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# Audit-ready hiring data: building the data pipeline behind a defensible AI bias audit

Research paper · 18 pages · Published 2026-10-21 · By Holistic AI

A bias audit is only as strong as the data under it, and in 9 of 22 audits New York's Comptroller reviewed, the data was not adequately explained. This paper sets out a reference architecture, a canonical event schema, SQL quality checks and a retention matrix for employers using automated employment decision tools. A seeded simulation shows how missing demographics and a mid-period model update can move impact ratios without any change in the tool.

## Key numbers

- **9 of 22**: employer audits the Comptroller found did not adequately explain their data
- **0**: inferred or imputed demographics allowed in an LL144 bias audit
- **30 days**: to answer a written request for data type, source and retention policy
- **0.82 vs 0.71**: pooled impact ratio vs. the ratio after a model update, in our simulation

## What is inside

- A three zone reference architecture that keeps demographics away from decision makers
- A canonical event schema with LL144 use and retention for every field
- Four SQL data quality checks, including outcome related missingness and version drift
- A seeded simulation of how data gaps and model updates distort impact ratios
- A retention matrix, privacy controls, a vendor checklist and a 30/60/90 day roadmap

## Who it is for

- Data engineering and people analytics
- HRIS and ATS owners
- Privacy, legal and compliance teams

The full PDF is free. Request it with the form at https://www.nycbiasaudit.com/resources/audit-ready-hiring-data-pipeline.

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Holistic AI carries out independent Local Law 144 bias audits. What the service includes: https://www.nycbiasaudit.com/services.md. To commission a bias audit or ask a question, book a call at https://www.nycbiasaudit.com/demo or email we@holisticai.com.