Rutgers AI Ethics Lab's glossary entry on the ELIZA effect: the human tendency to read genuine understanding into a system that is only matching surface patterns, named after Joseph Weizenbaum's 1964 chatbot. Explains why it matters legally and ethically — people disclose more, attach emotionally, and decide based on false assumptions — and argues designs must not be built to imply empathy or consciousness.
Why I recommend it: Free, short, and from a university lab rather than a vendor — a good citation when you need a defensible definition. It is a working glossary, so entries carry a last-updated date and name no individual author; for the original argument, the further-reading link to Weizenbaum's 1976 book is free on the Internet Archive.
Free question-and-answer site explaining AI risk arguments in plain language, founded by Rob Miles and maintained by volunteers. Answers are organised as linked questions from beginner to advanced, covering how AI is advancing, why systems may pursue goals, alignment research and AI governance. Includes Stampy, a chatbot that answers AI safety questions with sources. Open source on GitHub; run as a project of Ashgro Inc, a US 501(c)(3) charity.
Why I recommend it: The clearest free place to find out what people mean when they talk about AI risk, written so you can follow it without a technical background. Be clear about what it is: this is advocacy, not a neutral survey of the debate. The homepage opens with 'it could lead to human extinction', and the whole site is built by people who already hold that view, so you will get their strongest arguments rather than the strongest objections to them. Their own chatbot warns it can be inaccurate — check its sources before repeating anything. Read it to understand the case, then read the critics of it, and pair it with the AI Basics page here for the numbers.
