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.

翻译：本文综述了英国国家物理实验室（NPL）在计量学（测量科学）背景下，于可信人工智能（TAI）领域，特别是可信机器学习（TML）方面的研究。我们阐述了可信人工智能的三个广泛主题：技术层面、社会技术层面和社会层面，这些主题在确保所开发的模型可信且能够被依赖以做出负责任决策方面发挥着关键作用。从计量学的视角，我们强调了不确定性量化（UQ）及其在可信人工智能框架内的重要性，以增强人工智能系统输出的透明度和可信度。随后，我们讨论了NPL正在开展的可信人工智能领域内的三个研究方向，并从遵循可信人工智能特性的角度探讨了人工智能系统的认证问题。