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REVIEW 2 major objections 5 minor 119 references

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

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that economically rational firms will meet the EU AI Act with 'avoision'—legally compliant conduct that undermines the law's intent—and organizes the foreseeable maneuvers into a three-tier taxonomy of scope, exemptions…

desk verdict A genuinely useful taxonomy of AIA loopholes, but the 'technically lawful' premise skips the EU abuse-of-rights doctrine that could make several listed strategies plain evasion. read the letter →

arxiv 2506.01931 v1 pith:NODFOH2C submitted 2025-06-02 cs.CY cs.AI

Rui-Jie Yew , Bill Marino , Suresh Venkatasubramanian This is my paper
classification cs.CYcs.AI
keywords avoisionEUAIActregulatoryarbitragegovernanceredteamingopen-sourceexemptionsrisk-basedregulationcomplianceevasion
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the EU AI Act will be met by 'avoision'—conduct that stays inside the letter of the law while frustrating the Act's intent—and that this conduct will come in three organized tiers. Firms will first try to step entirely out of scope by wrapping AI in human reviewers or rules-based software, or by arranging servers and human decision-makers so that AI outputs are arguably not 'used in the Union.' When that fails, they will hide inside the research and open-source exemptions while keeping findings, talent, and competition effects closed. And where neither works, they will steer into less burdensome categories, presenting high-risk education tools as general-purpose models, sandbagging benchmarks, gaming compute thresholds, and shifting provider obligations onto deployers. The payoff of the taxonomy is practical: it gives enforcers and standards bodies a checklist of specifically named maneuvers to anticipate before the Act fully applies in 2027.

What carries the argument

The carrying object is the taxonomy itself, built on three 'tiers' of AIA exposure: scope, exemptions, and consequential categories (type of AI, risk category, operator role). Each tier names avoisive strategies with their organizational and technological embodiments—human veneers, reverse AI-washing, extraterritorial wrappers, research-without-openness, open-washing, GPAI positioning, benchmark shopping, sandbagging, FLOP-gaming, and provider/deployer finger-pointing. The taxonomy's analytical work is to sort these maneuvers by which part of the Act they target and to show that each is economically rational: it lowers compliance cost while keeping the firm inside the letter of the law, which is exactly why the authors treat it as a red-teaming tool for enforcement.

What would settle it

The taxonomy would be falsified in its scope tier if EU courts or the Commission issue guidance holding that a human reviewer or a rules-based wrapper does not make a system non-'machine-based' or non-autonomous; that would eliminate the legal basis for the two signature Tier 1 strategies. A second check is empirical: once Article 51 is enforced, if no developer is found to have used decentralized training or distillation to stay under the $10^{25}$ FLOP threshold, the FLOP-gaming hypothesis predicts behavior that did not materialize.

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Extended reading notes

Core claim

On its own terms, the paper establishes a three-tier map of avoision against the AIA, where each tier corresponds to a point of regulatory exposure: scope, exemptions, and consequential categories. For each tier it identifies concrete technological and organizational maneuvers—human veneers and 'reverse AI-washing' to slip the definition of AI system, extraterritorial wrappers to slip the EU-market connection, research-without-openness and 'open-washing' to exploit carve-outs, and GPAI positioning, benchmark shopping, sandbagging, FLOP-gaming, and provider/deployer finger-pointing to land in a lighter regulatory class. The authors argue that these maneuvers are economically rational because compliance can cost up to €400,000 per system and add as much as 17% to development costs, while penalties punish only unlawful evasion. They conclude that the same behaviors undermine the Act's stated purposes of protecting health, safety, and fundamental rights, and that standards-setting and independent third-party conformity assessment are the main levers for closing the gaps.

Load-bearing premise

The load-bearing premise is that the AIA's key terms—'machine-based', 'autonomy', 'outputs used in the Union', 'substantial modification', and 'free and open-source'—will remain ambiguous long enough for firms to exploit them; if courts adopt the anti-circumvention reading of Recital 22 as a general principle, the whole scope tier of the taxonomy loses its legal foothold.

