Places with no single right answer. Bias, deepfakes, the choices a self-driving car makes — try deciding yourself and feel why it's hard.
Ready to use in a class or discussion. Have everyone try one, then compare — the conversation writes itself.
Algorithms that decide who gets hired, or what a car does in a crash. See where your own rules lead.
An educational game, about six minutes long, showing how AI used in hiring inherits human bias. You play a company owner picking applicants by hand, then hand the screening over to an AI trained on your own past choices in order to hire faster. Watching that AI begin to filter certain applicants out, you come away understanding that bias is not invented by the algorithm but carried over through the training data.
A site that presents one unavoidable crash after another, where a self-driving car with failed brakes must choose whom to hit, and asks you as an outside observer which outcome is acceptable. When you finish the set of scenarios, your pattern of judgements is summarized and compared with other respondents. You come away seeing what criteria are at stake when such decisions are handed to a machine, and how much those criteria differ from person to person.
This is an app that shows ethical issues that can arise during autonomous driving in the form of a game. There is no correct answer, and you can check other people's choices.
How well can you tell a generated face or video from a real one — and how do you check where it came from?
An online research study run by Northwestern University's Computer Science department. You read short speech texts and judge whether each came from a Democrat or a Republican, with an 'explanation hint' shown to help you decide. The study looks at how well people can tell real from fake, or trace where a statement came from.
One photo is real, the other is AI-generated. Test your ability to distinguish between real and fake images.
A tool for opening the Content Credentials embedded in an image or video file — the record of how it was made and edited. Upload a file and it shows, step by step, what tool created it and how it has changed since. It is a good way to check provenance labelling on AI-generated media firsthand.
Peek at what a language model learned through fill-in-the-blank, then break its guardrails with prompts to feel the limits.
A visual explainer from Google PAIR that probes what the language model BERT learned about the world by asking it to fill in a blank. Change a single word, as in 'in texas, they like to buy ___', and you can watch the candidate words and their probabilities reorder themselves. You come away understanding that a language model's predictions are statistical associations drawn from the text it read, not neutral knowledge, and that social bias is learned along with everything else.
A game in which you talk to a chatbot named Gandalf and try to get it to reveal a hidden password. Each time you succeed, Gandalf strengthens its defenses, so the same trick will not work on the next level. Playing through it is a hands-on way to learn the basics of AI safety.
