ladder offers the right level of analysis to further the discussions regarding responsible and safe AI At this level of analysis we are talking about AI Systems (i.e. engineered systems) not about systems that use AI technology (Embedded AI). across the socio-technical system, not just the AI technology. This is where professional AI engineers are most helpful and needed.

Artificial Intelligence (AI) is on a trajectory to revolutionize various industries, from healthcare Ethical AI in Action One notable example of integrating ethics into AI development is the concept of explainable AI (XAI). XAI emphasizes transparency and interpretability in AI systems, ensuring that decisions made by AI models integrity into AI initiatives.

This is one way to evaluate what is happening with AI.

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?

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 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.

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.