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Graduate School USA

AI Governance and Compliance for Federal HR Course

via Graduate School USA

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Overview

Federal HR offices are increasingly using AI to screen resumes, draft position descriptions, summarize records, and respond to FOIA requests, but without governance, those tools create real legal, privacy, and EEO exposure.

This half-day course gives federal HR professionals a practical governance toolkit: how to document AI use in records systems, protect PII, evaluate selection tools for bias, run EEO impact assessments, and maintain audit trails that hold up under FOIA, IG, and OIG scrutiny. Aligned to OPM guidance and built around real federal HR scenarios.

Syllabus

Module 1

  • The HR-specific governance stack: OPM AI guidance, EEOC Title VII, NIST AI RMF, FOIA, Privacy Act, Federal Records Act.
  • What changed and what did not: post-2025 EO landscape, state laws that still reach federal contractors and shared-service tenants, and obligations that are unchanged.
  • Common HR AI use cases and their risk profile (resume screening, PD drafting, chat assistants, FOIA redaction, employee relations summarization).
  • Activity: Solo sort - given 8 HR AI use cases, place each on a low / medium / high-risk grid and justify.

Module 2

  • What counts as a federal record when AI is in the loop (prompts, outputs, model versions, system logs).
  • Privacy Act and PII boundaries: what you can and cannot paste into a GenAI tool; SORN implications.
  • FOIA exposure: producibility of prompts, outputs, and tool configurations; redaction of AI-generated content.
  • Retention schedules and capture: where the record lives when the tool is SaaS.
  • Lab: Records mapping worksheet - trace a sample HR-AI workflow and identify every record-generating step, retention obligation, and FOIA risk point.
  • Knowledge check: 3 questions.

Module 3

  • What "selection tool" means under the Uniform Guidelines on Employee Selection Procedures (UGESP).
  • Disparate impact basics: the 4/5ths rule, when it applies to AI-scored assessments and resume rankers.
  • Validating an AI selection tool: vendor evidence to demand (model cards, bias audits, validation studies).
  • Building an EEO impact assessment for an AI tool: data, method, thresholds, mitigations, sign-offs.
  • Audit trail anatomy: decision record, prompt / output capture, version pinning, human-in-the-loop documentation.
  • Case study: The Resume Ranker - a fictional agency pilots an AI resume ranker; participants review vendor materials, identify governance gaps, and recommend go / no-go with conditions.

Module 4

  • Walkthrough of a one-page AI Governance Checklist for HR (template provided).
  • Participants apply it to a real or representative tool from their own office.
  • Capstone activity (solo, optional share-out for ILT): Complete the checklist for an AI use case from your office; identify the top 3 gaps and an owner for each.
  • Resources, references, and where to go for help (OPM, agency CAIO, OGC, privacy / records officers).

Wrap and Q&A

  • Recap of the six learning objectives; pointers to OPM, EEOC, NIST, and agency resources.

Taught by

Bruce Gay, Steve Pesklo, Brian Simms, Clarissa J. Corbin, Natalya H. Bah, and Michiel Pruijssers

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