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Working with AI over the past year opened my eyes to a systemic problem: AI systems are stuck in the It's a blindspot because AI systems literally cannot "see" emerging trends, innovations, or approaches This shift might be the future, but it barely exists in AI's world. The data doesn't show it enough, so the AI rarely mentions it. I've tried everything. Remember that AI shows what was common, not what's becoming common.

AI governance policies typically describe what organizations intend to do. Seven Elements of Operational AI Governance 1. AI-assisted operational controls where they add value. Periodic alignment with ISO 42001, NIST AI RMF, sector frameworks. Is your AI governance capable of ensuring and protecting Total Value?

AI now presents both the opportunity and the necessity to do so. Here is why. Organizations everywhere are adopting AI. I believe AI is now being used in ways that do exactly that. Much is said today about AI governance. AI will not be the end of Stakeholder Value.

How do we deliver safe AI? LLMs), as if AI exists in a vacuum. Example 2: AI Used Within the MOC Process Itself More subtly, when an organization uses AI to automate We now understand that different categories of AI systems present different risk profiles: Narrow AI AI, organizations can move beyond the hype and fear that too often characterize AI discussions.

This integration requires understanding that AI challenges span multiple dimensions. At its core, AI is simultaneously a technical, organizational, and social problem. As we build AI systems, we should continuously ask: where can AI best complement human work, and which We need breakthroughs in AI safety just as much as we need advances in AI capabilities. Through all of this, remember the simplest principle: be good with AI.

Canada's sovereign AI infrastructure is being built right now. Environmental scrutiny of AI energy consumption is intensifying. AI governance frameworks are formalizing.

this is important, it will not be enough to handle concerns associated with Artificial Intelligence (AI When decisions are made to proceed with a course of action (for example, to use or not use AI, in the That's why I believe our upcoming micro program, "Ethical Decision Making Involving AI" is so important You will learn how to make ethical choices supporting responsible and safe AI along with your other compliance

to "How do we enable AI to reliably meet obligations?" Consider an AI agent managing customer service operations. Unlike current AI systems that respond to prompts, agentic AI agents must serve as the reliable fulfillment AI Agents Enabling Human Promise Fulfillment Understanding AI agents through Promise Theory also helps Rather than asking "Is the AI agent following the rules?"

The rapid proliferation of AI across critical sectors—from healthcare diagnostics to financial trading Yet most current AI governance efforts remain trapped in conventional compliance paradigms: reactive It reflects a deeper challenge rooted in the nature of AI systems themselves. Unlike traditional engineered systems with predictable inputs and outputs, AI systems exhibit emergent What regulatory approaches have you seen that effectively address AI's unique challenges?

In this post we take a look at a new AI technology called ChatGPT from OpenAI. I am sure that AI will continue to develop and so will ChatGPT.

the concept of manufacturers' integrity and the significance of self-regulation with application for AI Principles of Ethical Use of AI for Ontario Countries across the world are actively looking at how best Regular peer review of AI systems is also important. These principles aim to ensure that AI systems respect the rule of law, human rights, civil liberties framework for responsible AI practice and use.

exposure resulting from adverse effects of AI. This week the Responsible AI Institute as part of their work (which I support) released an AI tool that AI systems are self-referencing. Clearly, AI is still in the experimental stage. You need to understand what AI can and cannot do.