{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:27:39Z","timestamp":1764588459516,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031408366"},{"type":"electronic","value":"9783031408373"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:00:00Z","timestamp":1692662400000},"content-version":"vor","delay-in-days":233,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>It is commonly understood that the resilience of critical information technology (IT) systems based on artificial intelligence (AI) must be ensured. In this regard, we consider resilience both in terms of IT security threats, such as cyberattacks, as well as the ability to robustly persist under uncertain and changing environmental conditions, such as climate change or economic crises. This paper explores the relationship between resilience and sustainability with regard to AI systems, develops fields of action for resilient AI, and elaborates direct and indirect influences on the achievement of the United Nations Sustainable Development Goals. Indirect in this case means that a sustainability effect is reached by taking resilience measures when applying AI in a sustainability-relevant application area, for example precision agriculture or smart health.<\/jats:p>","DOI":"10.1007\/978-3-031-40837-3_12","type":"book-chapter","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T23:02:25Z","timestamp":1692658945000},"page":"188-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sustainability Effects of\u00a0Robust and\u00a0Resilient Artificial Intelligence"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-2535","authenticated-orcid":false,"given":"Torsten","family":"Priebe","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2847-2152","authenticated-orcid":false,"given":"Peter","family":"Kieseberg","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6233-1353","authenticated-orcid":false,"given":"Alexander","family":"Adrowitzer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1053-8421","authenticated-orcid":false,"given":"Oliver","family":"Eigner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1611-2598","authenticated-orcid":false,"given":"Fabian","family":"Kovac","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"issue":"3","key":"12_CR1","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1016\/j.gsf.2019.10.001","volume":"11","author":"Y Achour","year":"2020","unstructured":"Achour, Y., Pourghasemi, H.R.: How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci. Front. 11(3), 871\u2013883 (2020). https:\/\/doi.org\/10.1016\/j.gsf.2019.10.001","journal-title":"Geosci. Front."},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Adensamer, A., Klausner, L.D.: Part man, part machine, all cop: automation in policing. Front. Artif. Intell. Forthcom. (2021)","DOI":"10.3389\/frai.2021.655486"},{"key":"12_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.124077","volume":"277","author":"A Akande","year":"2020","unstructured":"Akande, A., Cabral, P., Casteleyn, S.: Understanding the sharing economy and its implication on sustainability in smart cities. J. Clean. Prod. 277, 124077 (2020)","journal-title":"J. Clean. Prod."},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Barocas, S., Selbst, A.D.: Big data\u2019s disparate impact. 104 California Law Review, p. 671 (2016). https:\/\/doi.org\/10.2139\/ssrn.2477899, https:\/\/www.ssrn.com\/abstract=2477899","DOI":"10.2139\/ssrn.2477899"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Breit, A., et al.: Combining machine learning and semantic web: a systematic mapping study. ACM Comput. Surv. (2023). https:\/\/doi.org\/10.1145\/3586163","DOI":"10.1145\/3586163"},{"key":"12_CR6","unstructured":"Case, D.U.: Analysis of the cyber attack on the Ukrainian power grid. Electr. Inf. Shar. Anal. Center (E-ISAC) 388, 1\u201329 (2016)"},{"key":"12_CR7","first-page":"323","volume":"2","author":"DD Clark","year":"2011","unstructured":"Clark, D.D., Landau, S.: Untangling attribution. Harv. Nat\u2019l Sec. J. 2, 323 (2011)","journal-title":"Harv. Nat\u2019l Sec. J."},{"key":"12_CR8","unstructured":"Das, N., et al.: MLsploit: a framework for interactive experimentation with adversarial machine learning research. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201919, ACM, New York, NY (2019). https:\/\/www.kdd.org\/kdd2019\/docs\/KDD2019_Showcase_2062.pdf"},{"key":"12_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103008","volume":"125","author":"X Di","year":"2021","unstructured":"Di, X., Shi, R.: A survey on autonomous vehicle control in the era of mixed-autonomy: from physics-based to AI-guided driving policy learning. Transp. Res. Part C Emerg. Technol. 