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Guide Origin: Official | ArcKit Version: [VERSION]

/arckit:ai-playbook assesses UK Government AI Playbook compliance for responsible AI deployment in public sector projects.

Inputs

Artefact Purpose
Requirements (ARC-<id>-REQ-v1.0.md) AI-related functional and non-functional requirements
Architecture diagrams AI system components and data flows
Data model Training data sources, personal data processing
Stakeholder drivers Business context and user impact

Command

/arckit:ai-playbook Assess AI Playbook compliance for <project>

Output: projects/<id>/ARC-<id>-AIPB-v1.0.md

Assessment Structure

Section Contents
Executive Summary Overall compliance status and key findings
AI System Overview System description, use cases, decision types
10 Principles Assessment Compliance against each AI Playbook principle
Risk Assessment AI-specific risks (bias, transparency, accountability)
Data Governance Training data quality, bias mitigation, personal data
Human Oversight Human-in-the-loop requirements, escalation paths
Explainability How decisions can be explained to users
Monitoring & Evaluation Ongoing performance and fairness monitoring
Compliance Roadmap Actions needed for full compliance

10 AI Playbook Principles

# Principle Focus Area
1 Use AI to support public sector tasks Legitimate public benefit
2 Have clear goals and robust evaluation Measurable outcomes
3 Be fair, accountable and transparent Bias mitigation, explainability
4 Consider the full lifecycle Development through retirement
5 Use quality data responsibly Data governance and ethics
6 Ensure adequate human oversight Human-in-the-loop controls
7 Keep data secure and manage risks Security and risk management
8 Use existing tools where appropriate Avoid reinvention
9 Build internal capability Skills and knowledge transfer
10 Work in the open Transparency and collaboration

One-Page Workflow

Phase Key Activities ArcKit Commands
Discovery Define AI use cases and requirements /arckit:requirements, /arckit:stakeholders
Data Design data model and assess data quality /arckit:data-model, /arckit:dpia
Governance Assess AI Playbook compliance /arckit:ai-playbook
Transparency Create algorithmic transparency record /arckit:atrs
Operations Plan ongoing monitoring and governance /arckit:mlops, /arckit:operationalize

Review Checklist

  • All 10 AI Playbook principles assessed with evidence.
  • Bias risks identified with mitigation measures.
  • Human oversight requirements clearly defined.
  • Explainability approach documented for affected users.
  • Training data quality and governance addressed.
  • Ongoing monitoring plan for fairness and performance.
  • Compliance roadmap with owners and timelines.
Requirement Document When Needed
Algorithmic Transparency ARC-<id>-ATRS-v1.0.md Public-facing algorithmic decisions
Data Protection Impact ARC-<id>-DPIA-v1.0.md AI processing personal data
MOD AI Assurance ARC-<id>-JSP936-v1.0.md Defence AI systems

Key Principles

  1. Public Benefit First: AI must serve legitimate public sector goals.
  2. Fairness by Design: Bias detection and mitigation from the start.
  3. Transparency: Users should understand how AI affects them.
  4. Human Oversight: Appropriate human control based on risk level.
  5. Continuous Evaluation: Ongoing monitoring, not just initial assessment.