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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorBrunner, Stefan-
dc.contributor.authorFrischknecht-Gruber, Carmen-
dc.contributor.authorReif, Monika Ulrike-
dc.contributor.authorWeng, Joanna-
dc.date.accessioned2024-01-04T12:45:06Z-
dc.date.available2024-01-04T12:45:06Z-
dc.date.issued2023-
dc.identifier.isbn978-981-18-8071-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29452-
dc.description.abstractLegislators and authorities are working to establish a high level of trust in AI applications as they become more prevalent in our daily lives. As AI systems evolve and enter critical domains like healthcare and transportation, trust becomes essential, necessitating consideration of multiple aspects. AI systems must ensure fairness and impartiality in their decision-making processes to align with ethical standards. Autonomy and control are necessary to ensure the system remains aligned with societal values while being efficient and effective. Transparency in AI systems facilitates understanding decision-making processes, while reliability is paramount in diverse conditions, including errors, bias, or malicious attacks. Safety is of utmost importance in critical AI applications to prevent harm and adverse outcomes. This paper proposes a framework that utilizes various approaches to establish qualitative requirements and quantitative metrics for the entire application, employing a risk-based approach. These measures are then utilized to evaluate the AI system. To meet the requirements, various means (such as processes, methods, and documentation) are established at system level and then detailed and supplemented for different dimensions to achieve sufficient trust in the AI system. The results of the measures are evaluated individually and across dimensions to assess the extent to which the AI system meets the trustworthiness requirements.de_CH
dc.language.isoende_CH
dc.publisherResearch Publishingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSafe AIde_CH
dc.subjectTrustworthy AIde_CH
dc.subjectAI standardde_CH
dc.subjectArtificial intelligencede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleA comprehensive framework for ensuring the trustworthiness of AI systemsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.publisher.placeSingaporede_CH
dc.identifier.doi10.3850/978-981-18-8071-1_P230-cdde_CH
zhaw.conference.details33rd European Safety and Reliability Conference (ESREL), Southampton, United Kingdom, 3-7 September 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end2779de_CH
zhaw.pages.start2772de_CH
zhaw.parentwork.editorBrito, Mário P.-
zhaw.parentwork.editorAven, Terje-
zhaw.parentwork.editorBaraldi, Piero-
zhaw.parentwork.editorČepin, Marko-
zhaw.parentwork.editorZio, Enrico-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceeding of the 33rd European Safety and Reliability Conferencede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Brunner, S., Frischknecht-Gruber, C., Reif, M. U., & Weng, J. (2023). A comprehensive framework for ensuring the trustworthiness of AI systems [Conference paper]. In M. P. Brito, T. Aven, P. Baraldi, M. Čepin, & E. Zio (Eds.), Proceeding of the 33rd European Safety and Reliability Conference (pp. 2772–2779). Research Publishing. https://doi.org/10.3850/978-981-18-8071-1_P230-cd
Brunner, S. et al. (2023) ‘A comprehensive framework for ensuring the trustworthiness of AI systems’, in M.P. Brito et al. (eds) Proceeding of the 33rd European Safety and Reliability Conference. Singapore: Research Publishing, pp. 2772–2779. Available at: https://doi.org/10.3850/978-981-18-8071-1_P230-cd.
S. Brunner, C. Frischknecht-Gruber, M. U. Reif, and J. Weng, “A comprehensive framework for ensuring the trustworthiness of AI systems,” in Proceeding of the 33rd European Safety and Reliability Conference, 2023, pp. 2772–2779. doi: 10.3850/978-981-18-8071-1_P230-cd.
BRUNNER, Stefan, Carmen FRISCHKNECHT-GRUBER, Monika Ulrike REIF und Joanna WENG, 2023. A comprehensive framework for ensuring the trustworthiness of AI systems. In: Mário P. BRITO, Terje AVEN, Piero BARALDI, Marko ČEPIN und Enrico ZIO (Hrsg.), Proceeding of the 33rd European Safety and Reliability Conference. Conference paper. Singapore: Research Publishing. 2023. S. 2772–2779. ISBN 978-981-18-8071-1
Brunner, Stefan, Carmen Frischknecht-Gruber, Monika Ulrike Reif, and Joanna Weng. 2023. “A Comprehensive Framework for Ensuring the Trustworthiness of AI Systems.” Conference paper. In Proceeding of the 33rd European Safety and Reliability Conference, edited by Mário P. Brito, Terje Aven, Piero Baraldi, Marko Čepin, and Enrico Zio, 2772–79. Singapore: Research Publishing. https://doi.org/10.3850/978-981-18-8071-1_P230-cd.
Brunner, Stefan, et al. “A Comprehensive Framework for Ensuring the Trustworthiness of AI Systems.” Proceeding of the 33rd European Safety and Reliability Conference, edited by Mário P. Brito et al., Research Publishing, 2023, pp. 2772–79, https://doi.org/10.3850/978-981-18-8071-1_P230-cd.


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