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When I read about AI in the news, and this includes social media, I am troubled by the way it is often The Need for AI Mythbusters These articles often don’t prove or demonstrate that AI is better, and neither The AI hype machine is definitely operating on all cylinders. Is that what we understand generative AI is - creative? Time for professionals to be AI Mythbusters!

As someone working in compliance during this wave of AI adoption, I've been thinking about how we approach Instead of implementing AI systems and then auditing their outputs, we might design systems that continuously A Different Relationship with AI What strikes me most about reflecting on Jidoka in the context of AI Instead of viewing AI as either fully autonomous or requiring constant supervision, Jidoka points toward For compliance professionals considering AI adoption, this perspective might be liberating.

The Current State of AI Artificial intelligence has indeed made remarkable progress in performing specific AI can analyze medical images, review legal documents, optimize engineering designs, or process geological This framework helps explain why AI excels at certain tasks while falling short of what is required for The Master and his Emmisary Machine-Like Intelligence (Left Hemisphere - apprehending) AI demonstrates their emphasis on standardization and procedural efficiency, have created natural opportunities for AI

So ask the hard questions: Does your AI know your obligations, your values, your promises? Their AI risk register looks like this 👇 — every line high probability, high severity, all red. They're obligation risks — the effect of AI uncertainty on commitments across every part of their organization How will they give stakeholders the assurance that they can stay between the lines — or will AI cause That's what AI assurance is for — engineered, operational guardrails that keep AI between the lines and

There's a growing urgency to establish regulations for artificial intelligence (AI). consider how existing regulations, standards, and professional oversight bodies can be leveraged for AI Adapting these frameworks to address AI-specific risks could be a quicker and more efficient approach critical infrastructure, public safety, and environmental sustainability, we can promote responsible AI It’s time we considered Lean AI Regulation.

AI safety is closely related to the broader field of responsible AI, which aims to ensure that AI systems AI assurance and AI safety are both important concepts in the field of artificial intelligence (AI), Impact on Compliance AI governance, AI assurance, and AI safety are critical components to support current AI Assurance : AI assurance refers to the process of testing and validating AI systems to ensure that AI Safety: AI safety refers specifically to ensuring that AI systems are safe and do not cause harm

Imagine your organization deploys an AI agent to process vendor invoices. This is the governance question that agentic AI is forcing organizations to confront. Much of the current AI governance discourse has inverted this. Agentic AI changes that calculation. Governing AI agents isn't something separate from what compliance professionals already do.

AI is pushing humans out of the loop. Train AI agents to participate in the governance loops that already exist. Most organizations are responding by drafting AI-specific policies, standing up AI ethics committees, This will fail — because it treats AI governance as separate from organizational governance. The governance architectures needed to govern AI agents are not new. They already exist.

Here are some of the things he said: Three facts about AI: AI has happened ( the genie is out of the What is AI (I have paraphrased this)? Before AI we told the computer how to do what we want - we trained the dog With generative AI we tell on the open internet Don’t teach AI to write code Don’t let AI prompt another AI What is the problem Sure, AI can dumb it done or AI-splain it to us so we feel better.

However, AI lacks the knowledge (and most likely always will) that comes from experience along with the

AI Systems: Learning Machines with Unpredictable Behaviour AI systems—especially those based on machine Why Engineering Matters for AI Because of these differences, AI systems need a new layer of discipline—AI Here are some key concepts behind engineering AI systems: 1.  Life-cycle Management AI development doesn’t end at deployment. It’s not enough to build AI systems that work. We need to build AI systems we can trust.

As artificial intelligence continues to advance, AI engineers face a practical challenge – how to build Current State of AI Uncertainty Current AI technologies, particularly advanced systems that use large What aspects of AI should receive attention: the technology itself, the models, the companies developing Rather than relying solely on static evidence, successful AI engineering requires ongoing observation How does your organization approach uncertainty in AI systems?