Understanding Feedback Loops in Machine Learning Systems

Abstract

Machine learning systems increasingly operate in dynamic environments where models both influence and are influenced by their surroundings, creating feedback loops that fundamentally alter system behavior over time. These loops manifest across various domains including advertising, logistics, real estate, and content recommendation, presenting both opportunities and challenges for responsible AI deployment. This article explores the nature of feedback loops, distinguishing between beneficial loops that incorporate unbiased external data and degenerative loops that amplify existing biases. It examines why detecting these cycles matters, presents methodologies for identification, and offers domain-specific mitigation strategies for different system types. The comprehensive framework provided encompasses requirements analysis, observability, unbiased data acquisition, and continuous monitoring practices to manage the effect of feedback loop appropriately throughout the machine learning lifecycle.