Barriers to Evidence in AI-Related Cases and the Privatization of Proof

Hannah Ismael; Lucia Zheng; Sarah H. Cen

arxiv: 2605.21816 · v1 · pith:4GBA7ALYnew · submitted 2026-05-20 · 💻 cs.CY

Barriers to Evidence in AI-Related Cases and the Privatization of Proof

Sarah H. Cen , Hannah Ismael , Lucia Zheng This is my paper

Pith reviewed 2026-05-22 07:26 UTC · model grok-4.3

classification 💻 cs.CY

keywords AI evidencelitigation barriersprivatization of proofaccess asymmetriesdiscovery in AI caseslegal accountabilityAI disputes

0 comments

The pith

Asymmetries in access to AI systems create a privatization of proof that blocks evidence in litigation.

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

This paper examines how evidence becomes hard to obtain in AI-related legal disputes even when claims have merit. It identifies seven recurring asymmetries in access to models, data, documentation, logs, expertise, compute, and infrastructure. These patterns amount to a privatization of proof in which private actors control what counts as evidence and can demand justification for access while keeping that justification out of reach. The authors show that different forms of access can substitute for one another to yield equivalent information. They propose a three-part test drawing on proportionality and reasonable alternatives to help courts resolve access requests.

Core claim

Evidence lies at the core of litigation, but it is increasingly difficult to obtain in AI-related disputes because decisive facts are hidden inside proprietary models, platform logs, and protected databases. We identify seven recurring sources of asymmetry that reflect the broader pattern of the privatization of proof: when control over proof falls in the hands of private actors that can demand justification for access while ensuring that justification remains out of reach. Different types of access can be fungible, so that query access or user logs can sometimes substitute for direct model internals. We propose a three-part test that draws on concepts such as proportionality and reasonable

What carries the argument

The privatization of proof, the pattern in which private actors control evidence and can demand justification for access while keeping justification out of reach, carried by the seven identified asymmetries and addressed through a three-part test based on proportionality and reasonable alternatives.

If this is right

Courts can apply the three-part test to decide whether to grant access to AI models or alternative forms of information in ongoing litigation.
Claimants may obtain functionally equivalent evidence through substitute access such as query rights or log inspection when direct model access is refused.
Developers' resistance to disclosure can be evaluated by weighing the value of the requested evidence against the cost of production.
The framework supplies a structured way to handle discovery disputes that arise in future AI-related lawsuits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Adoption of the test could lower the cost of bringing suits against AI systems and thereby increase accountability for harms.
Lawmakers might draw on the seven asymmetries to design mandatory transparency rules for high-stakes AI deployments.
The same logic could apply outside litigation to regulatory audits or public oversight of algorithmic decision systems.

Load-bearing premise

The proposed test assumes that the cause of action can provide a reliable baseline for determining the appropriate level of access to evidence.

What would settle it

A concrete case in which a claimant with a strong cause of action receives no meaningful access even after the three-part test is applied would test whether the test adequately overcomes the asymmetries.

read the original abstract

Evidence lies at the core of litigation, but it is increasingly difficult to obtain in AI-related disputes. Even when a claimant's position has merit, cases are often settled or dismissed because decisive facts are hidden inside proprietary models, platform logs, and protected databases. Grounding our discussion in past and ongoing cases, we investigate how asymmetries in access, resources, and expertise can create significant barriers to evidence in AI-related cases. We show how developers and deployers resist disclosure through various strategies challenging the value of the evidence to the requesting party and the cost of evidence production. From these patterns we identify seven recurring sources of asymmetry -- access to models, data, documentation, logs, expertise, compute, and infrastructure -- that reflect a broader pattern that we call the privatization of proof: when control over proof falls in the hands of private actors that can demand justification for access while ensuring that justification remains out of reach. We further argue that different types of access can be fungible: in the absence of a certain type of access (e.g., to model internals), one may be able to use alternative forms of access (e.g., sufficient compute, query access, and access to user logs) and to obtain a functionally equivalent amount of information. We propose a three-part test that can help resolve AI access disputes in litigation, drawing on concepts such as proportionality and reasonable alternatives. Our test relies on a few observations, including that the cause of action can provide a baseline for access.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper names the privatization of proof in AI cases and proposes a three-part test for evidence access, but the test lacks enough operational detail on mapping causes of action to specific access needs.

read the letter

The main thing to know is that the authors have identified a pattern in AI-related lawsuits where evidence is hard to get because it's controlled by private companies. They label this the privatization of proof and list seven specific asymmetries in access. They do well by grounding the discussion in real past and ongoing cases, showing how developers resist disclosure by questioning the value or cost of the evidence. The point about fungible access types is a solid practical insight – you don't always need the model internals if you can get enough from logs or queries instead. The new contribution is the three-part test for handling these disputes, based on proportionality, reasonable alternatives, and a baseline from the cause of action. This could help courts order disclosure in a more structured way. The soft spot is that the test doesn't provide enough operational criteria for mapping a cause of action to the exact access needed. For example, it's unclear how to decide between model access and alternatives when proving causation or harm in different claims. This could make the framework less reproducible in practice and leave it open to case-by-case variation without clear standards. Overall, the observations seem drawn from external cases rather than circular reasoning, which is good. But more detail on counterexamples or validation of the test would strengthen it. This paper is for legal academics, practitioners in AI law, and policymakers focused on accountability. A reader working on tech litigation or regulation would find the framing and proposal valuable. It deserves a serious referee because the problem is important and the ideas are worth testing in the literature, even with the need for more specifics on the test.

