The AI Discovery Crisis
Why traditional discovery tools fail for AI litigation, and what courts are demanding instead.
+427%
AI Litigation Growth
Federal AI cases since 2020 (Stanford AI Index)
10,000+
Companies Using Workday
AI hiring tools screening millions of applicants (Mobley v. Workday)
92%
Discovery Motion Rate
AI cases involve contested discovery motions (Lex Machina)
Discovery Motion Rates by Practice Area
AI Litigation
Patent
Commercial
Employment
Source: Lex Machina
Key Discovery Battles in AI Cases
Click through landmark cases that defined AI discovery standards
Jan 27, 2025
Federal Judge Orders OpenAI to Produce Training Dataset
The Discovery Problem
- •Scale: 1+ trillion tokens across multiple datasets
- •Technical Complexity: No documentation mapping specific data to model behavior
- •Trade Secret Claims: Blanket assertions protecting all training data
What the Court Ordered
- ✓Dataset Index: Cryptographic hashes of all training data
- ✓Secure Inspection: Air-gapped room with expert review only
- ✓Proportionality Analysis: Manual review rejected as impossible
💡
Impact: This ruling established that technical solutions (hash-based search) must replace manual review for AI discovery.
Why AI Discovery Is So Brutal
Plaintiffs now demand technical evidence that most companies can't produce cleanly:
What Plaintiffs Ask For:
- 📊Rejection rates by protected class (race, age, disability)
- 🔧All model versions with change logs and testing results
- 📈Feature correlations (employment gaps, graduation year impact)
- ⚙️Customer-level tuning configs (how each company customized the AI)
Why Companies Can't Produce It:
- ❌No logging: Never tracked model versioning properly
- ❌No audit trails: Can't reconstruct who changed what when
- ❌Data silos: Customer configs in different systems than model code
- ❌No documentation: Never analyzed feature correlations for bias
💸
The Result: Months of forensic work, $500+/hr experts, and still incomplete responses that trigger motions to compel.
Why AI Discovery Is Different
📊
Scale Beyond Human Review
Training datasets contain billions of items. Manual review would take centuries.
Tremblay v. OpenAI: "Plaintiffs abandoned search after estimating 6+ hours per copyrighted work."
🔒
Trade Secret Tension
Courts balance legitimate IP protection against overbroad confidentiality claims.
Multiple rulings: "Dataset indices ordered, but model weights protected."
⚖️
Jurisdictional Patchwork
Different standards in US, EU, Germany, UK create compliance nightmares.
GEMA v. OpenAI: German "memorization" standard differs from US "substantial similarity."
From Discovery Crisis to Motion-Ready Strategy
Courts are demanding technical solutions. We provide the bridge between legal strategy and technical execution.
The Old Way
- ✗Manual document review (240+ hours)
- ✗Manual discovery on SaaS systems (impossible without vendor cooperation)
- ✗Motion drafting from scratch (2-3 weeks)
With Rule26 AI
- ✓Hash-based search (2 hours)
- ✓AI-powered analysis & gap detection
- ✓Motion-ready packages (4 clicks)
Mobley v. Workday, Inc.
AI Hiring Discrimination Class Action • Filed 2023
Ongoing
Real Case
Case Facts
- •Allegation: Workday's AI hiring tools discriminate against applicants based on protected characteristics
- •Scale: Used by 10,000+ companies to screen millions of applicants
- •Discovery Issues: Black box algorithms, training data, trade secret claims, multi-tenant SaaS
Source: Mobley v. Workday, Inc., U.S. District Court
Your role
Evidence sources connected
Relativity
GitHub
Slack
Jira
AWS S3
MLflow
Confluence
Databricks
Rule26 AI searches across 12+ systems to map AI evidence to legal elements
Selected jurisdiction will tailor citations, discovery rules, and motion strategy.
🔔 Recent AI Discovery Rulings
Live
• Jan 27, 2025: Federal judge orders OpenAI to produce training dataset
• Mar 15, 2025: 9th Circuit affirms AI interaction logs discoverable
• Apr 2, 2025: German court adopts "memorization" standard
Key artifacts
Mapped from connected sources. Rule26 AI links technical artifacts to legal elements (e.g., feature_weights.csv → disparate impact).
Model weights (v2.3)
Training dataset manifest
Inference logs (Q3 2023)
Bias audit report
Feature importance docs
HR integration spec
Role: Plaintiff's Counsel
Legal Elements
Proportionality Analysis
Based on 500,000 training samples. Courts reject manual review as disproportional.
Upload Copyrighted Work for Hash Comparison
Copyrighted Work
SHA-256 Hash
Match in Dataset Index
Trade Secret Balancing
Courts have ordered: Dataset indices, training methodology
Courts have protected: Model weights, source code, proprietary algorithms
Adversarial Analysis
Opposing Argument
Our Potential Counter
Suggested Discovery
Training Data Provenance Chain
Follow the evidence from training data to legal impact. Click any stage for details.
