FairGap®

NYC Local Law 144 · EU AI Act · CO SB21-169

Bias audits that hold up under cross-examination.

Independent algorithmic audits delivered as a notarized report — methodology spelled out in plain English, statistics in the appendix, defensible under audit by counsel and regulators.

What an audit covers

  1. Scope & population. Defining the impacted class precisely enough that the result is reproducible.
  2. Disparate-impact analysis. Selection rates by protected category, with confidence intervals and effect sizes.
  3. Provenance. Training-data lineage and feature-engineering decisions, traced to source.
  4. Remediation roadmap. What to change and what to monitor; tied to your deployment cadence, not ours.
  5. Public-facing summary. The plain-English version your candidates and HR team can read.