AI Delegation Audit: Draft a Hiring Process Risk Memo That Flags Where Your Team Doesn't Understand the AI Tools They're Using

The task

HR leaders — heads of TA, people ops directors, HRBPs who own hiring compliance — need to periodically audit where their recruiters and hiring managers have quietly handed decisions to AI tools they can't explain. This piece turns that audit into a two-prompt exercise: describe your current workflow, get back a structured risk memo you can bring to Legal and your CHRO. Do this before your next quarterly compliance review, or after any new sourcing/screening tool gets added to the stack.

Before AI

The manual version is a week of interviews. You sit down with each recruiter, ask what tools they use at each stage, ask them to explain how the scoring works (they usually can't), then cross-reference with vendor documentation, then draft a memo mapping each gap to a legal exposure category. Most HR teams skip it — which is exactly why the EEOC states that an employer may be responsible under Title VII if a selection procedure discriminates, even if the test was developed by an outside vendor. The audit doesn't get done until a candidate complaint forces it.

The workflow

Step 1 — Describe your current AI-assisted hiring stack in plain language. Write out every stage of your funnel and every tool touching it. Don't sanitize. Include the things recruiters are doing that aren't officially sanctioned (ChatGPT for JD rewrites, LinkedIn's AI matching, resume screeners nobody remembers approving). Paste that into the first prompt.

You are an HR compliance analyst specializing in AI-assisted hiring under EEOC and state-level AI hiring laws (NYC Local Law 144, Illinois AI Video Interview Act, Colorado AI Act). I will give you a description of a company's current hiring workflow, including which AI or algorithmic tools touch each stage.

Your job in this step: produce a DELEGATION MAP. For each stage of the funnel, output a table row with these columns:
1. Stage (e.g., sourcing, JD drafting, resume screening, assessment, interview scoring, offer)
2. Tool(s) in use
3. What decision or output the tool produces
4. Who on the team relies on that output
5. Delegation level: ADVISORY (human still decides), SHADOW (human rubber-stamps), or AUTONOMOUS (tool decides, human never sees candidates it rejected)
6. Explainability status: CAN EXPLAIN / CANNOT EXPLAIN / VENDOR BLACK BOX — based on whether the described team demonstrates working knowledge of how the tool scores

Be strict about SHADOW vs ADVISORY. If the description says a recruiter "reviews" scores but there's no evidence they override them, mark it SHADOW. If nobody on the team can articulate the scoring logic, mark explainability CANNOT EXPLAIN even if the vendor publishes docs.

End with a short "Signals of concern" list — direct quotes or paraphrases from the input that suggest delegation without understanding.

Here is the workflow description:

Company: mid-size SaaS, ~800 employees, hiring ~15 roles/month across engineering, sales, and CS.
TA team: 4 recruiters, 1 sourcer, 1 TA ops person, reporting to me (Director of Talent).

Sourcing: Sourcer uses LinkedIn Recruiter with the AI "Recommended Matches" feature turned on. She says it "just surfaces good people" — when I asked what signals it uses, she said she wasn't sure but the results "feel right." Also using SeekOut for diversity sourcing; team uses its AI-generated candidate summaries verbatim in outreach.

JD drafting: Hiring managers draft in Google Docs, then paste into ChatGPT with a prompt like "make this more inclusive and appealing." Nobody reviews the ChatGPT output against our approved language bank. One recruiter mentioned ChatGPT sometimes adds phrases like "rockstar" or "ninja" that we've explicitly banned.

Resume screening: We use Eightfold for initial resume ranking. Recruiters see a 1-5 match score and typically only review candidates scored 4 or 5. Nobody on the team can explain what drives the score. Vendor says it's based on "skills adjacency and career trajectory." We have not run an adverse impact analysis in the 14 months since rollout.

Assessments: Engineering uses CodeSignal (has its own scoring). Sales uses a Bryq cognitive+personality assessment — recruiters told me they trust the "culture fit" score and often deprioritize candidates below a 70.

Interview scheduling: GoodTime, no AI decisioning.

Interview intelligence: We turned on Metaview's AI interview notes and "candidate scorecards" last quarter. Two hiring managers have started pasting the AI summary directly into the debrief without watching the recording.

Offer stage: Comp recommendations come from a Payfactors model. TA ops person accepts the recommended range 95% of the time.

Step 2 — Turn the delegation map into a risk memo with recommended human checkpoints. This is the artifact you actually hand to Legal and your CHRO. Ground it in current guidance — see the EEOC's technical assistance on assessing adverse impact in AI selection procedures and NYC's Local Law 144 on automated employment decision tools — but the memo should read like an internal HR document, not a legal brief.

