Risk Mitigation in AI Procurement
Throughout the AI procurement lifecycle, procurement professionals are presented with many opportunities to mitigate AI risks.
Risk Management Framework for Procuring AI Solutions

How to manage AI risks using the Procurement Lifecycle as a Governance Control Mechanism
STEP 1 - Establish a risk appetite for EACH procurement​
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STEP 2 - Create risk-aware solicitation requirements
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STEP 3 - Assess the risks presented by each vendor / solution​
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STEP 4 - Establish risk controls to satisfy the risk appetite​
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STEP 5 - Monitor and manage the risk controls and conditions ​
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Procurement Tools for
Responsible AI Governance
Throughout the procurement lifecycle, there are many opportunities to govern, map, measure, and manage an AI solution. Here are a few key opportunities that matter along with some helpful resources that can make each milestone make a meaningful difference in your outcomes.
Defining the Problem
Excellent AI governance begins with excellent attention to defining the problem to be solved (BEFORE finding a solution). Focusing on the root cause of the issue will shape the solution in profound ways. Don't skip this part!!!


Stakeholder Voice
Involving diverse stakeholders in the design, development, and deployment process of high-risk systems can yield safer, more responsible, and robust solutions.
AI Impact Assessments
According to the Bipartisan Policy Center, "An impact assessment is a risk assessment tool that seeks to ensure an organization has sufficiently considered a system’s relative benefits and costs before implementation."


Solicitations & Tenders
Communicating AI requirements requires a balance of identifying organizational needs, organizational risk tolerance, and performance-based expectations. Creating a risk-aware solicitation or tender is a critical element of a modernized responsible AI procurement practice.
Vendor Governance Assessments
Assessing vendor governance practices will help highlight important clues to how the benefits and risks within the AI solution may be manifested.


Solution Governance Assessments
Assessing governance practices across the AI development lifecycle provide a clearer picture of the quality and legal compliance of the AI system's output.
Solution Quality Assessments
Assessing the quality of AI solution can provide quantitative benchmark data points with respect to performance metrics that are then comparable between independent solution providers.


Assessing AI Usability & Explainability
All high-risk AI systems should be explainable. The trick is that they need to be explainable to different stakeholders (e.g., end users, installers, program managers, procurement/buyers, etc.,) - each of whom need different levels of information about how the system works.
AI Contract Clauses
Once risks have been identified, they can best be mitigated by establishing specific mitigation tactics agreed to within the contract terms and conditions. Many AI systems present common risks as noted in the following resources:


Monitoring AI Contracts
High-risk AI is like taking care of a toddler. You have to know what it is doing at all times. Monitoring AI is a necessary element of every AI depoloyment.

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