Training Data Sources
Data Sources Identified
- Common Crawl (web scrape)
- Proprietary HR datasets
- GitHub repositories
Hash Analysis Results
Copyrighted works tested:1,247
SHA-256 matches found:38
Ready for motion Exhibit A
Key Evidence at This Stage
Data License Agreements
Hash Match Report
Provenance Gaps
Model Training
Model v1.1 trained on aggregated datasets. Bias audit flagged disparate impact. Key artifacts: decision_engine_v3.2.pt, bias_audit_report_2023.pdf.
Key Evidence
- Training logs and hyperparameters
- Bias audit report (Q3 2023)
- Model card and documentation
Deployment
Model v1.1 deployed to production Oct 2023. No pre-deployment legal review documented. Deployment log and release notes available.
Key Evidence
- Deployment log (Apr 2023)
- Release notes and runbook
- Monitoring and rollback procedures
Legal Impact
Bias audit flagged; notice chain established. Undocumented inputs and proxy variables may encode protected characteristics. Discovery targets: model weights, architecture, training methodology.
Key Evidence
- Bias audit report and cover letter
- Preprocessing pipeline and feature docs
- Legal hold and preservation notices
Discovery Gaps That Trigger Motions
📊
Missing: Rejection Rate Logs
System wasn't designed to track by protected class
🔧
Missing: Version Control
No audit trail of model changes
📈
Missing: Feature Analysis
Never calculated correlation statistics
⚙️
Missing: Config Documentation
Customer settings spread across systems
Memorization Analysis
Testing Methodology
- • 5,000+ prompt variations per copyrighted work
- • Character-level string matching
- • N-gram analysis for partial reproduction
- • Statistical significance calculations
German Court Requirements
- • Expert must explain methodology
- • Must test representative sample
- • Results must be reproducible
- • Threshold: "Substantial verbatim reproduction"
Sample Regurgitation Results
| Copyrighted Work | Verbatim Match | GEMA Standard | US Standard |
|---|---|---|---|
| Song A (GEMA) | 94% | Violation | No violation |
| Song B (GEMA) | 45% | No violation | No violation |
AI Interaction Discovery (Feature #6)
Internal AI Chat Logs
Relevant to litigation hold and work product analysis
Legal significance: Some courts treat AI prompts as work product, but outputs used in filings may waive protection
Discovery request ready: "All AI-generated content used in case strategy"
📝 Tip: Add AI interaction preservation to litigation hold notices
Secure Inspection Protocols (Feature #8)
Air-Gapped Review Room
Courts have approved inspection in secure rooms with no internet access.
Chain-of-Custody Logs
Automated documentation for court oversight and audit trails.
Court-Precedent Templates
Security protocols already accepted by federal courts.
Selected Artifact: biometric_model_logs.json
Supports
Does NOT Prove
Opposing Counsel Spin
Next Questions
Why This Is Brutal Discovery
- Companies don't log rejection rates by protected class
- Model versioning is ad-hoc, not systematic
- Feature correlation analysis requires data science expertise
- Customer configs live in different databases
What Rule26 AI Does
- Reconstructs rejection rates from logs + HR data
- Maps feature importance from model artifacts
- Documents gaps for proportionality arguments
- Generates expert-ready statistical analysis
Jurisdiction-Specific Analysis
In Germany: This evidence would trigger GEMA v. OpenAI "memorization" standard.
Expert Declaration Guidance
- Emphasize SHA-256 collision resistance in declaration
- Cite ACM paper on dataset fingerprinting (2023)
- Explain why manual review is disproportional
Analysis Confidence:
Medium
Motion-Ready Output Zone
Your complete motion package is ready for filing
Motion Package
Motion to Compel Training Data
Exhibits
Proposed Protective Order
Expert Template
Security Protocol
Air-gapped
GEMA Memorization Report
Turning Brutal Discovery Into Strategy
💸
The Old Way
- 6+ months forensic investigation
- $500+/hr data science experts
- Incomplete, defenseless responses
- Multiple motions to compel
⚡
With Rule26 AI
- Days, not months of analysis
- Automated statistical reports
- Court-ready documentation
- Proportionality arguments built-in
Rule26ai Architecture
☁️
SaaS Portal
What you see and use
- •Beautiful interface (this demo!)
- •Motion template library
- •Case law database
- •Jurisdiction rules engine
🔒
Local AI Agent
Runs in your firm's environment
- •Sensitive data never leaves your control
- •Direct integration with your systems
- •AI processing on your infrastructure
- •Full privilege/work product protection
Only metadata and analysis results flow between systems
Raw case data stays securely within your firm's environment
From 6+ Hours to Motion-Ready in 4 Clicks
What took Tremblay v. OpenAI 240+ hours of manual review is now automated with hash-based search and proportionality analysis.