125, 103008 (2021)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"12_CR10","unstructured":"Doll, T.: K\u00fcnstliche Intelligenz in den Landstreitkr\u00e4ften. Amt f\u00fcr Heeresentwicklung (2019). https:\/\/www.bundeswehr.de\/resource\/blob\/156024\/d6ac452e72f77f3cc071184ae34dbf0e\/download-positionspapier-deutsche-version-data.pdf"},{"key":"12_CR11","doi-asserted-by":"publisher","unstructured":"Eigner, O., et al.: Towards resilient artificial intelligence: survey and research issues. In: 2021 IEEE International Conference on Cyber Security and Resilience, pp. 536\u2013542. CSR 2021, IEEE, Washington, DC (2021). https:\/\/doi.org\/10.1109\/CSR51186.2021.9527986","DOI":"10.1109\/CSR51186.2021.9527986"},{"key":"12_CR12","unstructured":"European Commission: Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts (2021). https:\/\/eur-lex.europa.eu\/legal-content\/EN\/ALL\/?uri=celex:52021PC0206 , proposal for a Regulation of the European Parliament and of the Council, No. COM\/2021\/206 final"},{"issue":"6","key":"12_CR13","doi-asserted-by":"publisher","first-page":"565","DOI":"10.2217\/pme.13.57","volume":"10","author":"M Flores","year":"2013","unstructured":"Flores, M., Glusman, G., Brogaard, K., Price, N.D., Hood, L.: P4 medicine: how systems medicine will transform the healthcare sector and society. Pers. Med. 10(6), 565\u2013576 (2013)","journal-title":"Pers. Med."},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Frenken, K., Schor, J.: Putting the sharing economy into perspective. In: A Research Agenda for Sustainable Consumption Governance, pp. 121\u2013135. Edward Elgar Publishing (2019)","DOI":"10.4337\/9781788117814.00017"},{"key":"12_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/978-3-319-99740-7_21","volume-title":"Machine Learning and Knowledge Extraction","author":"R Goebel","year":"2018","unstructured":"Goebel, R., et al.: Explainable AI: the new 42? In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2018. LNCS, vol. 11015, pp. 295\u2013303. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99740-7_21"},{"key":"12_CR16","unstructured":"Gro\u00dfklaus, M.: Vom Modewort zum transformativen Hebel: Wie die Konjunktur des Resilienzbegriffs f\u00fcr die digital-\u00f6kologische Transformation genutzt werden kann. IZT (2022). https:\/\/codina-transformation.de\/transformative-resilienz\/, CO:DINA position paper no. 11"},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"High-Level Expert Group on Artificial Intelligence: Ethics Guidelines for Trustworthy AI. Publications Office of the European Union, Luxembourg (2019). https:\/\/doi.org\/10.2759\/346720","DOI":"10.2759\/346720"},{"key":"12_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-84060-0_1","volume-title":"Machine Learning and Knowledge Extraction","author":"A Holzinger","year":"2021","unstructured":"Holzinger, A., Weippl, E., Tjoa, A.M., Kieseberg, P.: Digital transformation for sustainable development goals (SDGs) - a security, safety and privacy perspective on AI. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2021. LNCS, vol. 12844, pp. 1\u201320. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-84060-0_1"},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Institute for the Protection and Security of the Citizen (Joint Research Centre): Towards Testing Critical Infrastructure Resilience. Publications Office of the European Union, Luxembourg (2014). https:\/\/doi.org\/10.2788\/41633","DOI":"10.2788\/41633"},{"key":"12_CR20","unstructured":"Java, O., Asprion, B., Priebe, T., Sarkozi, E., Neves Madeira, R.: Application of digital technology in agriculture: potential support for winegrowers. In: Proceeding of the 8th International Conference on Trends in Agricultural Engineering 2022. Prague (2022). https:\/\/2022.tae-conference.cz\/proceeding\/TAE2022-32-Oskars-JAVA.pdf"},{"key":"12_CR21","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s43681-021-00039-2","volume":"1","author":"E Kazim","year":"2021","unstructured":"Kazim, E., Denny, D.M.T., Koshiyama, A.: AI auditing and impact assessment: according to the UK information commissioner\u2019s office. AI Ethics 1, 301\u2013310 (2021). https:\/\/doi.org\/10.1007\/s43681-021-00039-2","journal-title":"AI Ethics"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Klinkenberg, R.: Learning drifting concepts: example selection vs. example weighting. Intell. Data Anal. 8(3), 281\u2013300 (2004). https:\/\/dl.acm.org\/doi\/10.5555\/1293831.1293836","DOI":"10.3233\/IDA-2004-8305"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Kong, Z., Xue, J., Wang, Y., Huang, L., Niu, Z., Li, F.: A survey on adversarial attack in the age of artificial intelligence. Wirel. Commun. Mob. Comput. 