When AI Trains on AI: Model Collapse and the Case for Preserving Human Data
Model collapse can make generative AI narrower and less reliable when synthetic outputs feed future training. Learn what the evidence shows and how to protect original data.
Tag: AI governance
When AI Flags a Problem, Who Owns the Decision? Build a Clear Escalation Map
Give AI alerts a named decision owner, evidence requirement, deadline and fallback so a warning can lead to accountable action.
Give Every AI Agent a Small Key: A Practical Permissions Plan for Small Teams
Define an AI agent’s task, access, allowed actions and review points before connecting it to business systems.
An AI System Card Is Evidence, Not a Safety Guarantee: How to Read One Properly
Learn how to read an AI system card critically: identify the tested model, evaluation scope, deployment context, limitations, gaps and monitoring needs.
The Model Passed Its Test. Production Is a Different Experiment
An AI system can pass laboratory tests and still fail in production. Build a monitoring plan for drift, uneven errors, incidents, human overrides and retirement.
Consent Must Travel With the Data: A Rule for AI Personalisation
Responsible AI personalisation requires consent, purpose and revocation to travel with data as it moves from collection to inference and action.
The Best AI Leader Does Not Approve Everything: Design Escalation Rules
Effective AI leadership replaces constant approvals with clear escalation rules based on impact, reversibility, permissions and uncertainty.
OpenAI’s Misalignment Reporting Framework Signals a New Phase for AI Safety
OpenAI has introduced a framework for investigating and reporting model misalignment, raising an important question about how AI incidents should be documented.
Small Business AI Governance: A Simple Policy That Works
Create a practical AI policy for your small business covering privacy, accuracy, human review, approved tools and accountability.
Bill Gates Says Governments Are Not Ready for AI: The Real Challenge Is Social Infrastructure
Bill Gates warned on 15 September 2026 that governments worldwide are unprepared for AI’s effects on jobs, security and wellbeing. The deeper challenge may be institutional adaptation: education, welfare, regulation and public services must evolve alongside intelligence itself.