Editorial extensions

If this is right

  • Closing the scope definitions—for instance, by clarifying that human or rules-based wrappers leave a system inside the Act—would neutralize the first tier of avoision before it spreads.
  • The standards-setting process becomes a decisive site of enforcement, because it will decide what counts as genuinely open source, how FLOPs are counted in decentralized training, and which benchmarks justify systemic-risk classification.
  • Independent third-party conformity assessment is the paper's main structural fix, since much category-tier avoision relies on the Act's self-assessment and self-grading provisions.
  • Avoision pressure can be expected to migrate down the tiers over time: as courts and standards close scope arguments, rational firms will lean harder on exemptions and category arbitrage.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • We infer that the three-tier structure transfers to other risk-based AI statutes, including pending or future laws that define their objects by autonomy, machine-based operation, or compute thresholds; those laws inherit a similar evasion space.
  • We infer a testable shift: after the AIA is enforced and scope guidance appears, observed avoision should concentrate in Tier 2 and Tier 3 rather than Tier 1, so enforcement resources can be timed accordingly.
  • We infer that the taxonomy doubles as a measurement scheme: fine-tuning versus prompt-tuning choices, benchmark selection, the accessibility of open-source releases, and the location of training compute are all observable proxies for the hypothesized strategies.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper proposes a three-tier taxonomy of 'avoision' — conduct that is formally lawful but defeats a law's intent — that firms might use to reduce the regulatory burden of the EU AI Act. Tier 1 targets the Act's scope by circumventing the definitions of AI systems and by distancing systems from the EU market; Tier 2 targets the research and open-source exemptions; Tier 3 targets 'consequential categories' by arbitraging between AI system/GPAI model classifications, risk categories, and operator roles. Each strategy is supported by contemporary examples or analogies from GMO, patent, tax, emissions, and privacy law, and the paper closes with policy recommendations on standards-setting and enforcement.

Significance. If the taxonomy holds, it makes the avoision space under the AIA legible in a structured, actionable way and gives regulators and standard-setting bodies a concrete checklist. The paper is carefully anchored in the AIA text and prior legal scholarship, explicitly hedged ('could', 'might'), and ships a substantial bibliography and illustrative case studies. Its main weakness is that the legal-predicate half of the central claim — that the enumerated strategies are 'technically lawful' rather than unlawful evasion — is asserted rather than established, because the manuscript does not engage the EU abuse-of-rights doctrine. That gap is fixable and does not invalidate the taxonomy as a red-teaming exercise, but it must be addressed before the 'technically lawful' claim can stand.

major comments (2)
  1. [Section 3; Section 4.1.1–4.1.2] Criterion (1) of the methodology requires that included behaviors are not unlawful evasion of the AIA but 'comply with the letter of the law.' The manuscript never engages the established EU abuse-of-rights doctrine (e.g., Case C-255/02 Halifax; Case C-196/04 Cadbury Schweppes), under which the Court of Justice may disregard formalistic compliance that defeats a regulation's purpose. Because Recital 22 explicitly states that the AIA's extraterritorial scope is intended 'to prevent the circumvention of this Regulation,' the human-veneer and non-AI-wrapper strategies in Sections 4.1.1 and 4.1.2 could plausibly be reclassified as unlawful evasion, which would place them outside the paper's own criterion (1). The authors should either analyze why abuse-of-rights does not apply to each listed strategy or explicitly weaken the predicate to 'not clearly unlawful under a formal textual reading' and revise the Abstract and Section 1 accordingly. The Section 5 statement that the AIA has yet to be interpreted in courts understates this issue, because the abuse-of-rights doctrine predates the AIA and does not depend on AIA-specific rulings.
  2. [Section 4.3.1] The GPAI-for-education case study appears to conflate model-level and system-level obligations. A GPAI model that is subsequently incorporated into a system whose intended purpose is education makes the provider of that system subject to the high-risk provisions of Article 6 and Annex III (and Article 25 for substantial modifications), regardless of how the release is framed. The paper should specify whether the same firm remains the provider of the downstream education system and, if not, why the firm nonetheless obtains 'the same level of access and potentially the same level of impact' in the education sector. As written, the case study overstates the regulatory advantage of 'productionizing' an education AI system as a GPAI model.
minor comments (5)
  1. [Section 1] The Introduction misspells the Act as 'Artifical Intelligence Act'; it should read 'Artificial Intelligence Act.'
  2. [Section 4.1.1] In the last paragraph of Section 4.1.1, 'defition' should be 'definition,' and in Section 4.3.1 'ikely' should be 'likely.'
  3. [Section 4.3] The opening of Section 4.3 contains 'avoison' for 'avoision,' and Section 4.1.2 contains 'cross-jurisidictional' for 'cross-jurisdictional.'
  4. [Section 5] The three headings 'A voision Targeting...' contain an erroneous space and should read 'Avoision Targeting...'.
  5. [Section 2.1.3] The euro amounts appear with a corrupted symbol: 'e400,000' and 'e35,000,000' should render as '€400,000' and '€35,000,000.'

