Make AI decisions you can defend — and be proud of

If you've found yourself in a meeting about an AI tool — a hiring platform, a monitoring system, a generative AI rollout — and felt the quiet discomfort of not quite knowing how to push back, even when something felt wrong, then I want to start by saying: that discomfort is the right instinct. You're not missing data. You're missing a framework.

That's exactly the gap this school was built to close. I've spent years at the intersection of moral philosophy and organisational decision-making, and what I've seen consistently is that smart, experienced leaders freeze when AI ethics conversations get hard — not because they don't care, but because nobody ever gave them the conceptual tools to reason through novel moral territory with confidence. They've been handed compliance checklists and called it ethics training. It isn't. It's the ethical equivalent of teaching people traffic laws and calling them drivers.

What genuine ethical reasoning looks like — the kind that holds up in a board room, in a regulatory review, in a conversation with an employee who feels surveilled or unfairly screened — is structured, multi-perspectival, and intellectually honest about trade-offs. It means being able to say: here is what a utilitarian analysis reveals, here is where Kantian duty ethics pushes back, here is what care ethics surfaces that neither of those frameworks saw, and here is the argument I'm prepared to defend and the risks I'm prepared to own. That's the skill this school builds, step by step, case study by case study.

I won't pretend this is easy material. Philosophy is precise, and precision takes effort. But I promise you this: every abstract concept we work through together is immediately put to work on a real scenario — the kind you've probably already encountered or will encounter soon. By the time we're done with the hiring algorithm case study, you won't just understand the difference between utilitarianism and Kantian deontology. You'll feel it, in the way that only comes from watching two frameworks reach completely different conclusions about the same decision.

The world is deploying AI faster than it is developing the wisdom to oversee it. That gap is exactly where your role matters most. Come and close it.