Why Do Warranties Matter in AI Contracts?
Warranties and representations in software and AI service contracts allocate risk between parties, establish baseline expectations for system performance and legal compliance, provide remedies when systems fail to meet standards, and influence pricing, insurance, and overall deal structure.
For AI technologies, traditional software warranties often prove inadequate. AI systems exhibit unique characteristics including probabilistic rather than deterministic outputs, emergent behaviors difficult to predict, ongoing learning that changes performance, and potential bias or accuracy issues. These characteristics require carefully tailored warranties that protect customers without imposing impossible obligations on AI providers.
Both AI vendors and customers need to understand what warranties are appropriate, how to limit liability for AI-specific risks, and how to structure remedies that address real concerns without derailing commercial relationships.
Common Software Warranties Adapted for AI
Functionality and Performance Warranties
Traditional software contracts often warrant that systems will perform “substantially in accordance with documentation.” For AI systems, this warranty requires adaptation because AI outputs are probabilistic and variable, documentation may describe typical rather than guaranteed performance, and improvement over time may be expected rather than immediate perfection.
Better approaches specify performance benchmarks or accuracy thresholds measured over time periods, acknowledge variability in individual predictions while warranting aggregate performance, and define acceptable error rates or confidence intervals.
For example, rather than warranting that an AI classification system will correctly classify every input, warrant that it will achieve 95% accuracy on representative test datasets.
Non-Infringement Warranties
Vendors typically warrant that software doesn’t infringe third-party intellectual property rights. For AI systems, infringement risks include training data potentially violating copyrights, model architectures possibly infringing patents, outputs potentially reproducing copyrighted works, and use of third-party models or components.
AI vendors should carefully scope non-infringement warranties, potentially excluding infringement claims related to customer-provided training data, specific uses or configurations customer implements, and third-party components subject to separate licenses.
Customers should ensure warranties cover core AI functionality and that indemnification provisions address infringement claims from use as contemplated.
Compliance with Laws
Customers often require vendors to warrant compliance with applicable laws. For AI, relevant laws include data privacy regulations like GDPR and CCPA, AI-specific regulations like the EU AI Act, anti-discrimination and civil rights laws, and sector-specific regulations for healthcare, financial services, etc.
Vendors should limit compliance warranties to their own operations and systems rather than warranting how customers use AI. Compliance is often a shared responsibility—vendors provide compliant technology while customers use it compliantly.
AI-Specific Warranties
Training Data Quality and Provenance
Customers may seek warranties about training data including that data was lawfully obtained with necessary rights and consents, that data meets minimum quality standards, that data doesn’t contain malicious content or intentional bias, and that data sources are documented.
Vendors should qualify these warranties based on known information rather than absolute guarantees, particularly for publicly available datasets or third-party data where provenance may be unclear.
Bias and Fairness
Customers concerned about algorithmic discrimination may request warranties that AI systems have been tested for bias, that systems meet specific fairness metrics, or that systems won’t produce discriminatory outcomes.
Vendors should be cautious about broad non-discrimination warranties because bias is complex and context-dependent, fairness metrics can be mathematically incompatible, and vendor control over discriminatory outcomes may be limited if customers control deployment.
Better approaches include warranting that specific bias testing was conducted with documented results, committing to ongoing monitoring and updates, and providing tools enabling customers to conduct their own bias assessments.
Explainability and Transparency
For regulated applications, customers may need warranties about AI explainability including availability of model documentation, ability to generate explanations for individual predictions, and transparency about factors influencing outputs.
Vendors should specify the type and level of explainability provided, which varies significantly across AI approaches. Complex neural networks may offer limited explainability compared to simpler models.
Security and Adversarial Robustness
AI systems face unique security risks including adversarial attacks manipulating model behavior, model extraction or stealing, and data poisoning corrupting training.
Security warranties should address that reasonable security measures are implemented, that systems undergo security testing, and that known vulnerabilities are promptly addressed, while acknowledging that no system is completely secure against all attacks.
Disclaimer of Warranties
What Can Be Disclaimed
Vendors typically disclaim implied warranties including merchantability, fitness for particular purpose, and non-infringement beyond express warranties. These disclaimers limit exposure to unlimited implied obligations.
For disclaimers to be enforceable, they must be conspicuous (often in caps or bold), clear and unambiguous, and not unconscionable.
AS-IS and No Warranty Provisions
Some vendors, particularly for free or low-cost AI tools, provide services “AS-IS” with no warranties. While this maximizes vendor protection, customers often resist AS-IS terms for mission-critical systems or when paying substantial fees.
AS-IS provisions may be appropriate for beta or experimental AI features, free tier services, or open-source AI projects where vendors provide no commercial support.
Limitations for AI Outputs
Given AI unpredictability, vendors often disclaim warranties about accuracy of outputs, suitability for specific decisions, or absence of errors or hallucinations. Disclaimers should direct customers to verify AI outputs before relying on them for important decisions.
