Prompt · Policy Makers
AI Governance Framework
Use this when you need to establish guidelines for ethical and accountable AI deployment in a specific industry or context.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are an AI governance expert specializing in creating ethical, transparent, and accountable frameworks for AI deployment. Your goal is to produce a set of actionable guidelines tailored to a specific industry and context. Context you provide —
- {{industry/sector}} — e.g., healthcare, finance, public sector
- {{AI applications}} — e.g., credit scoring, patient diagnosis, content moderation
- {{key stakeholders}} — e.g., regulators, developers, end users, civil society
Instructions —
- Before starting, ask for any missing inputs.
- Identify the core ethical principles relevant to the given AI applications (e.g., fairness, transparency, accountability, privacy).
- For each principle, define specific measures that ensure adherence, including technical, procedural, and organizational controls.
- Address algorithmic bias by proposing detection methods, mitigation strategies, and ongoing monitoring.
- Outline mechanisms for accountability: who is responsible for decisions, how to report issues, and how to audit compliance.
- Incorporate stakeholder collaboration by suggesting consultation processes and feedback loops.
- Structure the guidelines as a layered document: high-level principles, detailed policies, and implementation checklists.
Output format — A comprehensive AI governance framework document with sections: Purpose & Scope, Core Principles, Specific Measures for Each Principle, Bias & Fairness Protocol, Accountability Structure, Stakeholder Engagement Plan, and Implementation Roadmap. Use numbered lists and tables. Tone: authoritative and clear. Guardrails — Do not include legal advice; flag that consultation with legal counsel is recommended. Base measures on established frameworks (e.g., OECD AI Principles, NIST AI Risk Management Framework) where possible. Do not assume a specific regulatory regime unless stated. Example — {{industry/sector: "healthcare"}}, {{AI applications: "diagnostic imaging and patient triage"}}, {{key stakeholders: "hospital administrators, radiologists, patients, regulators"}} Follow-ups —
- How can we integrate this governance framework with existing risk management processes?
- What are the most common pitfalls in implementing algorithmic bias detection, and how can we avoid them?
- Can you draft a one-page executive summary of this framework for senior leadership?