AI Contract Structuring

Traditional contract paradigms were conceived for deterministic technologies where system behavior could be exhaustively specified and performance objectively measured. Artificial intelligence disrupts these foundational assumptions. AI systems exhibit emergent behaviors, their outputs vary with input data characteristics, and their performance may drift over time as underlying models evolve. These realities demand contractual architectures specifically engineered for algorithmic transactions.

The allocation of liability in AI deployments presents particularly intricate challenges. When an AI system produces an erroneous output causing commercial harm, responsibility may distribute across multiple parties: the model developer, the training data provider, the system integrator, the deploying enterprise, and potentially the end user who provided input data. Standard limitation of liability clauses—drafted for conventional software where the vendor controls all code execution—require fundamental reconceptualization for AI contexts where system behavior emerges from the interaction of model, data, and environment.

Intellectual property provisions in AI contracts require careful calibration. Training data may carry pre-existing IP rights with onward licensing restrictions. Model architectures may embody patented techniques. The outputs of generative AI systems raise novel questions about copyright subsistence and ownership. Our practice structures IP clauses that provide commercial certainty while acknowledging the distinctive characteristics of AI-generated and AI-assisted works, including appropriate provisions for regulatory scenarios where model deletion (algorithmic disgorgement) may be mandated.

Service level agreements for AI systems demand metrics appropriate to probabilistic technologies. Unlike traditional software where uptime and response time suffice, AI SLAs must address accuracy rates, false positive and negative thresholds, inference latency, model update frequencies, and performance degradation tolerances. We draft SLAs that establish meaningful performance baselines while preserving the vendor's ability to improve systems and the customer's right to audit claimed performance levels. Remediation provisions address not merely service credits but practical recourse when AI outputs fall below contracted standards.

Regulatory compliance obligations require explicit contractual allocation. Under the EU AI Act, providers and deployers carry distinct obligations depending on their role in the AI value chain. Contracts must clearly establish which party bears responsibility for conformity assessments, technical documentation, human oversight implementation, and post-market monitoring. We structure compliance cascades that flow regulatory obligations appropriately while establishing cooperation frameworks for regulatory inquiries and enforcement proceedings.

Data governance provisions assume heightened significance in AI contexts. Training data quality directly impacts model performance, while inference data may require specific protections. Contracts must address data retention obligations, particularly where ongoing model improvement requires historical data access, while respecting data protection requirements including data minimization principles and purpose limitation. Our approach balances legitimate commercial interests in data-driven improvement with regulatory compliance and data subject rights.

Exit provisions for AI engagements present unique considerations. Model lock-in—where system performance depends on proprietary model access—can create switching costs exceeding traditional software transitions. We structure contracts with appropriate portability provisions, escrow arrangements for critical model components, and transition assistance obligations that protect client interests while respecting legitimate vendor IP protection needs. These provisions assume particular importance given regulatory scenarios where model access may be required for auditing or enforcement cooperation.