# Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges

arXiv: https://arxiv.org/abs/2504.08861 · HTML: https://mlsanity.com/paper/2504.08861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Limits of trust in medical AI](https://mlsanity.com/paper/2503.16692/) (2025)
- [Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems](https://mlsanity.com/paper/2008.08060/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Trustworthiness-based Metaphysics of Artificial Intelligence Systems](https://mlsanity.com/paper/2506.03233/) (2025)
- [Federated learning, ethics, and the double black box problem in medical AI](https://mlsanity.com/paper/2504.20656/) (2025)
- [Update Opacity: Epistemic Accessibility and Governance Under AI System Change](https://mlsanity.com/paper/2606.00037/) (2026)

## Abstract (arXiv)

Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this paper, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature. Target audience: The target audiences for this tutorial are the developers of machine learning AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. Scope: Discussions of adaptive ML systems to date have overlooked the distinction between two sorts of variance that such systems may exhibit -- diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites) -- and under-estimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.