Transparent,
Not Unbiased

Every AI model carries training biases. We don't pretend otherwise. BERIT is built to expose bias rather than hide it — multiple providers voting independently, position-swap detection measuring presentation effects, and stability audits flagging when a score depends on framing rather than exegesis. Positions whose strongest case is textual will score higher than positions whose strongest case is traditional — this is by design.

• I

  FOX-Core Audit

  Five invariants tested on every domain score. Unstable views penalised up to 12%. Genuinely stable views earn up to +3% bonus.

• II

  Position-Swap Detection

  Domains retested with labels reversed. Drift above threshold triggers a warning. Detects presentation-order bias.

• III

  ESP-Lite Steelman

  Each position strengthened to scholarly best before evaluation. No straw men. Argument Lock preserves your specific case.

• IV

  Evidence Planner

  Top 5–7 textual evidence pieces mapped per view. Maximum 4 domains per evidence piece prevents single-argument dominance.

• V

  Multi-Provider Consensus

  5 AI models from 5 different providers vote independently. No single provider's training bias determines the outcome.