How your score is protected

1. 1. Harsh and generous raters are evened out

   Some people rate everyone a 3; others hand out 5s. Before anything is scored, each rater's pattern is statistically normalized against the organization (once they've rated enough for a pattern to exist), so being rated by a tough grader doesn't cost you and an easy one doesn't inflate you.

2. 2. No single person controls your score

   Different perspectives are weighted deliberately — peers as a group carry the most weight, and a perspective with too few raters (for example, a lone peer) is excluded entirely, with its weight redistributed to the perspectives that have enough voices.

3. 3. Too little data means no score — not a bad score

   Any behavior with fewer than 3 raters is suppressed: it shows as 'insufficient data' rather than a number. We never publish a score that one person's opinion could have produced, which also means no one can reverse-engineer who said what.

4. 4. Inflated self-ratings are automatically discounted

   If a self-rating is far above what everyone else consistently observed, the self-rating's weight is halved for that behavior. Honest self-assessment is never penalized — only statistically implausible gaps are.

5. 5. Every number carries its confidence

   Scores display with a confidence level (and, in 360 mode, a confidence interval) that reflects how many people contributed. Fewer raters = visibly wider uncertainty. The system never reports more confidence on less data.

6. 6. The AI cannot invent a statistic

   Every AI narrative is generated from — and validated against — the same computed numbers you can see. Each claim carries its evidence chips, and any AI-rewritten text is checked so it contains no number that isn't grounded in the cited data. If validation fails, the deterministic version is shown instead.

7. 7. Risk flags are reviewed by people, resolved by evidence

   A behavioral risk flag is raised by the scoring engine, acknowledged by a leader (with the action taken on record), and marked recovered only when a later cycle's scores show the behavior actually improved — never by someone clicking it away.

These protections are implemented in the scoring engine itself (not policy documents) and apply identically to everyone in your organization.