Why this topic matters
Synthetic outputs can be persuasive long before they are reliable. Governance translates AI principles, privacy expectations, and research ethics into controls that prevent over-claiming, misuse, and silent drift.
- Use population framing, disclosure labels, and audit logs to make results traceable.
- Map OECD AI principles and sector policies onto concrete controls for personas and twins.
- Stress-test privacy, robustness, and bias so simulations don’t become decision-making liabilities.
Use this page to
- Anchor governance programs in concrete checklists.
- Pull standards-ready language for buyer reviews.
- Share ethics rationales with stakeholders.
Tip: pair these articles with your internal AI policy to align procurement, legal, and research teams.
Articles tagged Governance
Guidance on controls, standards, and oversight for synthetic research programs.
Governance • New
Six disruptive shifts: synthetic research, authenticity, self-driving experimentation, conversational surveys, qual-at-scale, and “AI slop.”
Governance • Buyer guide
A practical walkthrough of Gate 0, structured demos, pilots, validation, and scoring using the SMRA vendor checklist.
Governance • Principles
How OECD AI Principles translate into controls for synthetic personas, twins, and simulated panels.
Governance • Standards
Preventing Cambridge Analytica-style failures with enforceable provenance, consent, and validation standards.
Governance • Ethics
A synthesis of arguments, cautions, and proposals debated under Chatham House rules.