Show HN: Ethical demographic and location datasets for AI fairness (free pilot)
datalis.appI’ve been working on Datalis, a platform that supplies consent-verified, anonymised demographic and location datasets for AI teams.
These datasets are built for fairness benchmarking, bias testing, and model robustness evaluation — all aggregated, no PII, no scraping. Each panel combines regional demographic data (age, gender, income, education, occupation) with geographic context to help labs test model outputs across diverse populations.
We’ve just opened a free 30-day pilot for startups, researchers, and AI teams who want to run evaluations using real, ethical ground-truth data. CSV and Parquet formats are available (US + AU regions to start).
I’d love feedback from anyone working on model evaluation, bias detection, or Responsible AI — especially what additional fields or formats would make this most useful.
For context: Datalis started as a data-for-rewards app, but we pivoted into an aggregated dataset provider after seeing how much demand there was for ethical, representative data sources.
We’re not scraping or reselling user data, everything is opt-in and anonymised.
Happy to answer questions about how we collect, aggregate, or verify consent.