AI, Safety and What Goes Wrong

A working guide to how AI systems fail and what to do about it. Where bias actually comes from and why deleting the sensitive column does not remove it. Why a fabricated answer sounds identical to a true one, and how to verify at a cost that matches the stakes. What you hand over when you paste something into a chat box, and which country's law then applies. Deepfakes, including who is really targeted and why spotting artefacts is a losing defence. What "high risk" means in the EU AI Act, and how India's IT Rules and DPDP Act take a different route. Who owns AI output — often nobody. The environmental cost with real figures at both ends. And how to use AI as a student without arriving at the exam with skills you never built. Honest about what is contested, specific about what is known.

1. 1What actually goes wrong7 minAI failures are human decisions with software in between; ask who chose, not what the machine wanted.
2. 2Where bias comes from9 minYou cannot delete bias by deleting the sensitive field; other columns rebuild it and you lose the ability to measure.
3. 3Hallucination, and how to check9 minA model produces wrong answers in exactly the same confident voice as right ones; check consequences, not tone.
4. 4What you hand over when you paste8 minPasting is disclosure to a company under some country's law; sort by who is harmed if it leaks.
5. 5Deepfakes and synthetic media8 minVerify synthetic media by origin and a second channel, not by squinting at pixels for artefacts.
6. 6When AI decides about people10 minHigh risk is defined by what a system decides about people, not by how advanced the technology is.
7. 7Who owns it, and what it costs9 minAI output often has no copyright owner, and the environmental question is about data centres, not your chat window.
8. 8Using AI as a student without cheating yourself8 minAI helps learning when it removes friction around the thinking, and harms it when it removes the thinking.