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

2022-11-15

J. Am. Medical Informatics Assoc.

AI Summary

This paper identifies and systematically analyzes two critical ethical challenges arising from adaptive machine learning systems in clinical practice: *diachronic drift*—temporal shifts in model behavior over time—and *synchronic variation*—inter-institutional disparities in algorithmic behavior across deployments—both threatening patient safety, validity of informed consent, and healthcare equity. Methodologically, the study integrates clinical scenario modeling with cross-institutional comparative analysis of algorithmic behavior, grounded in a medical AI governance framework. It is the first to formally define and foreground synchronic variation as an ethically distinct concern for quality assurance, regulatory compliance, and algorithmic fairness. The contribution fills a key gap in medical AI ethics research by proposing two foundational ethical principles—*diachronic stability* and *synchronic consistency*—and delivering an actionable ethical risk assessment guide tailored for developers, regulators, and clinicians.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Abstract 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 article, 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 ML 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 2 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 underestimated 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.

Problem

• Ethical challenges in adaptive ML systems in medicine
• Diachronic and synchronic variation impacts patient care
• Neglected ethical issues in continual learning ML systems

Innovation

• Adaptive ML systems for continual learning
• Diachronic and synchronic variation analysis
• Ethical trade-offs in medical ML design