By use case · AI in hiring and employment
Hiring and employment is the use case regulated most often and most specifically: bias audits, notices to candidates, impact assessments, human review of decisions and record keeping appear in city, state, national and supranational rules. An organisation that uses AI anywhere in the employee lifecycle is a deployer in most of them.
Tell the candidate, test for disparate impact, keep a human able to change the outcome, and keep the records that show you did. The wording differs; the controls do not, which is why one bias-testing control below serves duties in several jurisdictions.
Who must run the audit, how often, whether it is published, and whether a candidate can opt out or request an alternative process. Those details are on each duty page with the article cited.
Sorted by how many of the duties on this page each control satisfies, so the ones worth building first are at the top. A control page lists every other duty it serves, in every jurisdiction.
| Control | Satisfies | Supports | Owner · frequency |
|---|---|---|---|
| AI interaction and use disclosure notices Process | 7 | 3 | Product owner · at launch and on material change |
| Decision explanation, human review and appeal route Process | 7 | 2 | Customer operations lead · once per ai system |
| AI risk assessment and lifecycle risk register Process | 4 | 2 | AI system owner · once per ai system |
| AI governance policy and accountability structure Policy | 4 | 2 | Executive sponsor for AI · annual |
| Technical documentation, model cards and instructions for use Process | 4 | 2 | Product or model owner · at launch and on material change |
| AI incident management and regulatory reporting Process | 4 | 1 | Incident coordinator · continuous |
| Human oversight design and override procedure Process | 3 | 5 | AI system owner · once per ai system |
| Privacy and data-protection controls for AI Process | 3 | 0 | Data protection officer · once per ai system |
| Post-deployment monitoring and drift detection Technical measure | 2 | 5 | AI system owner · continuous |
| Accuracy, robustness, fairness and security testing Technical measure | 2 | 5 | Quality or testing lead · at launch and on material change |
| AI impact and fundamental-rights impact assessment Process | 2 | 4 | AI system owner · once per ai system |
| Data governance and dataset documentation Process | 2 | 3 | Data governance lead · once per ai system |
| Prohibited and unacceptable-use screening gate Process | 2 | 0 | AI governance lead · once per ai system |
| AI system inventory and classification Process | 1 | 4 | AI governance lead · continuous |
| Model release and change-management gate Process | 1 | 2 | Release manager · at launch and on material change |
| Contractual allocation of AI duties across the supply chain Contractual term | 1 | 2 | Legal counsel · once per ai system |
| AI literacy and role-based training programme Training programme | 1 | 2 | Learning and development lead · annual |
| Quality management system for AI development and supply Policy | 1 | 1 | Quality lead · annual |
| Training-data provenance and copyright register Process | 0 | 2 | Model development lead · at launch and on material change |
| Automatic event logging and record retention Technical measure | 0 | 2 | Engineering lead · continuous |
| Vendor and third-party AI due diligence Process | 0 | 2 | Procurement or vendor risk lead · once per ai system |
| Conformity assessment, declaration and registration Process | 0 | 2 | Regulatory compliance lead · once per ai system |
Every published duty whose record names this audience. It is the recorded set, not every rule in the world; a jurisdiction missing here may simply not be mapped yet (open gaps).
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.
