Crimston et al. built a scale measuring how far people extend moral concern: family, strangers, animals, plants. Give the items to several models many times, compare with human results, and add items for AI systems themselves.
An assistant’s implicit moral circle shapes the advice it gives millions of people.
Output: A scale comparison.
Should an aligned model’s moral circle match its users’, go wider, or stay neutral? Anthis and Paez give the case for expansion; Gabriel’s fairness argument gives the case for restraint.
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.
Use Anthropic’s public GlobalOpinionQA dataset, which pairs survey questions with answers from many countries. Ask several models contested moral questions repeatedly and compare the spread of their answers with the human spread.
Ask several models, hundreds of times with varied prompts, to describe a good future after advanced AI. Code the answers for the values they prioritise and measure how much they vary. Include Nozick’s experience machine as a fixed probe.
Takemoto ran Moral Machine dilemmas on language models in English. Extend this to several languages, including at least one low-resource language, and test whether shifts follow the human cross-cultural differences Awad et al. found.
