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What are the core principles of responsible AI procurement for government
Responsible AI procurement for government agencies centers on prioritizing public benefit, managing risks through multidisciplinary oversight, and ensuring transparency throughout the AI lifecycle[1]. Agencies should focus on problem-based procurement rather than prescribing specific technical solutions, allowing for iterative development and early market engagement[2].
The following table summarizes key principles for responsible AI procurement:
| Requirement/Finding | Jurisdiction | Status | Scope | Source | Date |
|---|---|---|---|---|---|
| Conduct initial AI impact assessment | Global/General | Guidance | Public Sector | NIST[3] | June 2020 |
| Establish multidisciplinary teams | Global/General | Guidance | Public Sector | NIST[4] | June 2020 |
| Ensure algorithmic accountability | Global/General | Guidance | Public Sector | NIST[5] | June 2020 |
| Secure data governance mechanisms | Global/General | Guidance | Public Sector | NIST[6] | June 2020 |
Core Procurement Checklist
- Problem Definition: Clearly articulate the challenge and public benefit goal before selecting AI as the solution[7].
- Risk Assessment: Perform an initial AI impact assessment to identify potential harms, such as bias or inaccuracy, and revisit this at every decision point[8].
- Transparency & Auditability: Require vendors to provide documentation on model methodology, training data, and known biases, and include provisions for independent audits where appropriate[9][10].
- Data Access: Ensure the agency retains access to raw input and enriched output data produced by the vendor to monitor performance and rectify issues[11].
- Vendor Accountability: Mandate knowledge transfer and training as part of the contract to ensure agency staff can oversee the system's operation[12].
Would you like to explore how these procurement guidelines align with specific risk management frameworks like the NIST AI RMF?
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