From Linear Risk to Emergent Harm: Complexity as the Missing Core of AI Governance

TL;DR

This paper critiques risk-based AI regulation for ignoring complex system dynamics, proposing a complexity-informed governance framework that emphasizes adaptive, systemic approaches over static controls to better manage emergent harms.

Contribution

It introduces a novel complexity-based framework for AI governance that accounts for emergent harms and advocates for adaptive, systemic regulation strategies.

Findings

Risk-based regulation often fails due to assumptions of linear causality.

Emergent harms are amplified through feedback loops in socio-technical systems.

A complexity approach enables more robust and adaptive AI governance.

Abstract

Risk-based AI regulation has become the dominant paradigm in AI governance, promising proportional controls aligned with anticipated harms. This paper argues that such frameworks often fail for structural reasons: they implicitly assume linear causality, stable system boundaries, and largely predictable responses to regulation. In practice, AI operates within complex adaptive socio-technical systems in which harm is frequently emergent, delayed, redistributed, and amplified through feedback loops and strategic adaptation by system actors. As a result, compliance can increase while harm is displaced or concealed rather than eliminated. We propose a complexity-based framework for AI governance that treats regulation as intervention rather than control, prioritises dynamic system mapping over static classifications, and integrates causal reasoning and simulation for policy design under uncertainty. The aim is not to eliminate uncertainty, but to enable robust system stewardship through monitoring, learning, and iterative revision of governance interventions.