2020.00029"},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-73959-1_7"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3032544"},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68534-8_19"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2022.2093773"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72188-6_2"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.3389\/fhumd.2021.688152"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTS.2021.3066209"},{"key":"e_1_3_2_132_2","first-page":"30","article-title":"On assessing trustworthy AI in healthcare. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls","author":"Zicari Roberto V.","year":"2021","unstructured":"Roberto V. Zicari, James Brusseau, Stig Nikolaj Blomberg, Helle Collatz Christensen, Megan Coffee, Marianna B. Ganapini, Sara Gerke, Thomas Krendl Gilbert, Eleanore Hickman, Elisabeth Hildt, et\u00a0al. 2021. On assessing trustworthy AI in healthcare. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Frontiers in Human Dynamics 3 (2021), 30.","journal-title":"Frontiers in Human Dynamics"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626234","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:05:47Z","timestamp":1750291547000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,9]]},"references-count":131,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3626234"],"URL":"https:\/\/doi.org\/10.1145\/3626234","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,9]]},"assertion":[{"value":"2022-09-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}