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the taxonomy is constructed from the AIA text, external legal scholarship, and historical analogies; the sole self-citation [114] is peripheral and non-load-bearing.

full rationale

This is a qualitative taxonomy paper with no formal model or fitted parameters, so there are no equations whose outputs collapse into inputs. The central claim that economically rational firms may engage in avoision against the EU AI Act is not defined in terms of the paper's own results: the three tiers are organizational categories derived from the AIA's scope, exemptions, and risk/operator classifications, and each strategy is justified by the AIA text, contemporary empirical observations, or historical analogies from other regulatory domains. The only self-citation is [114] in Section 4.3.3, used alongside [105] to support the peripheral observation that privacy-preserving client-side processing may help companies avoid 'controller' categorization under the GDPR; that analogical example is not the load-bearing premise for the AIA taxonomy, and the central argument would not change if the citation were removed. Section 5's own limitation that 'The AIA has yet to be interpreted in courts' is acknowledged in the paper, and the skeptic's abuse-of-rights objection is a substantive legal-robustness question about whether the listed strategies are genuinely 'technically lawful' under future judicial interpretation; that concern is about external correctness, not circularity, because the paper does not assume the lawfulness conclusion by construction. No definitional equivalence, fitted-input-renamed-as-prediction, or author-imported uniqueness theorem appears in the derivation chain.

Assumptions & free parameters 0 free parameters · 4 assumptions · 2 invented entities

The paper is conceptual, so the ledger records its interpretive assumptions rather than numeric parameters. No free parameters are fit to data.

assumptions (4)
  • domain assumption The AIA provisions referenced in the paper support the interpretations given to them.
    The legal analysis rests on the accuracy and completeness of the authors' readings of Articles 2, 3, 6, 51, 53 and related recitals; I cannot independently verify these legal interpretations.
  • domain assumption Firms act as economically rational cost-minimizers searching for loopholes.
    Inclusion criterion (3) in Section 3 presumes rational avoidance behavior without evidence that AI firms will behave this way.
  • ad hoc to paper Analogies from GMO, patent, tax, emissions, and privacy law are probative for AIA behavior.
    Table 1 and Section 3 use historical analogies as support, but the paper does not systematically test the analogy against structural differences in enforcement and market structure.
  • domain assumption The stated intent of the AIA in its recitals is a coherent benchmark for detecting avoision.
    The framework relies on a purpose-based reading of the AIA (Section 2.1.1) that courts may not adopt.
invented entities (2)
  • Three-tier avoision taxonomy (scope, exemptions, consequential categories)
    purpose: Organizes all considered avoision strategies by regulatory exposure.
    A conceptual construct; it has no falsifiable handle outside this paper, though its component strategies could be observed in enforcement actions.
  • 'Reverse AI-washing' strategy category
    purpose: Describes adding traditional software or rule-based veneers to position AI outside the legal definition of an AI system.
    Coined nomenclature for a hypothesized behavior; no independent evidence of the behavior occurring under the AIA is provided.

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Cite this review

Pith. "Pith review of Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act." pith.science (2026). https://pith.science/paper/NODFOH2C

@misc{pith2026250601931,
  author       = {Pith},
  title        = {Pith review of: Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NODFOH2C}},
  note         = {Machine review of arXiv:2506.01931}
}
read the original abstract

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.

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