AI hiring review starts with use-case inventory, jurisdiction scope, evidence ownership, and the distinction between Tallin-proved data and customer-attested artifacts.
Start with the employment decision
Do not begin with the model name. Begin with the decision the AI touches: recruiting, resume screening, candidate ranking, interview analysis, promotion, or other employment opportunity. That use case determines which obligations are worth reviewing.
Separate jurisdictions from policy preference
AI hiring obligations can depend on location, candidate population, employee population, and the role of the tool in the decision. Track New York City, Illinois, Colorado, and federal employment-law review separately instead of putting every rule into one generic policy row.
Use three evidence buckets
Tallin can auto-prove inventory, usage, gateway routes, identity context, and discovery signals. The customer usually attests documents that live outside Tallin, such as bias audits, notices, DPIAs, and counsel review. Anything required but missing should remain an open gap.
Key takeaways
- 01Name the employment decision before naming the AI model.
- 02Track jurisdictions explicitly rather than using a freeform policy note.
- 03Separate Tallin-proved evidence from customer-attested evidence.
- 04Keep missing audits, notices, and sign-offs visible as open gaps.