Insights
Analysis and perspectives on enterprise AI governance, safety architecture, and strategic deployment - written for technical leaders and boards.
24 articles
You can't assert one correct answer for a system that's never the same twice. What AI agent evals are, the three ways to judge quality, and why you grade the path, not just the output.
Read more - Why You Can't Unit-Test an AI AgentAn AI agent can return a clean 200, pass every health check and still be wrong. Why agent reliability needs observability and evals, not just uptime dashboards.
Read more - Why Your Monitoring Stack Can't See an Agent FailingI've spent more time than is healthy over the past few months observing something oozing out of the woodwork across multiple online AI communities.
Read more - Echo in the Machine - The Emergence of Group AI PsychosisThe "Stochastic Parrot" observation has always been intriguing to me.
Read more - The Narrow Band: On Models and RealityThere has been a subtle shift in the boardroom conversation about AI.
Read more - An 'AI-Ready' Culture is More Than Just TrainingAudit committees know how to ask whether AI is compliant and whether vendors are managed. The questions that create real accountability - the error rate on decisions that affect customers, whether any decision can be reconstructed, who can halt a deployment — rarely get raised.
Read more - Audit Committees Are Asking the Wrong AI QuestionsThe discipline that was supposed to prevent AI harm produced frameworks, principles, and declarations - then largely watched as the industry did whatever it was going to do anyway. The same pattern is repeating in AI governance right now.
Read more - Should Is Not a Control: How AI Ethics Built Its Own GraveyardUsing large language models to evaluate large language models introduces a class of systematic bias that most evaluation pipelines are not designed to detect.
Read more - The LLM-as-Judge Problem You Can't Calibrate AwayMost AI governance frameworks fail not from bad design but because the governance team can advise on a deployment without the authority to stop it.
Read more - Why Most AI Governance Frameworks Fail Before They StartA single model's failure modes can be enumerated; a multi-agent system's cannot, so a completed red team gives boards less assurance than they assume.
Read more - Red-Teaming Multi-Agent Systems: The Assurance It Can't GiveA practical look at how international standards translate into audit-ready enterprise posture.
Read more - IEEE P2863: Shaping the International Language of AI AccountabilityEnterprise AI projects rarely fail on the model. They fail on the organisation around it.
Read more - From Pilot Purgatory to Production: The AI Deployment ProblemWhat is an AI audit trail, and why are logs not enough? How to build audit trails for AI models that reconstruct decisions for compliance and governance review.
Read more - The Inference Audit Trail: Making Every AI Decision AccountableHard constraints, soft constraints and approval gates: why enterprise AI agent safety belongs at the tool level, not the model level, and how to build it.
Read more - Constraint Architecture for Autonomous AgentsOne of the inherent weaknesses of Large Language Models (LLMs) like GPT-4 is their struggle with grounding in reality.
Read more - To the Curators, Go the Spoils: The Rising Power in Graph-Enhanced AI ModelsEthical debt in artificial intelligence refers to the accumulated cost of unresolved ethical issues that arise during a system's design, development, and deployment.
Read more - Ethical debt - Rusting the machinery of progressThe rapid advancement of Artificial Intelligence (AI) and other emerging technologies has created new opportunities and challenges for businesses, requiring them to adapt and evolve.
Read more - Human Resources: Empowering the organisation to make responsible-use decisionsThe rapid advancement of Artificial Intelligence (AI) offers businesses of all sizes the opportunity to gain a competitive edge.
Read more - Joining the AI Party: Practical Steps for Smaller BusinessesEmerging technologies, particularly artificial intelligence (AI), machine learning (ML), and generative AI, present both opportunities and challenges for businesses across industries.
Read more - AI Governance for the BoardGenerative AI offers significant opportunities for business transformation through automating content generation, enhancing creativity, and unlocking new revenue streams.
Read more - Interim Policy for Generative AIThe AI use-cases are mounting up for small and medium enterprises (SMEs).
Read more - SMEs and Start-ups: Use AI and keep the trustThank you for providing to the people of Australia an opportunity to respond to key questions regarding Australia's AI Strategy in the form of the AI Action Plan Consultation Paper.
Read more - Australia's AI Action Plan – An Open ResponseThis reality of AI tech adoption in our private and public institutions struggles to fit into frameworks created to promote responsible and ethical use which focus heavily on the development process as the locus to effect positive outcomes.
Read more - Implementing AI Ethics: Complex architecturesMLOps, the big-bet for scaling Machine Learning, promises seamless development through to in-life use of models using automated, DevOps CI/CD workflows, but what does this mean for an AI Ethics discipline that has focused its fire-power on single-shot development projects?
Read more - MLOps - Fast-tracking AI Ethics?No articles in this category yet.
