{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T02:32:59Z","timestamp":1776825179299,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":10,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union?s Horizon 2020 research and innovation program","award":["957197"],"award-info":[{"award-number":["957197"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,16]]},"DOI":"10.1145\/3522664.3528615","type":"proceedings-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T16:30:14Z","timestamp":1666024214000},"page":"43-45","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Structural causal models as boundary objects in AI system development"],"prefix":"10.1145","author":[{"given":"Hans-Martin","family":"Heyn","sequence":"first","affiliation":[{"name":"University of Gothenburg, Sweden"}]},{"given":"Eric","family":"Knauss","sequence":"additional","affiliation":[{"name":"University of Gothenburg, Sweden"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Markus Borg Cristofer Englund et al. 2018. Safely entering the deep: A review of verification and validation for machine learning and a challenge elicitation in the automotive industry. arXiv preprint arXiv:1812.05389 (2018)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20652-9_14"},{"key":"e_1_3_2_1_3_1","volume-title":"Technical Report 957197. Horizon 2020 Research Framework. https:\/\/vedliot.eu\/deliverable\/deliverable-d23\/","author":"Meierh\u00f6fer Franz","year":"2021","unstructured":"Franz Meierh\u00f6fer, Roland Weiss, et al. 2021. Specification for selected pilots \/ use cases. Technical Report 957197. Horizon 2020 Research Framework. https:\/\/vedliot.eu\/deliverable\/deliverable-d23\/"},{"key":"e_1_3_2_1_4_1","volume-title":"Causal inference in statistics: An overview. Statistics surveys 3","author":"Pearl Judea","year":"2009","unstructured":"Judea Pearl. 2009. Causal inference in statistics: An overview. Statistics surveys 3 (2009), 96--146."},{"key":"e_1_3_2_1_5_1","volume-title":"Possible Minds: 25 Ways of Looking at AI","author":"Pearl Judea","unstructured":"Judea Pearl. 2019. The Limitations of Opaque Learning Machines. In Possible Minds: 25 Ways of Looking at AI, Johnm Brockman (Ed.). Penguin Press, London, Chapter 2."},{"key":"e_1_3_2_1_6_1","volume-title":"Causal inference in statistics: A primer","author":"Pearl Judea","unstructured":"Judea Pearl, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons."},{"key":"e_1_3_2_1_7_1","volume-title":"Elements of causal inference: foundations and learning algorithms","author":"Peters Jonas","unstructured":"Jonas Peters, Dominik Janzing, and Bernhard Sch\u00f6lkopf. 2017. Elements of causal inference: foundations and learning algorithms. The MIT Press."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3055015"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Laura von Rueden Sebastian Mayer et al. 2021. Informed Machine Learning-A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. IEEE Transactions on Knowledge and Data Engineering (2021).","DOI":"10.1109\/TKDE.2021.3079836"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2166"}],"event":{"name":"CAIN '22: 1st Conference on AI Engineering - Software Engineering for AI","location":"Pittsburgh Pennsylvania","acronym":"CAIN '22","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE TCSC IEEE Technical Committee on Scalable Computing"]},"container-title":["Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522664.3528615","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3522664.3528615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:34Z","timestamp":1750183774000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522664.3528615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,16]]},"references-count":10,"alternative-id":["10.1145\/3522664.3528615","10.1145\/3522664"],"URL":"https:\/\/doi.org\/10.1145\/3522664.3528615","relation":{},"subject":[],"published":{"date-parts":[[2022,5,16]]},"assertion":[{"value":"2022-10-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}