AI Responsible AI Procurement Framework Playbook

A state government CIO's office is developing a responsible AI procurement policy for all state agencies. Any AI system purchased must pass a pre-procurement assessment. The policy must address bias, privacy, security, transparency, and vendor accountability across 42 agencies and $180M in annual AI spending.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A state government CIO's office is developing a responsible AI procurement policy for all state agencies.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Responsible AI Procurement Framework".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Current state IT procurement policies
• NIST AI Risk Management Framework
• Other state AI procurement policies (3 examples)
• Agency use case inventory (42 agencies)
• State privacy law and data governance requirements

Attachments: Documents (Documents)

You are an AI policy specialist developing a responsible AI procurement framework for a state government. I am attaching:

Work only from the attached source files. If a conclusion is not supported, say so.

Produce:
1. Design the pre-procurement AI risk assessment: what questions must agencies answer before purchasing any AI system?
2. Build the tiered review framework: which AI systems can agencies purchase without additional oversight (low risk), which require CIO review (medium risk), and which require a full Algorithmic Impact Assessment (high risk)?
3. Define the mandatory vendor contract requirements: what minimum transparency, bias testing, audit rights, and incident notification obligations must vendors meet?
4. Design the ongoing monitoring requirement: how do agencies report on deployed AI performance, and what triggers a post-deployment review?
5. Tell me the implementation roadmap: how to phase in requirements across 42 agencies with different technical capacity, and what the enforcement mechanism is.

Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.

What to expect

• Pre-procurement AI risk assessment design
• Tiered review framework with risk thresholds
• Mandatory vendor contract requirements
• Ongoing monitoring and post-deployment review triggers
• Implementation roadmap with enforcement mechanism

Review before you act

• Validate this output against source files before relying on it: Design the pre-procurement AI risk assessment: what questions must agencies answer before purchasing any AI system?.
• Validate this output against source files before relying on it: Build the tiered review framework: which AI systems can agencies purchase without additional oversight (low risk), which require CIO review (medium risk), and which require a full Algorithmic Impact Assessment (high risk)?.
• Validate this output against source files before relying on it: Define the mandatory vendor contract requirements: what minimum transparency, bias testing, audit rights, and incident notification obligations must vendors meet?.
• Validate this output against source files before relying on it: Design the ongoing monitoring requirement: how do agencies report on deployed AI performance, and what triggers a post-deployment review?.
• Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
• Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
• Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.