Towards Safe ML-Based Systems in Presence of Feedback Loops

Abstract

Machine learning (ML) based software is increasingly being deployed in a myriad of socio-technical systems, such as drug monitoring, loan lending, and predictive policing. Although not commonly considered safety-critical, these systems have a potential to cause serious, long-lasting harm to users and the environment due to their close proximity and effect on the society. One type of emerging problem in these systems is unintended side effects from a feedback loop; the decision of ML-based system induces certain changes in the environment, which, in turn, generates observations that are fed back into the system for further decision-making. When this cyclic interaction between the system and the environment repeats over time, its effect may be amplified and ultimately result in an undesirable. In this position paper, we bring attention to the safety risks that are introduced by feedback loops in ML-based systems, and the challenges of identifying and addressing them. In particular, due to their gradual and long-term impact, we argue that feedback loops are difficult to detect and diagnose using existing techniques in software engineering. We propose a set of research problems in modeling, analyzing, and testing ML-based systems to identify, monitor, and mitigate the effects of an undesirable feedback loop.