Research map / Large language models / AI and society
Why AI Is WEIRD and Shouldn't Be This Way: Towards AI for Everyone, with Everyone, by Everyone
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A position paper arguing AI should be inclusive across the pipeline (data, annotation, models, evaluation, incentives, people) to serve everyone, with everyone, and by everyone.
Problem
Current AI development under-represents much of the world's cultures, languages, and communities in data, models, evaluations, incentives, and developer workforce, leading to biased performance and harms.
Approach
Frames the AI pipeline's WEIRD-like coverage gaps as a set of interlinked problems and offers recommendations along data, models, evaluation, incentives, and people, summarized as "for everyone, with everyone, by everyone."
Surprising findings
- Amazon Mechanical Turk annotators are reported as 75% from the United States and 16% from India, per a cited demographic report.
Limitations
- No empirical experiments; recommendations are largely qualitative and drawn from cited literature.
- Authors acknowledge WEIRD does not perfectly map onto AI coverage dimensions.
Experimental setup
Builds on: WEIRD datasheets for datasets pluralistic alignment
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