Can you trust your ML metrics? Using Subjective Logic to determine the true contribution of ML metrics for safety

Herd, Benjamin and Burton, Simon orcid.org/0000-0001-9040-8752 (2024) Can you trust your ML metrics? Using Subjective Logic to determine the true contribution of ML metrics for safety. In: 39th Annual ACM Symposium on Applied Computing, SAC 2024. 39th Annual ACM Symposium on Applied Computing, SAC 2024, 08-12 Apr 2024 Proceedings of the ACM Symposium on Applied Computing . Association for Computing Machinery, Inc , ESP , pp. 1579-1586.

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Item Type: Proceedings Paper
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Publisher Copyright: © 2024 Copyright held by the owner/author(s).

Keywords: machine learning,safety assurance,subjective logic,uncertainty
Dates:
  • Published: 8 April 2024
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Computer Science (York)
Depositing User: Pure (York)
Date Deposited: 06 Aug 2025 09:40
Last Modified: 06 Aug 2025 09:40
Published Version: https://doi.org/10.1145/3605098.3635966
Status: Published
Publisher: Association for Computing Machinery, Inc
Series Name: Proceedings of the ACM Symposium on Applied Computing
Identification Number: 10.1145/3605098.3635966
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Filename: 3605098.3635966.pdf

Description: Can you trust your ML metrics? Using Subjective Logic to determine the true contribution of ML metrics for safety

Licence: CC-BY 2.5

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