If the UK’s plan doesn’t identify risk levels and only focuses on outcomes, they wouldn't know which systems are dangerous. It feels like they’re assuming outcome-based regulation will work just as well without proving it. I would say that shows hasty generalization because skipping risk evaluation could mean missing red flags early on. The author should explain why this approach is still reliable or give an example of it actually working.

It was shocking to me that most countries that were surveyed did not have specific laws for AI accountability in 2020. This shows that even though people have been talking about regulating AI for a while, actual rules haven’t caught up. The paragraph says that having laws automatically makes AI safer, but it doesn’t explain how these rules would actually be enforced. And I feel like there is some false cause in this because it just assumes that passing laws will simply make AI safer. It is also important to see what countries created good regulations or showed cases where the lack of laws caused real problems.

A ChatGPT bug in March 2023 leaked some users’ chat info to others which is kind of scary, but it makes sense since the software isn’t perfect. The paragraph makes it seem like it’s over and done with, but stuff like this could happen again. It would be better if they explained how OpenAI actually fixed it or what they’re doing to prevent future leaks, but it shows that AI isn’t totally risk-free, and people should still be careful with their data.

Using people’s personal data without permission for AI could break privacy laws. That makes sense cause privacy laws are supposed to protect people, and using data for something totally different seems wrong. But the paragraph kind of assumes all countries handle this the same way, which isn’t true and makes the point weaker. It would be stronger if it gave like examples of real laws or cases where AI misused personal data.

AI tools being reliable if they are verified is what the author is saying here. Verification could be an easy step, but chatbots don’t always show sources. We are not told though how verification could be done, so this is missing that point. And the author could make the argument stronger by showing examples of AI mistakes and explaining verification steps.

The argument the author makes here is reasonable and sound because there are still errors that can be made using AI. There is a common belief that AI is super trusting when it comes to its information, and that is because AI is not a person, so we think it only gives correct and accurate information. Since this is not true, there is still risk associated with AI usage, since its information can be misleading.