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cs.LG2026
An uncertainty-aware Bayesian framework for machine learning classification models: A case study in land cover classification
Samuel Bilson, Miles McCrory, Anna Pustogvar
Ensuring that predictions of machine learning (ML) classification models are accompanied by uncertainty estimates is one of the main pillars of trustworthy AI. Current research in…
cs.LG2025
A metrological framework for uncertainty evaluation in machine learning classification models
Samuel Bilson, Maurice Cox, Anna Pustogvar +1
Machine learning (ML) classification models are increasingly being used in a wide range of applications where it is important that predictions are accompanied by uncertainties, inc…
cs.LG2024
Trustworthy Artificial Intelligence in the Context of Metrology
Tameem Adel, Sam Bilson, Mark Levene +1
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…