Bias Amplification Dynamics in Generative Policy Expression Models

Abstract

Generative policy expression models are increasingly embedded in enterprise workflows to guide
decision routing, compliance enforcement, and advisory recommendations. However, their integration
introduces a risk of bias amplification, where model-generated outputs gradually influence operational
norms and shift organizational behavior patterns over time. This study examines how generative
models interact with workflow sequencing, user interpretation, and system state propagation,
demonstrating that even minimal representational skew can accumulate into structural bias within
enterprise processes. The results show that generative advisory systems tend to reinforce historically
dominant procedural pathways, compress the diversity of available decision alternatives, and shape
user expectations toward narrower interpretations of policy logic. These effects are often subtle,
distributed, and long-term, making bias difficult to detect without systemic analysis. The findings
underscore the need for governance-aware model deployment strategies and corrective oversight
mechanisms to prevent the institutionalization of unintended bias dynamics in enterprise
environments.