Machine learning (ML) systems are vulnerable to performance decline over time due to dataset shift. To address this problem, experts often suggest that ML systems should be regularly updated to ensure ongoing performance stability. Some scholarly literature has begun to address the epistemic and ethical challenges associated with different updating methodologies. Thus far, however, little attention has been paid to the impact of model updating on the ML-assisted decision-making process itself, particularly in the AI ethics and AI epistemology literatures. This article aims to address this gap in the literature. It argues that model updating introduces a new sub-type of opacity into ML-assisted decision-making -- update opacity -- that occurs when users cannot understand how or why an update has changed the reasoning or behaviour of an ML system. This type of opacity presents a variety of distinctive epistemic and safety concerns that available solutions to the black box problem in ML are largely ill-equipped to address. A variety of alternative strategies may be developed or pursued to address the problem of update opacity more directly, including bi-factual explanations, dynamic model reporting, and update compatibility. However, each of these strategies presents its own risks or carries significant limitations. Further research will be needed to address the epistemic and safety concerns associated with model updating and update opacity going forward.
View on arXiv@article{hatherley2025_2504.05210,
title={ A moving target in AI-assisted decision-making: Dataset shift, model updating, and the problem of update opacity },
author={ Joshua Hatherley },
journal={arXiv preprint arXiv:2504.05210},
year={ 2025 }
}