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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30549
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dc.contributor.authorWeng, Joanna-
dc.contributor.authorDenzel, Philipp-
dc.contributor.authorReif, Monika Ulrike-
dc.contributor.authorSchilling, Frank-Peter-
dc.contributor.authorBilleter, Yann-
dc.contributor.authorFrischknecht-Gruber, Carmen-
dc.contributor.authorBrunner, Stefan-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorRepetto, Marco-
dc.contributor.authorIranfar, Arman-
dc.date.accessioned2024-04-26T13:51:19Z-
dc.date.available2024-04-26T13:51:19Z-
dc.date.issued2024-06-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30549-
dc.description.abstractArtificial Intelligence (AI) and machine learning (ML) algorithms are making an impact in an increasing number of industries. AI models differ from conventional software due to their probabilistic decision-making process, with a heavy reliance on training data quantity and quality. Ensuring the trustworthiness of AI-based systems (AIS), including dimensions such as reliability and transparency, is becoming increasingly vital due to their widespread adoption. As regulatory standards are put in place, it becomes essential to have practical guidelines for certification. In this paper, we present an ongoing effort to develop a validated Certification Scheme for AIS. This scheme encompasses distinct objectives, criteria, and corresponding measures, as well as specific metrics and technical methods which support the implementation of trustworthy AI. A critical aspect of this scheme is the establishment of a clear connection between the set of requirements and the validated ML algorithms and methods used to evaluate the compliance of AIS. We provide a tangible example of the workflow for the reliability dimension on a hypothetical real-life use case: employing the Yolo5 model for the detection of construction vehicles in a diverse image dataset of construction sites. This example demonstrates the step-by-step process of the Certification Scheme from establishing initial requirements to selecting and applying technical methods for two example objectives.de_CH
dc.language.isoende_CH
dc.publisherZHAW Zürcher Hochschule für Angewandte Wissenschaftende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectAIde_CH
dc.subjectExplainabilityde_CH
dc.subjectTrustworthinessde_CH
dc.subjectSafetyde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleCertification scheme for artificial intelligence based systemsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.21256/zhaw-30549-
zhaw.conference.details34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedIntelligent Vision Systemsde_CH
zhaw.webfeedResponsible Artificial Intelligence Innovationde_CH
zhaw.funding.zhawcertAInty – A Certification Scheme for AI systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Weng, J., Denzel, P., Reif, M. U., Schilling, F.-P., Billeter, Y., Frischknecht-Gruber, C., Brunner, S., Chavarriaga, R., Repetto, M., & Iranfar, A. (2024, June). Certification scheme for artificial intelligence based systems. 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. https://doi.org/10.21256/zhaw-30549
Weng, J. et al. (2024) ‘Certification scheme for artificial intelligence based systems’, in 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30549.
J. Weng et al., “Certification scheme for artificial intelligence based systems,” in 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024, Jun. 2024. doi: 10.21256/zhaw-30549.
WENG, Joanna, Philipp DENZEL, Monika Ulrike REIF, Frank-Peter SCHILLING, Yann BILLETER, Carmen FRISCHKNECHT-GRUBER, Stefan BRUNNER, Ricardo CHAVARRIAGA, Marco REPETTO und Arman IRANFAR, 2024. Certification scheme for artificial intelligence based systems. In: 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Juni 2024
Weng, Joanna, Philipp Denzel, Monika Ulrike Reif, Frank-Peter Schilling, Yann Billeter, Carmen Frischknecht-Gruber, Stefan Brunner, Ricardo Chavarriaga, Marco Repetto, and Arman Iranfar. 2024. “Certification Scheme for Artificial Intelligence Based Systems.” Conference paper. In 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30549.
Weng, Joanna, et al. “Certification Scheme for Artificial Intelligence Based Systems.” 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2024, https://doi.org/10.21256/zhaw-30549.


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