Not Science Fiction, Real Practical Concerns

When people hear "AI ethics and safety," a lot of them picture science fiction: robots turning against their creators, a machine suddenly developing its own agenda. That's not what this track is about. The real concerns are much more grounded, and much more immediate.

The real questions look like this: whose data trained this system, and does it reflect everyone fairly? Who's actually affected when it gets something wrong? Who's responsible when that happens — the people who built it, the people who deployed it, or the person who used it? And can you even tell when you're interacting with AI in the first place?

These aren't hypothetical thought experiments — they're practical, ongoing questions about systems already being built and used today. This track treats them that way: concrete, answerable, and worth understanding clearly, not as a source of vague anxiety.

What is this track's actual focus, as opposed to the science-fiction image many people have of "AI safety"?

A core practical question this track asks is who is actually ____ when an AI system gets something wrong.

This track treats AI ethics and safety as vague, unanswerable anxieties rather than concrete questions.