The PRISM alignment project collects diverse human feedback for AI conversations. Our difference lies in our specific mission to collect participatory, representative and individualised feedback to inform subjective and multicultural alignment norms.

In the early days of human feedback learning in AI systems, data was collected from a narrow and unrepresentative set of crowdworkers. This raises concerns about the potential impact of limited voices steering language models that are now used by hundreds of millions of people around the world.

To combat these concerns, we've collected diverse and disaggregated feedback from 1,500 participants born in 75 countries, including census-representative samples from the UK and the US. Our participants converse with over 20 LLMs in real-time, giving rich signals on each response.

With this data, we aim to provide insights into how humans differ in their interactions with large language models across different sociocultural contexts.