From pilot to policy: A one-page “Responsible AI in HR” SOP

Hey Bosses!

Let’s turn your AI experiments into a grown-up policy you can ship this week. Below is a copy-paste, one-page “Responsible AI in HR” SOP designed for mid-size employers. It keeps you practical, compliant-minded, and manager-friendly—without turning your HR team into part-time data scientists. (Because “move fast and break things” should never apply to paychecks. 😅)

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From pilot to policy: A one-page “Responsible AI in HR” SOP

Purpose: Ensure HR uses of AI are lawful, fair, explainable, secure, and human-led.

Scope: Applies to any tool or workflow that assists with HR decisions (recruiting, screening, promotion, performance, compensation, scheduling, leave, investigations, safety). Includes vendor tools and internal automations.

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1) Roles & Decision Rights

• HR AI Owner (HRAIO): Approves use cases, maintains inventory, signs off on risk controls.

• Data Protection Lead (DPL): Reviews privacy, retention, security, and access.

• HRBP/Manager: Uses tools, documents human review, issues final decisions.

• Rule: No fully automated adverse actions. A qualified human must review and can override any AI output before it affects people (e.g., rejection, demotion, termination, pay change).

2) Allowed vs. Prohibited Data

• Allowed: Job-related, up-to-date data needed for the decision (JD requirements, skills, work samples, performance metrics mapped to role).

• Prohibited: Protected or sensitive attributes and proxies (e.g., race, religion, national origin, sex, pregnancy, disability, age, genetic info, union status), plus non-job-related web/social scraping.

• Minimize: Only collect/store what you actually need; default to “no PII export.”

3) AI Inventory & Risk Tiering

Maintain a living register with: tool name/version, vendor, purpose, data in/out, decision impact, and owner.

• Tier 1 (Advisory): Drafting job ads, first-pass Q&A, summarizing notes.

• Tier 2 (Assistive): Ranking candidates, performance insights, pay bands.

• Tier 3 (High-Impact): Anything that could change employment status, pay, or benefits.
  Controls scale with tier (see Sections 4–6).

4) Pre-Use Checks (gate before go-live)

For Tier 2–3 tools, HRAIO completes this checklist before any pilot becomes production:

• Job-relatedness: Inputs/outputs map to essential functions & validated criteria.

• Bias & validity: Test on recent, representative data; check group outcomes (e.g., selection rates). Document results & remediation if adverse impact appears.

• Explainability: Define what the model uses and how HR will explain outcomes to candidates/employees.

• Notice & transparency: Provide clear notice where required and offer a non-AI alternative if feasible.

• Vendor due diligence: Obtain security summary, data locations, sub-processors, retention, model update cadence, and bias/validation documentation.

• Data contract: No training on our data without written permission; purge on exit.

5) Human-in-the-Loop Review (every use)

• Two-step rule: (1) AI suggests; (2) qualified human evaluates with the JD/criteria in view.

• No blind trust: If the reviewer can’t articulate why the recommendation fits the criteria, do not use it.

• Appeal path: Provide a simple way for candidates/employees to request review and receive a human explanation.

6) Logging, Retention & Access

• Log: Tool used, version/date, inputs, outputs, human reviewer, decision, and rationale.

• Retention: Keep logs for the same period as hiring/EE records; auto-delete per schedule.

• Access: Role-based access only; MFA for admin panels; no personal accounts.

7) Monitoring & Drift (stay honest over time)

• Quarterly spot-checks (Tier 2) / Monthly (Tier 3): Re-test outcomes, calibration, and error rates; compare across demographics where lawful and available.

• Change control: Any major vendor/model update triggers a mini re-validation before continued use.

• Kill switch: HRAIO can pause a tool immediately upon anomalous results or incident.

8) Communications & Training

• Manager card: “AI suggests, humans decide. Stick to job-related criteria. Document rationale.”

• Candidate/employee FAQ: What AI we use, why, how it’s reviewed, how to appeal.

• Annual training: 30-minute refresher on this SOP, bias basics, and documentation.

9) Incident & Escalation

• What counts: Data leak, model hallucination affecting a decision, major disparity in outcomes, access breach, or unapproved AI use.

• 24-hour path: Notify HRAIO + DPL → pause tool if needed → investigate → remediate → notify affected parties where required → record lessons learned and update SOP.

10) Governance Snapshot (what we’ll publish internally)

• AI inventory (non-sensitive fields), contacts for questions/appeals, last validation date, links to policy & FAQ.

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7-Day Rollout Plan (so you actually ship this)

Day 1–2: Stand up the inventory (Google Sheet is fine), assign HRAIO & DPL, tag tiers.
Day 3: Run pre-use checks on Tier 2–3 tools; pause anything you can’t document yet.
Day 4: Draft the candidate/employee notice + FAQ; add an appeal email/form.
Day 5: Create the manager card and a 30-minute micro-training deck.
Day 6: Turn on logging in each tool/workflow; set retention & access controls.
Day 7: Announce the SOP, publish the inventory snapshot, and schedule the first monitoring dates.

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Copy-paste Policy Header (slot into your handbook/SharePoint)

Responsible AI in HR (One-Page SOP)
We use AI to improve speed and consistency, not to replace human judgment. AI outputs are advisory; a qualified human makes final decisions using job-related criteria. We maintain an AI inventory, tier tools by impact, run pre-use validation, monitor outcomes, protect privacy, and provide notice and appeal options. Questions or appeals: [HR-AI@yourcompany.com].

Lisa Smith, SPHR, SCP