2021, TBD (2021). https:\/\/doi.org\/10.1155\/2021\/4907754","DOI":"10.1155\/2021\/4907754"},{"key":"12_CR24","unstructured":"Kosow, H., Ga\u00dfner, R.: Methods of future and scenario analysis: overview, assessment, and selection criteria, vol. 39. DEU (2008)"},{"key":"12_CR25","unstructured":"KPMG: AI Risk and Controls Matrix (2018). https:\/\/assets.kpmg\/content\/dam\/kpmg\/uk\/pdf\/2018\/09\/ai-risk-and-controls-matrix.pdf"},{"key":"12_CR26","unstructured":"Larson, J., Mattu, S., Kirchner, L., Angwin, J.: How We Analyzed the COMPAS Recidivism Algorithm. ProPublica (2016). https:\/\/www.propublica.org\/article\/how-we-analyzed-the-compas-recidivism-algorithm"},{"key":"12_CR27","doi-asserted-by":"publisher","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 55(6) (2022). https:\/\/doi.org\/10.1145\/3457607","DOI":"10.1145\/3457607"},{"issue":"12","key":"12_CR28","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1080\/01900692.2019.1575664","volume":"42","author":"A Meijer","year":"2019","unstructured":"Meijer, A., Wessels, M.: Predictive policing: review of benefits and drawbacks. Int. J. Public Adm. 42(12), 1031\u20131039 (2019)","journal-title":"Int. J. Public Adm."},{"key":"12_CR29","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.procs.2022.10.118","volume":"210","author":"R Neves Madeira","year":"2022","unstructured":"Neves Madeira, R., et al.: Towards digital twins for multi-sensor land and plant monitoring. Procedia Comput. Sci. 210, 45\u201352 (2022). https:\/\/doi.org\/10.1016\/j.procs.2022.10.118","journal-title":"Procedia Comput. Sci."},{"key":"12_CR30","doi-asserted-by":"publisher","unstructured":"Rammert, W.: Where the action is: distributed agency between humans, machines, and programs. In: Seifert, U., Kim, J.H., Moore, A. (eds.) Paradoxes of Interactivity: Perspectives for Media Theory, Human-Computer Interaction, and Artistic Investigations, pp. 62\u201391. transcript, Bielefeld (2008). https:\/\/doi.org\/10.14361\/9783839408421-004","DOI":"10.14361\/9783839408421-004"},{"issue":"1","key":"12_CR31","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1109\/MRA.2017.2787267","volume":"25","author":"L Righetti","year":"2018","unstructured":"Righetti, L., Pham, Q.C., Madhavan, R., Chatila, R.: Lethal autonomous weapon systems [ethical, legal, and societal issues]. IEEE Robot. Autom. Mag. 25(1), 123\u2013126 (2018)","journal-title":"IEEE Robot. Autom. Mag."},{"key":"12_CR32","doi-asserted-by":"publisher","unstructured":"Tao, F., Qi, Q., Wang, L., Nee, A.Y.C.: Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering (Beijing) 5(4), 653\u2013661 (2019). https:\/\/doi.org\/10.1016\/j.eng.2019.01.014","DOI":"10.1016\/j.eng.2019.01.014"},{"key":"12_CR33","unstructured":"Tjoa, S., Buttinger, C., Holzinger, K., Kieseberg, P.: Penetration testing artificial intelligence. ERCIM News 123, 36\u201337 (2020). https:\/\/ercim-news.ercim.eu\/en123\/r-i\/penetration-testing-artificial-intelligence"},{"key":"12_CR34","doi-asserted-by":"publisher","unstructured":"Tobin, J., et al.: Domain randomization for transferring deep neural networks from simulation to the real world. In: 2017 IEEE\/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 23\u201330. IROS 2017, IEEE, Washington, DC (2017). https:\/\/doi.org\/10.1109\/IROS.2017.8202133","DOI":"10.1109\/IROS.2017.8202133"},{"key":"12_CR35","unstructured":"United Nations: Transforming our World: The 2030 Agenda for Sustainable Development (2015). https:\/\/sdgs.un.org\/2030agenda, resolution No. A\/RES\/70\/1"},{"key":"12_CR36","doi-asserted-by":"publisher","unstructured":"Vermaas, P., Kroes, P., van de Poel, I., Franssen, M., Houkes, W.: A Philosophy of Technology: From Technical Artefacts to Sociotechnical Systems, Synthesis Lectures on Engineers, Technology, and Society, vol. 17. Morgan & Claypool, San Rafael, CA (2011). https:\/\/doi.org\/10.2200\/S00321ED1V01Y201012ETS014","DOI":"10.2200\/S00321ED1V01Y201012ETS014"},{"key":"12_CR37","unstructured":"Winter, P.M., et al.: Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications. T\u00dcV Austria, Brunn am Gebirge (2021). https:\/\/www.tuv.at\/loesungen\/digital-services\/trusted-ai"},{"key":"12_CR38","unstructured":"Yudkowsky, E.: The ai alignment problem: why it is hard, and where to start. Symbolic Systems Distinguished Speaker (2016). https:\/\/intelligence.org\/stanford-talk\/"},{"key":"12_CR39","doi-asserted-by":"publisher","unstructured":"Zelaya, C.V.G.: Towards explaining the effects of data preprocessing on machine learning. In: Proceedings of the 35th International Conference on Data Engineering. pp. 2086\u20132090. ICDE \u201919, IEEE Computer Society, Washington, DC (2019). https:\/\/doi.org\/10.1109\/ICDE.2019.00245","DOI":"10.1109\/ICDE.2019.00245"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40837-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:33:29Z","timestamp":1710333209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40837-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031408366","9783031408373"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40837-3_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CD-MAKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Cross-Domain Conference for Machine Learning and Knowledge Extraction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Benevento","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cd-make2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cd-make.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}