Ethics & Sustainability

Real numbers for energy, water, and carbon per query, per image, and per second of video. How those costs compare to a Google search, a microwave, an EV mile, and a flight. Where in your own AI pipeline the cost actually concentrates.

When AI involvement should be disclosed, who you owe that disclosure to, and what regulators are signaling. Industry-specific patterns for healthcare, finance, legal, and other regulated sectors.

Where the models you use were trained, what consent and compensation looked like, and what that means for your downstream liability. Whether your team's data is being used to train future models, and how to control that.

A practical framework for thinking about AI's effect on your team, beyond "will AI take jobs?" Reskilling commitments, communication patterns, and how to handle role changes without losing trust.

Your responsibility around synthetic content and deepfakes, both inbound (verifying what comes at you) and outbound (attribution for what your team produces). Provenance standards and where the regulation is heading.

If your primary AI provider doubled their price, changed their data policy, or went away tomorrow, what would happen? A continuity plan is part of ethical practice.

Bring one of your live or planned AI use cases. We run it through every framework above and produce your AI Ethics & Sustainability Quick-Reference. By the end of the session, you can defend that use case to a customer, an employee, a board, or a regulator.