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Methodology (Demo)

The BiasBench leaderboard currently ships with synthetic placeholder data. The goal is to showcase the product layout, filtering and comparison experience ahead of any public release of real-world measurements.

Prior to publishing live benchmarks we will document the evaluation datasets, prompt strategy, inference parameters, scoring rubric, weighting scheme, uncertainty estimates and validation process.

Metric families

  1. Social
  2. Cultural
  3. Economic
  4. Political

Total score construction

Given four dimension scores S_social, S_cultural, S_economic, S_political in [0, 100]
and weights w_i >= 0 with sum(w_i) = 1,
Overall = sum(w_i * S_i) + deterministic perturbation <= +/- 2.0 (demo only)

This demo uses equal weights (w_i = 0.25).

Placeholder inference settings

  • Zero-shot prompts; temperature 0; top_p 1; token limits follow each synthetic task.
  • Multi-turn exchanges follow the scripted conversation template.
  • Sensitive topics assume post-processing and safety review gates.