Why Are AI Disputes Becoming More Common?
As AI adoption accelerates, disputes involving AI technologies are proliferating across multiple fronts including copyright infringement claims over training data, patent disputes over AI innovations, breach of contract claims regarding AI system performance, trade secret misappropriation of algorithms and models, and regulatory enforcement actions for AI compliance failures.
High-profile litigation involving companies like OpenAI, Stability AI, Microsoft, and others demonstrates emerging legal battlegrounds around generative AI. Artists and authors have filed class actions alleging copyright infringement through training on their works. Companies face patent litigation over machine learning implementations. Customers sue vendors when AI systems fail to perform as promised.
Understanding how to navigate AI-related disputes—whether as plaintiff or defendant—is critical for companies developing, deploying, or relying on AI technologies. Effective dispute management requires anticipating litigation risks, implementing preservation and discovery protocols, choosing optimal forums and strategies, and considering settlement versus litigation trade-offs.
Common Types of AI Disputes
Copyright Infringement Claims
Copyright litigation involving AI typically involves allegations that training data includes copyrighted works without authorization, that AI outputs reproduce substantial portions of copyrighted works, or that AI systems enable copyright infringement by users.
Plaintiffs include content creators claiming training constitutes infringement, publishers alleging unauthorized use of corpora, and rights holders asserting derivative work violations.
Defenses often focus on fair use arguments, lack of substantial similarity between training data and outputs, and absence of copying or access.
Patent Infringement Disputes
Patent litigation in AI involves claims that machine learning implementations infringe method or system patents, that AI-generated inventions use patented processes, or that AI training or inference infringes claims.
AI companies both assert patents offensively and defend against infringement allegations from patent holders and non-practicing entities.
Trade Secret Misappropriation
Trade secret claims arise when departing employees allegedly take AI algorithms or models to competitors, when companies claim competitors improperly acquired proprietary techniques, or when partnerships dissolve with disputes over who owns developed IP.
Trade secret litigation often involves preliminary injunctions to prevent ongoing use and damage calculations for unjust enrichment.
Contract Disputes
Contract litigation involves AI systems failing to meet performance warranties, disputes over IP ownership in development agreements, breaches of licensing terms or usage restrictions, and failures to deliver promised capabilities.
Customers increasingly sue AI vendors when systems don’t perform as marketed or contracted.
Regulatory and Compliance Actions
Regulatory disputes involve FTC enforcement for unfair or deceptive AI practices, EEOC claims alleging discriminatory AI employment tools, state attorney general actions under consumer protection laws, and privacy regulator enforcement for data handling violations.
Pre-Litigation Considerations
Evaluating Dispute Merits
Before initiating litigation or responding to demands, assess the strength of legal claims or defenses, availability and persuasiveness of evidence, potential damages or remedies, and costs versus benefits of litigation.
Not every dispute justifies litigation—sometimes settlement, licensing, or walking away is optimal.
Preservation Obligations
When litigation is reasonably anticipated, implement litigation holds preserving relevant evidence including source code and model files, training data and datasets, communications about disputed matters, and customer agreements and technical documentation.
Failure to preserve evidence can result in sanctions including adverse inference instructions.
Insurance Coverage
Review insurance policies for potential coverage including cyber liability for data breaches or privacy claims, errors and omissions for professional service failures, directors and officers liability for regulatory actions, and IP insurance for patent or copyright litigation.
Notify carriers promptly of potential claims to preserve coverage.
Choosing Forums and Procedures
Federal vs. State Court
Patent and copyright claims must be brought in federal court due to exclusive jurisdiction. Trade secret and contract claims can be brought in state or federal court depending on diversity jurisdiction.
Federal court may offer more expertise with complex IP issues, while state courts may be faster or more favorable depending on jurisdiction.
Arbitration vs. Litigation
Many AI contracts include arbitration provisions requiring dispute resolution through private arbitration rather than court litigation. Arbitration offers confidentiality, potentially faster resolution, and specialized arbitrators, but limits discovery and appeal rights.
Consider whether arbitration clauses are enforceable and strategically beneficial.
Venue Selection
For litigation, venue choice significantly impacts outcomes. Patent plaintiffs often favor certain districts like the Western District of Texas or Eastern District of Texas. Technology defendants may prefer California or Delaware.
Contracts may include forum selection clauses requiring disputes in specific courts.
Discovery Challenges in AI Litigation
Source Code and Model Production
AI litigation frequently involves discovery of proprietary source code, trained model weights and parameters, training scripts and configurations, and data preprocessing pipelines.
Producing this information risks disclosing trade secrets to adversaries. Courts typically implement protective orders with attorney-eyes-only provisions, source code escrows, and limitations on copying or retention.
Training Data Discovery
Determining what data was used for training can be critical in copyright or privacy disputes. However, training datasets may be massive (billions of data points), include third-party proprietary data, or be difficult to reconstruct after training.
Parties often negotiate sampling approaches rather than producing entire datasets.
Technical Expert Involvement
AI litigation requires technical experts who understand machine learning architectures, training methodologies, and output generation. Experts analyze whether infringement occurred, evaluate performance claims, or assess damages.
Early expert involvement helps develop technical strategies.
Privilege and Work Product Considerations
Communications with counsel about AI disputes are privileged. Technical analyses prepared in anticipation of litigation constitute work product.
However, routine business documents and technical development materials aren’t privileged merely because litigation exists.
Defending Copyright Infringement Claims
Fair Use Defense
Fair use is the primary defense in copyright claims involving AI training. Fair use analysis considers purpose and character of use (transformative uses favor fair use), nature of copyrighted work, amount and substantiality of portion used, and effect on market for original.
