The MIT AI Risk Repository organises over a thousand AI risks into a shared taxonomy. Use it to classify 20 recent news stories, then look at what the pattern tells you.
Shared categories let researchers and policymakers compare notes. Applying a taxonomy to real events also reveals where it fits badly.
Output: A spreadsheet of 20 classified stories and a short analysis.
Explore the MIT AI Risk Repository and read how its causal and domain taxonomies work.
Gather recent AI news stories involving harm or near-misses. Mix topics: jobs, misinformation, security, safety.
Put each story into the taxonomy. Note when it is ambiguous or fits nowhere.
Which domains dominate the news? Which risks are missing from coverage entirely?
Share your spreadsheet and three findings, including one place the taxonomy struggled.
All steps done
Share what you made, then try another project. The PDF version has room for notes.
Pick one case from the AI Incident Database and write it up like an accident investigator: what happened, who was harmed, why, and what rule or practice would have prevented it.
Summarise how the EU sorts AI systems by risk (banned, high-risk, transparency duties, minimal) with one everyday example for each level, plus what changes for general-purpose models.
Find out which laws, strategies and guidance apply to AI in your country, and fit them onto one page.
Gather 6–12 friends, give each a stakeholder role (lab, regulator, journalist, public) and play out how society responds to a fictional AI incident.
