Locking In Trust: Key Terms For Strong AI Vendor Contracts - Above the Law

"Defining Responsibility Clearly Every AI contract should start with an unambiguous allocation of responsibility. If the system produces harmful results, fails accuracy tests, or violates applicable laws, the agreement should state who is accountable. This includes performance standards, quality controls, and obligations to fix problems promptly. Regulatory compliance cannot be assumed. Vendors should commit to meeting relevant laws and notify you immediately if legal changes require updates to the system or its deployment."

"Demanding Operational Transparency To manage risk, you need visibility into the AI system. That means contractual rights to documentation that explains how it works, where its data originates, and how it reaches its conclusions. This might take the form of technical summaries, training data disclosures, and change logs for updates. Without this information, you may be left unprepared when a regulator asks for details or when a customer challenges the product's decisions."

AI innovation relies on partnerships with cloud providers, niche developers, and data vendors that accelerate products but introduce operational and legal risk. Vendor agreements must allocate responsibility for harmful outputs, accuracy failures, and legal violations, and must include performance standards and remediation obligations. Vendors should commit to meeting applicable laws and promptly notify buyers of legal changes that affect the system. Contracts should grant visibility into system design, data origins, and decision logic through technical summaries, training data disclosures, and change logs. Agreements must also clarify model, output, and data ownership and licensing to prevent misunderstandings.

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