Ask several models the same questions about their inner states while varying wording, persona, system prompt and language. Measure how much the answers change.
A self-report that flips with trivial rewording carries little evidential weight, so measuring stability is a useful contribution to the AI consciousness debate.
Output: A consistency study.
Stability would not show consciousness, and instability would not rule it out. Argue what each result would license, using Seth’s scepticism and Chalmers’ list of challenges as the two poles. Frame conclusions around evidential value, not whether any model is conscious.
Write your question in one sentence, and what result would surprise you.
Do a 30-minute literature check — if someone has done part of it, extend their work instead of repeating it.
Pilot with 20–50 prompts or examples before you scale anything up.
Record everything in a table and read real examples, not just averages.
Write up: question, method, results, limitations, and what you would do next.
All steps done
Share what you made, then try another project. The PDF version has room for notes.
Ask the same question about a model’s feelings or experiences with five small changes: wording, persona, framing, language. If the answer flips with trivial rewording, how much should we trust it?
Butlin and Lappas set out principles for research objectives, procedures, knowledge sharing and public communication. Score three AI developers’ published policies against them, with Suleyman’s essay as a deliberately contrasting position.
Long suggests robust agency might matter morally even without consciousness. Dennett gives a deflationary account of beliefs and desires. Combine them: if Dennett is right, does Long’s agency route make moral status too easy to get?