AI companies argue training is transformative, uses small portions of individual works, and doesn’t substitute for originals.
Challenging Substantial Similarity
Copyright requires showing copying and substantial similarity. If AI outputs don’t closely resemble specific training inputs, infringement may not exist even if copyrighted works were in training data.
DMCA Safe Harbor
Platforms hosting user-generated AI content may invoke Digital Millennium Copyright Act safe harbor provisions if they implement notice and takedown procedures, don’t have actual knowledge of infringement, and don’t financially benefit from infringement while having control.
Patent Litigation Strategies
Invalidity Defenses
Defendants challenge patent validity based on lack of novelty due to prior art, obviousness over combinations of references, or failure to meet patent eligibility standards under Alice Corp. v. CLS Bank.
AI patents face eligibility challenges if claims are directed to abstract ideas without inventive technical implementations.
Non-Infringement Arguments
Prove that accused AI systems don’t meet all claim limitations, use materially different technical approaches, or fall outside claim scope.
Inter Partes Review
Defendants can petition the Patent Trial and Appeal Board for Inter Partes Review (IPR) challenging patent validity. IPR provides faster, less expensive validity determinations than district court litigation.
Many AI patent disputes involve coordinated IPR and litigation strategies.
Trade Secret Litigation Tactics
Establishing Trade Secret Status
Plaintiffs must prove information qualifies as trade secrets by showing independent economic value, lack of general knowledge, and reasonable secrecy measures.
Document security practices comprehensively to support trade secret claims.
Proving Misappropriation
Demonstrate that defendants acquired secrets improperly, through breach of confidentiality, espionage, or from parties who breached duties.
Circumstantial evidence like suspicious timing, rapid development, or similarity to plaintiff’s methods can support misappropriation claims.
Inevitable Disclosure Doctrine
Some jurisdictions recognize “inevitable disclosure”—that employees with trade secret knowledge will inevitably use it at new employers. This can support injunctions against competitive employment even without proven misappropriation.
However, many states reject or limit this doctrine.
Contract Dispute Resolution
Interpreting AI Performance Terms
Contract disputes often center on whether AI systems met performance specifications. Interpret contract language regarding accuracy thresholds, functionality requirements, and service level agreements.
Contemporaneous communications and course of dealing may clarify ambiguous terms.
Warranty and Limitation of Liability Enforcement
Vendors invoke warranty disclaimers and liability limitations to cap exposure. Customers challenge these provisions as unconscionable or argue exceptions apply for gross negligence or breaches of specific warranties.
Damages Calculations
Contract damages aim to put non-breaching party in the position they’d occupy if contract was performed. This may include cost of replacement AI services, lost profits from system failures, or costs to remediate defective systems.
Regulatory Defense Strategies
Responding to Government Investigations
When regulators investigate AI practices, respond promptly and professionally, preserve relevant documents and data, coordinate through counsel to maintain privilege, and consider whether cooperation or contest is optimal.
Civil Investigative Demands and Subpoenas
Regulators may issue Civil Investigative Demands (CIDs) or subpoenas requiring document production and testimony. Challenge overly broad or burdensome demands while preserving working relationships with regulators.
Consent Decrees and Settlement
Many regulatory matters resolve through consent decrees requiring compliance commitments, monitoring and reporting, and civil penalties.
Evaluate whether settlement terms are acceptable compared to litigation risks.
Alternative Dispute Resolution
Mediation
Mediation involves neutral third parties facilitating settlement negotiations. Mediation is non-binding unless parties reach agreement, preserves confidentiality, and can occur at any litigation stage.
Many courts require mediation before trial.
Arbitration
Arbitration produces binding decisions from arbitrators selected by parties. It offers faster resolution than litigation and specialized expertise but limits discovery and appeals.
Expert Determination
For technical disputes, parties may submit issues to technical experts for binding or non-binding determinations. This works well for narrow technical questions like whether systems meet specifications.
Settlement Considerations
Licensing as Resolution
IP disputes often resolve through licensing agreements where alleged infringers obtain licenses rather than cease activities. This allows both parties to benefit commercially.
Business Relationship Preservation
Settlement may preserve valuable business relationships that litigation would destroy. Consider whether ongoing partnerships justify compromise.
Confidential Settlements
Confidential settlements prevent public disclosure of dispute details, settlement amounts, or admissions of liability. Confidentiality can protect reputations and avoid encouraging additional claims.
Conclusion: Strategic Dispute Management
AI-related disputes require sophisticated strategies addressing novel legal questions, complex technical evidence, and rapidly evolving legal standards. Effective approaches include early case assessment and strategy development, robust evidence preservation and discovery management, technical expert collaboration, consideration of settlement alternatives, and understanding of AI-specific legal and technical issues.
Companies that prepare for potential disputes proactively and handle them strategically minimize business disruption and protect valuable AI investments.
Contact Rock LAW PLLC for AI Litigation Counsel
At Rock LAW PLLC, we represent clients in AI-related disputes and litigation.
We assist with:
- Copyright and patent infringement defense
- Trade secret litigation
- Contract dispute resolution
- Regulatory investigation response
- Pre-litigation strategy and counseling
- Alternative dispute resolution
Contact us for experienced representation in AI disputes and litigation matters.
Related Articles:
- Responding to AI Cease and Desist Letters
- Who Owns AI-Generated Content?
- Liability for AI Model Providers
Rock LAW PLLC
Business Focused. Intellectual Property Driven.
www.rock.law/