mlsanityData to 7 Oct 2026

Trustworthy Artificial Intelligence in the Context of Metrology

arXiv 2406.10117 ↗First posted 14 Jun 2024cs.LGcited by 3

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Abstract (arXiv)

We review research at the National Physical Laboratory (NPL) in the area of trustworthy artificial intelligence (TAI), and more specifically trustworthy machine learning (TML), in the context of metrology, the science of measurement. We describe three broad themes of TAI: technical, socio-technical and social, which play key roles in ensuring that the developed models are trustworthy and can be relied upon to make responsible decisions. From a metrology perspective we emphasise uncertainty quantification (UQ), and its importance within the framework of TAI to enhance transparency and trust in the outputs of AI systems. We then discuss three research areas within TAI that we are working on at NPL, and examine the certification of AI systems in terms of adherence to the characteristics of TAI.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 56 references that are in the map. First posted on arXiv 14 Jun 2024.

Builds on

15 of 56 references.

References · 15

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  2. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
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3 more
  1. 2023Regulating ChatGPT and other Large Generative AI Modelscited by 102
  2. 2020Getting a CLUE: A Method for Explaining Uncertainty Estimatescited by 92
  3. 2020The Unreasonable Effectiveness of Deep Learning in Artificial Intelligencecited by 57

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025An uncertainty-aware Bayesian framework for machine learning classification models: A case study in land cover classificationcited by 1
  2. 2026TRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domainscited by 1
  3. 2026AI Assurance in UK Defence: Challenges in Operationalising JSP 936cited by 0