Referee Report

2 major / 2 minor

Summary. The manuscript examines barriers to obtaining evidence in AI-related litigation, grounding its analysis in past and ongoing cases. It identifies seven recurring asymmetries in access to models, data, documentation, logs, expertise, compute, and infrastructure. These patterns are framed as the 'privatization of proof,' in which private actors control key evidence and can require justification for access while keeping such justification difficult to meet. The authors argue that different forms of access can be fungible, allowing alternatives such as query access or user logs to substitute for direct model internals. They propose a three-part test drawing on proportionality, reasonable alternatives, and a baseline derived from the cause of action to help courts resolve access disputes.

Significance. If the three-part test can be made operational, the paper offers a timely and practical contribution to the intersection of AI systems and evidentiary law. The grounding in real cases and the identification of fungible access types provide a useful framework for addressing imbalances that are likely to grow with increased AI deployment. The work highlights structural issues in how proprietary control affects litigation outcomes, which could inform both judicial practice and policy discussions on AI accountability.

major comments (2)

[Proposal of the three-part test] The section proposing the three-part test states that the cause of action can provide a baseline for access, but does not supply operational criteria or concrete mappings showing how a given cause of action (e.g., negligence versus discrimination) translates into specific evidence thresholds for models, logs, or infrastructure. This gap leaves the test vulnerable to inconsistent application and weakens its claim to offer reproducible standards.
[Discussion of fungible access] The fungibility argument (that query access or compute resources can functionally substitute for model internals) is load-bearing for the proposed test yet is supported primarily by general observations rather than detailed case examples demonstrating equivalence in proving elements such as intent or causation. Additional validation through specific litigation outcomes would strengthen this central claim.

minor comments (2)

[Abstract and introduction] The abstract and introduction could more explicitly delimit the set of cases analyzed and note any selection criteria used to identify the seven asymmetries.
[Identification of asymmetries] Notation for the seven asymmetries would benefit from a summary table listing each asymmetry alongside a brief case-derived example.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which identifies key opportunities to strengthen the operational aspects of our proposed three-part test and the supporting discussion of access fungibility. We address each major comment below and have revised the manuscript accordingly to enhance clarity and applicability.

read point-by-point responses

Referee: [Proposal of the three-part test] The section proposing the three-part test states that the cause of action can provide a baseline for access, but does not supply operational criteria or concrete mappings showing how a given cause of action (e.g., negligence versus discrimination) translates into specific evidence thresholds for models, logs, or infrastructure. This gap leaves the test vulnerable to inconsistent application and weakens its claim to offer reproducible standards.

Authors: We agree that additional operational detail would reduce the potential for inconsistent application. In the revised manuscript, we have added a dedicated subsection with concrete mappings for two representative causes of action. For negligence claims, the baseline emphasizes access to model documentation and inference logs to assess foreseeability and causation, subject to proportionality. For discrimination claims, we map to training data summaries and decision logs to evaluate disparate impact. These examples are presented as illustrative rather than exhaustive, and we note that courts retain discretion to adjust based on case-specific facts. revision: yes
Referee: [Discussion of fungible access] The fungibility argument (that query access or compute resources can functionally substitute for model internals) is load-bearing for the proposed test yet is supported primarily by general observations rather than detailed case examples demonstrating equivalence in proving elements such as intent or causation. Additional validation through specific litigation outcomes would strengthen this central claim.

Authors: We acknowledge that the original discussion of fungibility relied more on conceptual patterns than on granular case outcomes. The revised manuscript now includes two specific examples drawn from real or closely analogous litigation. The first involves a product liability matter in which query access to an AI system enabled demonstration of causation without full internal access. The second concerns an employment discrimination case where user logs and interaction data provided functionally equivalent evidence of intent. These additions ground the fungibility claim in concrete outcomes while preserving the core argument that alternatives can suffice under proportionality analysis. revision: yes

Circularity Check

0 steps flagged

No circularity: observational analysis grounded in external cases

full rationale

The paper identifies seven asymmetries and the 'privatization of proof' pattern by examining past and ongoing litigation examples, then proposes a three-part test drawing on established legal concepts such as proportionality and reasonable alternatives. The cause-of-action baseline is presented as one supporting observation rather than a self-derived or fitted input. No equations, parameter fits, or self-citation chains reduce any central claim to its own inputs by construction. The derivation remains self-contained against external case benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The paper rests on observations from litigation cases and standard legal concepts of proportionality and discovery; it introduces the new concept of privatization of proof without independent empirical testing.

axioms (1)

domain assumption The cause of action can provide a baseline for access
Explicitly stated in the abstract as one of the observations the three-part test relies on.

invented entities (1)

privatization of proof no independent evidence
purpose: To name the broader pattern in which private actors control access to evidence in AI disputes
Coined in the paper to capture the seven identified asymmetries

pith-pipeline@v0.9.0 · 5796 in / 1497 out tokens · 46359 ms · 2026-05-22T07:26:26.699513+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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