AI Ethics & Risks: Who Wins, Who Loses, and What Can Go Wrong
Beyond the hype: the harms, power games, and hard questions AI creates in the real world
AI isn’t neutral. It’s built by specific people, in specific companies, with specific incentives, on specific data.
And when those systems get things wrong, it’s not abstract—it’s:
Someone denied a loan or job because a model didn’t like their profile
A community over-policed because an algorithm predicted “risk”
A worker monitored, scored, and nudged by systems they had no say in
A false image, fake quote, or deepfake video spreading faster than the correction ever will
This section is where we stop treating AI like a shiny productivity hack and start asking:
Who does this help, who does it hurt, and who gets to decide that tradeoff?
Use this section to understand:
The main ethical issues people should be worried about
How AI systems go wrong across the pipeline—from data to deployment
How AI shifts power between companies, governments, workers, and the public
What you can actually do as a user, worker, or decision-maker