Enterprise AI Policy Analysis
Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act
Authored by Rui-Jie Yew, Bill Marino, and Suresh Venkatasubramanian
This paper introduces a critical framework for understanding "avoision" behaviors firms might use to minimize regulatory burdens under the EU AI Act, proposing a taxonomy across three tiers of increasing AIA exposure and highlighting potential technological and organizational manifestations.
Executive Impact Summary
Understanding the EU AI Act's nuances is critical for risk mitigation and strategic compliance. Our analysis distills key insights for enterprise decision-makers.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Targeting the AIA's Scope
Firms may aim to avoid the AIA altogether by structuring their AI systems, models, or outputs to fall outside the law's explicit scope. This tier focuses on circumventing the AIA's definitions of AI systems and distancing AI elements from the EU market.
Targeting the AIA's Exemptions
This tier explores strategies where firms position themselves within the AIA's exemptions, such as for scientific research or open-source AI. The goal is to benefit from reduced regulatory burden while potentially undermining the intent of the exemptions regarding openness and competition.
Targeting Consequential Categories
Here, firms seek to reduce regulatory burden by strategically influencing how their AI systems, models, or even their operator roles are categorized under the AIA. This involves maneuvers like positioning high-risk AI as GPAI models, avoiding systemic risk classification, or shifting provider responsibilities.
Avoision Methodology Flow
Strategy | Implications for Firms | Potential Risks/Intent Subversion |
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Adding Human Veneers |
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Adding Rule-based/Traditional Software Veneers ("Reverse AI-washing") |
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Case Study: GPAI for Education - Regulatory Arbitrage
The AIA classifies "education and vocational training" as a high-risk domain. This means AI systems explicitly designed for this sector face stringent requirements.
However, firms can strategically productionize an AI system as a General-Purpose AI (GPAI) model, claiming one of its many uses is in education, rather than it being an AI system intended for the education domain. This rhetorical shift can result in the same market access and impact in the education sector but with significantly fewer regulatory requirements, thereby undermining the AIA's risk-based approach.
AIA Avoision Taxonomy Tiers
AI Policy Compliance ROI Calculator
Estimate the potential efficiency gains and cost savings from proactive AI policy compliance and strategic avoision mitigation.
Your AI Policy Red Teaming Roadmap
A structured approach to identify potential avoision strategies and bolster your AI regulation compliance.
Assess Current AI Landscape & AIA Applicability
Understand if your AI systems are in scope of the AIA, identify potential exemptions, and determine initial categorization (risk, type, operator role).
Identify Avoision Opportunities & Risks
Map current and planned AI practices against the avoision taxonomy to identify potential legal-letter-compliant but intent-defying strategies. Assess associated risks.
Develop & Implement Compliance/Avoision Strategy
Integrate appropriate "veneers", adjust AI deployment locations, leverage exemptions, or refine categorization to minimize regulatory burden while mitigating avoision-related risks.
Monitor & Adapt to Regulatory Evolution
Continuously review and adjust strategies as AIA interpretations evolve, new technical standards emerge, and enforcement efforts refine.
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