Editor’s note: The following is a sponsored blog post from ProcessUnity.
Artificial Intelligence is reshaping procurement, governance, risk and compliance (GRC), vendor assessments, and third-party risk management (TPRM) at a rapid pace. Organizations are understandably eager to leverage AI-powered solutions for an ever-expanding list of potential use cases.
Risk management and procurement teams are keen to incorporate AI to improve process efficiency and reduce their workload with better visibility into their third-party ecosystem and less need for manual review. However, with great power comes great responsibility, and in the case of AI, that means ensuring the technology is implemented and used ethically.
For many organizations, ethical AI considerations have not yet made their way into the third-party compliance checklist. We talk about cybersecurity posture and data protection safeguards, but rarely do we ask how a third party’s AI capabilities are sourced, trained, tested and governed.
So, how do you go beyond the checkbox approach and embed ethical AI principles into your third-party assessments? Here are five practical steps:
1. Introduce Ethical AI as a Core Requirement in Your Vendor Risk Framework
Most third-party risk frameworks include categories such as data protection, financial stability and regulatory compliance. Ethical AI should be treated as its own control category, with structured questions and criteria. This means asking vendors how they:
- Source and curate training data
- Mitigate bias in development and deployment
- Test AI models for fairness, sensitivity and transparency
- Govern AI usage internally, including maintaining human oversight
By signaling that ethical AI is a standard requirement and not just a “nice to have,” you set the tone that responsible AI is now expected across your vendor ecosystem.
2. Evaluate the Maturity of a Third Party’s AI Governance Program
When evaluating a third party’s use of AI, limiting assessment to “yes/no” questions isn’t enough. Use maturity-based questions that drive meaningful insight into how AI is governed, including asking third parties questions such as:
- What established ethical AI frameworks and standards have you incorporated into the design, development, testing and deployment of your AI products?
- What type of reviews are conducted to ensure that your AI products are operating under responsible AI practices?
- What independent verification or certification have you obtained that demonstrates the security and ethical operations of your AI products?
- What plain language documentation is available that explains the purpose, functionality and limitations of your AI solutions?
- What mechanisms exist for users of your AI solutions to report incidents of bias, discrimination or hallucination?
Treat AI governance much like cybersecurity maturity, with defined levels and expectations. Strong, executive-led governance is a leading indicator that the third party assesses and manages AI risks across their entire ecosystem and will treat emerging AI risks proactively and transparently.
3. Require Transparency Around AI Use Cases and Model Inputs
One of the most effective ways to uncover potential ethical issues before they become risks is to insist on transparency. Third parties should be prepared to explain, in plain terms, how they use AI to deliver their services. This includes disclosing the specific use cases where AI is applied and clarifying whether and where those systems directly impact end users.
It's also critical for your team to maintain visibility into the data sets that underpin these AI models and the data used for training and inference, which sometimes reveal potential sources of bias or gaps in coverage.
Third parties should be forthcoming about the limitations of their models and the areas where outputs may be uncertain. These disclosures do not require revealing proprietary code or algorithms. Still, they provide your procurement and compliance teams with a high-level understanding of how a system functions and the assumptions it relies on, giving critical insight for assessing ethical risk.
4. Build Ethical AI Clauses into Your Contracts and Assessments
Assessing a third party’s approach to ethical AI is an important first step, but it is only meaningful if it’s reinforced with contractual obligations. When first signing on with a third party, procurement teams should complement initial vendor scoring and questionnaires by adding ethical AI commitments directly into initial contracts. Ethical AI contract language may specify internally developed requirements or reference widely accepted frameworks and legislation such as the EU AI Act, the OECD AI Principles or the AI Bill of Rights.
This means drafting clauses that explicitly require third parties to adhere to defined principles of responsible AI, while also obligating them to disclose any significant changes to their AI systems over time. Contracts can establish rights for the buying organization to audit AI systems, ensuring that transparency isn’t just voluntary but enforceable.
You can also require third parties to notify you promptly if an AI incident or unintended consequence arises, so remediation can happen quickly, and any further impacts can be avoided upfront.
5. Leverage AI to Improve (Not Replace) Human Judgment in Procurement
Finally, it’s important to recognize the role of your own team in ensuring ethical AI practices. AI can streamline data collection, surface patterns and ease the assessment lifecycle, but final decisions around vendor risk should still involve human review.
Instead of replacing analysts with AI-generated decisions, use AI to:
- Collect, organize and analyze large and disparate data sets
- Perform rapid risk analysis and make recommendations for next steps
- Flag potentially contradictory risk data or program gaps that require additional review
- Summarize large volumes of third-party policy and procedure documentation
- Identify whether third-party evidence validates their questionnaire responses
- Track changes and trends over time to prioritize follow-up
Human oversight is one of the most effective checks against unintended bias or misuse. When AI augments (rather than replaces) the procurement and compliance process, you’ll gain efficiency, accuracy and integrity.
Parting Thought
Third-party compliance has always required balancing innovation and risk. As AI becomes more deeply embedded into every product and service, ethical use must be treated as a foundational requirement. By expanding your enterprise governance and risk assessments to include ethical AI principles, you’re not only reducing third-party risk for your own organization, you are actively shaping a more responsible third-party ecosystem for everyone.
To learn more about how ProcessUnity helps evaluate a third party’s use of AI, visit our website.
