AI and Third-Party Systems Liability

The Next Legal Frontier in the AI Supply Chain

Artificial Intelligence is transforming every layer of modern business—from finance and cybersecurity to healthcare and patient services. But as organizations integrate AI through third-party systems, APIs, and cloud vendors, they may also be inheriting hidden liability. The question is not if something will go wrong. But, when something goes wrong, who will be responsible? 

The Expanding Web of AI Responsibility

AI no longer operates as a closed system. Hospitals, payers, and enterprise platforms rely on third-party systems and components for everything from data ingestion, model training, and algorithmic decision support to billing automation and Electronic Health Record (EHR) integration. Systems, whether they are designed, integrated, or implemented by licensed vendors or custom-built in-house, share accountability for adhering to regulatory frameworks. Review one or more of the following guidelines below to ensure quality and compliance standards are adhered to:

• AI Transparency Guidelines (FTC)

• AI Governance NAIC Model Bulletin (U.S. Insurance)

• AI Act and Digital Services Act (EU)

• CMS and HIPAA interoperability rules (for healthcare AI)

When AI errors occur—be it a billing misclassification, a denied claim, or a false compliance alert—third-party liability becomes murky. The organization deploying the AI may face reputational damage, while the vendor providing the underlying model or data pipeline could face legal exposure.

Where Does Liability Live?

  1. Algorithmic Decision Errors

If an AI system incorrectly codes a patient encounter or auto-approves a non-compliant claim, the organization faces audit risk. However, if the logic flaw stems from a third-party algorithm, the liability could extend upstream—especially if the vendor failed to disclose training data limitations or bias.

2. Integration and Interoperability Failures

Third-party APIs that bridge EHRs, billing systems, and AI engines can create data integrity breaks. Under CMS and HIPAA rules, liability follows the “data controller” and “data processor” model—meaning both parties may share fault for a breach or bad decision.

3. Negligent Model Oversight

Organizations that “blindly trust” vendor models without validation, testing, or documentation may be deemed negligent. ISO 9001 emphasizes Quality Management accountability, meaning you cannot outsource risk; you can only mitigate it through governance.

4. SaaS and Cloud Dependency

If your AI system relies on a vendor’s infrastructure (like Cloud, AWS Health Lake, or Azure OpenAI), disruptions or misconfigurations may expose sensitive data. Contracts should include service level agreement guarantees, indemnification clauses, and auditability rights to ensure shared responsibility can be enforced.

Emerging Legal Precedents

Courts are finally catching up to the “foreseeable risk vectors” that AI Systems have the potential of presenting:  

In several early cases:

• Vendors can be held liable for algorithmic bias that affects insurance or employment outcomes.

• Healthcare entities can face penalties for negligent supervision of automated coding tools.

• Financial firms could be fined for not using human validation in opaque AI models.

Expect upcoming cases to refine the “Duty of Care for AI Vendors,” especially around disclosure of model provenance, data sources, and explainability.

Governance Strategies

Organizations must operationalize distinct accountability trails that govern AI:

1. Quality Control for Contractual Matters

• Track and trend the AI Service Level Agreement (SLA), Guarantee performance

• Disclosures should be required for model lineage and risk classification.

  • Does your SLA have a “Right to Suspend AI Use” if safety, bias, or compliance defects emerge?

2. Vet, Verify, and Validate

• To test, establish independent algorithm audits 

• Create and maintain version control logs for model updates and outputs.

  • What are the explainability thresholds, and who owns the responsibility for auditing questionable automated decisions?

3. Quality and Compliance Frameworks

• Map vendor systems to ISO 9001 standards.

• Implement AI Quality Scorecards to measure reliability, monitor bias, and ensure auditability.

  • Have you implemented Responsible AI Principles—Transparency, Accountability, Fairness, and Human Oversight?

4.  Documentation Governance

• Maintain AI Risk Registers and Vendor Accountability Logs.

• Record who trained, tested, validated, and deployed each algorithm.

  • Who verifies if AI Escalation Paths for errors and model drift have been established? 

The Future: AI Liability is Shared by Design

AI liability will soon be codified into contractual ecosystems, much like cybersecurity insurance or data privacy law. We will see a shift from “black box adoption” to “white box accountability”—where vendors, developers, and clients co-own the ethical and operational consequences of every AI decision. Organizations that prepare now—by embedding quality, compliance, and auditability into their AI supply chain—will not just avoid lawsuits; they will build trust capital in an era of algorithmic accountability.

Closing Thought

Artificial Intelligence does not absolve humans of responsibility—it amplifies it. Third-party liability is not just a legal conversation; it is a Quality and Compliance imperative.

The winners of the new AI era will be those who are audit-ready; they own their algorithms, document their errors, and govern their vendors responsibly. 

References

MCG Health Data Security Issue Litigation (2022-2024): United States District Court, Western District of Washington at Seattle.

European Union. (2024). Regulation (EU) 2024/1689: Artificial Intelligence Act. Official Journal of the European Union.

Federal Trade Commission (FTC). (2024). Business Guidance: Keep Your AI Claims in Check. https://www.ftc.gov/ 

International Organization for Standardization. (2015). ISO 9001:2015 Quality Management Systems – Requirements.

MCG Health Data Security Issue Litigation (2022-2024): United States District Court, Western District of Washington at Seattle.

National Association of Insurance Commissioners (NAIC). (2023). Model Bulletin on the Use of Artificial Intelligence Systems by Insurers.

About the Author

Corliss Collins, BSHIM, RHIT, CRCR, CCA, CAIMC, CAIP, CSM, CBCS, CPDC, serves as a Principal and Managing Consultant of P3 Quality, a Health Tech and AI in RCM Consulting Company. Ms. Collins stays very busy working on Epic and Cerner RCM Research projects. She also serves as a subject matter expert and a member of the Volunteer Education Committee for the American Institute of Healthcare Compliance (AIHC). She is a Member of the Professional Women's Network Board (PWN).

Disclosures/Disclaimers:

AI and Third-Party Systems Liability in RCM Ecosystems. This analysis draws on research, trends, and innovations in the AI in Revenue Cycle Management (RCM) industry. AI generates some of the blog content and details. Reasonable efforts have been made to ensure the validity of all materials and the consequences of their use. If any copyrighted material has not been properly acknowledged, use the contact page to notify us so we can make the necessary updates. P3 Quality is a Responsible AI in RCM Governance and Stewardship leader who identifies gaps, supports addressing the issues, and recommends results-driven solutions.