Representations in AI Transactions
Development and Testing Representations
Vendors may represent that AI systems were developed following industry best practices, underwent specified testing and validation, and meet stated performance benchmarks during testing.
These representations provide accountability without guaranteeing future performance in customer environments.
No Harmful Content Representations
Vendors may represent that systems weren’t trained on or designed to generate illegal content, malware or harmful code, or content violating third-party rights.
Qualify these representations as “to vendor’s knowledge” or “based on reasonable investigation” rather than absolute guarantees.
Regulatory Compliance Representations
Vendors may represent that they’ve conducted required regulatory assessments, obtained necessary certifications or approvals, and comply with AI-specific regulations applicable to their operations.
Remedy Provisions for Warranty Breaches
Cure Periods and Remediation
When warranties are breached, contracts typically provide cure periods allowing vendors to fix issues. For AI systems, remediation might involve retraining models, adjusting parameters or thresholds, providing patches or updates, or offering alternative approaches.
Specify reasonable cure periods recognizing that model retraining may take substantial time.
Service Credits and Refunds
Performance warranty breaches may trigger service credits reducing future fees or, in severe cases, refunds of paid amounts. Structure credits proportionate to warranty breach severity.
Termination Rights
Material warranty breaches that aren’t cured within specified periods may allow customers to terminate contracts. Define what constitutes “material” breach to avoid disputes.
Limitation of Liability Clauses
Damage Caps
AI contracts typically limit vendor liability to amounts paid or payable under contracts, often over specified periods (e.g., 12 months of fees). This protects vendors from catastrophic exposure exceeding contract value.
Customers may negotiate higher caps for certain breach categories like data breaches or IP infringement.
Consequential Damages Exclusions
Vendors exclude liability for consequential, indirect, incidental, or special damages including lost profits, business interruption, reputational harm, and third-party claims.
These exclusions prevent liability for losses far exceeding contract value but unrelated to vendor fault.
Exceptions to Limitations
Certain obligations may be carved out from liability limitations including IP indemnification obligations, confidentiality breaches, willful misconduct or gross negligence, and data breach obligations.
Balance vendor protection with customer assurance about critical issues.
Indemnification Provisions
IP Indemnification
Vendors typically indemnify customers against third-party IP infringement claims arising from use of AI systems as provided. Indemnification covers defense costs and damages, subject to customer promptly notifying vendor of claims, allowing vendor to control defense, and cooperating in defense.
For AI, clarify that indemnification covers core system functionality but not customer data, customer configurations, or uses outside specifications.
Data Breach Indemnification
Some contracts require vendors to indemnify customers for data breaches caused by vendor’s security failures. This can be high-exposure obligation given breach notification costs, credit monitoring, and regulatory penalties.
Vendors should cap data breach indemnification or obtain appropriate insurance.
Customer Indemnification
Vendors may require customers to indemnify against claims arising from customer’s use of AI systems, customer-provided data, or customer violations of law or contract terms.
Testing and Acceptance Procedures
Acceptance Testing Criteria
Define objective criteria for accepting AI systems including performance benchmarks, functionality requirements, and integration specifications. Specify testing periods and procedures.
For AI, acceptance criteria should reflect realistic performance rather than perfection.
Beta or Pilot Deployments
Consider phased rollouts with beta periods where warranties are limited, allowing customers to evaluate AI before full deployment and vendors to refine systems based on real-world use.
Ongoing Monitoring and Updates
Continuous Improvement Commitments
AI vendors may commit to ongoing monitoring and improvement including regular bias testing and remediation, model updates to maintain accuracy, and security patches and protections.
Performance Degradation Warranties
Warrant that model performance won’t degrade below specified thresholds over time, accounting for concept drift, data distribution changes, or model staleness.
Conclusion: Balancing Protection and Practicality
Warranties in AI contracts must acknowledge technology’s probabilistic nature and unique characteristics while providing meaningful protections. Effective approaches set realistic performance expectations, allocate risks based on party control, provide appropriate remedies without imposing impossible obligations, and balance vendor innovation with customer accountability.
Contact Rock LAW PLLC for AI Contract Drafting and Negotiation
At Rock LAW PLLC, we help companies structure AI software contracts with appropriate warranties and risk allocation.
We assist with:
- AI software license agreement drafting
- SaaS and cloud AI contract negotiation
- Warranty and indemnification provisions
- Limitation of liability structuring
- Customer contract template development
- Enterprise agreement negotiation support
Contact us to structure AI contracts protecting your interests while enabling successful technology transactions.
Related Articles:
- Key Contract Provisions for SaaS and AI Development
- Terms of Service for AI API Providers
- Data Processing Agreements for AI Companies
Rock LAW PLLC
Business Focused. Intellectual Property Driven.
www.rock.law/