Investigate a real AI incident
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.
If an employee at an AI company sees something dangerous, what protects them if they speak up? Compare protections across two or three jurisdictions.
Insiders are often the first to see problems. The MINT bibliography treats incident reporting and whistleblowing as a core governance topic.
Output: A comparison and short recommendations.
Write your question in one sentence.
Spend 30 minutes finding what has already been written about it.
Collect evidence in a simple table: claim, source, how strong.
Draft 1–2 pages: answer first, then reasons, then uncertainties.
Ask one person to poke holes in it, revise, and share.
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.
Explain how liability and insurance could shape AI companies’ behaviour, using one realistic harm scenario to make it concrete.
Which countries require AI developers to disclose safety testing, incidents or training details? Build a comparative tracker of transparency and disclosure rules.