# Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act

arXiv: https://arxiv.org/abs/2506.01931 · HTML: https://mlsanity.com/paper/2506.01931/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 119 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [Red-Teaming for Generative AI: Silver Bullet or Security Theater?](https://mlsanity.com/paper/2401.15897/) (2024)
- [Fairwashing: the risk of rationalization](https://mlsanity.com/paper/1901.09749/) (2019)
- [The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI](https://mlsanity.com/paper/2310.16787/) (2023)
- [Prompt Tuning for Generative Multimodal Pretrained Models](https://mlsanity.com/paper/2208.02532/) (2022)
- [The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market](https://mlsanity.com/paper/2208.12645/) (2022)
- [On the Limitations of Compute Thresholds as a Governance Strategy](https://mlsanity.com/paper/2407.05694/) (2024)
- [Risks and Opportunities of Open-Source Generative AI](https://mlsanity.com/paper/2405.08597/) (2024)
- [The Privatization of AI Research(-ers): Causes and Potential Consequences -- From university-industry interaction to public research brain-drain?](https://mlsanity.com/paper/2102.01648/) (2021)
- [The Value of AI-Generated Metadata for UGC Platforms: Evidence from a Large-scale Field Experiment](https://mlsanity.com/paper/2412.18337/) (2024)
- [You Still See Me: How Data Protection Supports the Architecture of AI Surveillance](https://mlsanity.com/paper/2402.06609/) (2024)

## Built on by (most cited first, 6 of 6)

- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [Distinguishing Task-Specific and General-Purpose AI in Regulation](https://mlsanity.com/paper/2506.17347/) (2025)
- [Internal Deployment Gaps in AI Regulation](https://mlsanity.com/paper/2601.08005/) (2026)
- [Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration](https://mlsanity.com/paper/2604.21554/) (2026)
- [Now You See Me: Designing Responsible AI Dashboards for Early-Stage Health Innovation](https://mlsanity.com/paper/2602.23378/) (2026)
- [Quality Assessment of Public Summary of Training Content for GPAI models required by AI Act Article 53(1)(d)](https://mlsanity.com/paper/2603.13270/) (2026)

## Abstract (arXiv)

The shape of AI regulation is beginning to emerge, most prominently through the EU AI Act (the "AIA"). By 2027, the AIA will be in full effect, and firms are starting to adjust their behavior in light of this new law. In this paper, we present a framework and taxonomy for reasoning about "avoision" -- conduct that walks the line between legal avoidance and evasion -- that firms might engage in so as to minimize the regulatory burden the AIA poses. We organize these avoision strategies around three "tiers" of increasing AIA exposure that regulated entities face depending on: whether their activities are (1) within scope of the AIA, (2) exempted from provisions of the AIA, or are (3) placed in a category with higher regulatory scrutiny. In each of these tiers and for each strategy, we specify the organizational and technological forms through which avoision may manifest. Our goal is to provide an adversarial framework for "red teaming" the AIA and AI regulation on the horizon.