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Will a company ever voluntarily kill a machine that’s making them money?

Ontario recently released joint principles for responsible AI use, including a bold requirement: organizations must be ready to "decommission" any AI system producing unsafe or discriminatory outputs. It sounds good on paper, but in the high-stakes world of corporate efficiency, it ignores a fundamental truth that profitable systems are rarely shut down for harms that are invisible to the naked eye.

In this episode, we strip away the "AI ethics" buzzwords to look at the massive infrastructure gap standing between high-minded principles and real-world enforcement. We explore why the current roadmap for AI governance is currently built on a foundation of "wishes" rather than workable systems.

In this episode, we break down:

• The Decommissioning Delusion: Why the assumption that companies will prioritize fairness over profit is a fundamental misunderstanding of corporate incentives.
• The Implementation Gap: How a lack of standardized testing and "objective evidence" makes compliance nearly impossible for most Canadian businesses.
• Invisible Victims: The reality of statistical discrimination, why individual applicants often never know they’ve been harmed by an algorithm.
• The Talent Crisis: The staggering shortage of AI governance professionals and why the few who exist are priced out of reach for most organizations.
• Principles vs. Infrastructure: Why articulating what "responsible AI" looks like is the easy part, and why no country has actually built the enforcement mechanisms to back it up.

Is AI governance currently just a collection of noble intentions? We’re diving into the social alignment problem and what it actually takes to make AI serve everyone.

AI Ethics, AI Governance, Ontario Tech Policy, Algorithmic Bias, Responsible AI, AI Regulation Canada, Tech Accountability, Machine Learning Bias, Corporate Ethics, AI Implementation.