Now take the delegation map you just produced and draft a RISK MEMO addressed from the Director of Talent to the CHRO and General Counsel. Structure it exactly like this:

**MEMO: AI Delegation Risk Assessment — [Current Quarter]**

1. **Executive summary** (3-4 sentences). What percentage of our funnel involves AI decisioning? Where is the biggest exposure? Do NOT hedge — name the top risk.

2. **Highest-exposure findings** — up to 5, ranked. For each:
   - The finding in one sentence
   - The legal or regulatory frame it sits under (Title VII disparate impact, ADA, state AEDT laws, GDPR Article 22 if relevant, etc.) — be specific about WHY that frame applies given the facts
   - Concrete evidence from the workflow description
   - The specific human checkpoint that is missing

3. **Explainability gaps** — list every tool where the team cannot articulate how outputs are generated. For each, specify what the team would need to be able to explain (in plain language) before continuing to use the tool.

4. **Recommended human checkpoints** — a numbered list of specific, testable process changes. Each must name (a) the stage, (b) who is accountable, (c) what they must review or override, and (d) what evidence gets logged. Avoid vague language like "provide oversight."

5. **What to ask each vendor** — a bulleted list of documentation requests, keyed to each vendor named in the workflow. Include adverse impact testing results, 4/5ths rule data if applicable, and model card / training data disclosures.

6. **What NOT to do** — 2-3 common overreactions to avoid (e.g., "rip out the tool tomorrow" when a documented remediation plan is defensible).

Tone: direct, non-defensive, written for executives who are busy. No throat-clearing. No "it is important to note." If a finding is speculative because the input didn't say, mark it (ASSUMPTION) and state what you'd need to confirm.

Gotchas

• Garbage in, garbage memo. If your Step 1 description sanitizes what's actually happening ("recruiters review all scores carefully"), the memo will miss the real exposure. Interview your team candidly first, or have a trusted lieutenant write the description.
• The model will over-index on named laws. It knows Title VII and NYC 144. It's weaker on newer state laws (Colorado, Illinois amendments) and often misses sector-specific rules — federal contractor OFCCP obligations, for example. Have Legal review the regulatory citations before circulating.
• "CANNOT EXPLAIN" is not the same as "illegal." The memo will flag black-box tools even where use is lawful. Read the checkpoint recommendations as risk-reduction, not compliance minimums.
• Vendor docs change. A tool marked BLACK BOX today may have published a model card last month. Re-run the audit when vendors ship major releases, not just quarterly.
• This is not a bias audit. It identifies where you should do one. An actual adverse impact analysis needs applicant-flow data and, in some jurisdictions, an independent auditor.

Time saved

A manual delegation audit — interviewing the team, mapping tools to stages, drafting the memo, cross-referencing regs — runs roughly 5-6 hours of a TA director's time per cycle. This workflow compresses the mapping and drafting into about 45 minutes, leaving the human work (candid team interviews, Legal review, vendor follow-up) intact. Conservative estimate: ~4 hours saved per audit cycle, run quarterly. The larger unmeasured value is that the audit actually gets done, given how often it doesn't.

What we got when we ran it

We executed every prompt above, in order, against the sample input (model: claude-sonnet-4-6). Outputs below are real and unedited apart from length truncation.

Step 1 output

# Delegation Map — AI-Assisted Hiring Workflow Audit Company: Mid-size SaaS (~800 employees) | TA Team: 4 recruiters, 1 sourcer, 1 TA ops, 1 Director --- ## Stage-by-Stage Delegation Map | # | Stage | Tool(s) in Use | What the Tool Produces | Who Relies on the Output | Delegation Level | Explainability Status | |---|-------|---------------|----------------------|--------------------------|------------------|-----------------------| | 1 | Sourcing | LinkedIn Recruiter (AI Recommended Matches); SeekOut (AI candidate summaries) | Ranked candidate pool surfaced for outreach; pre-written candidate summary blurbs | Sourcer (LinkedIn); Full TA team (SeekOut summaries used verbatim in outreach) | SHADOW — Sourcer accepts recommendations without interrogating signals; team repurposes AI summaries without review | VENDOR BLACK BOX (LinkedIn); CANNOT EXPLAIN (SeekOut — team has not evaluated accuracy or bias in summaries, uses them verbatim) | | 2 | JD Drafting | ChatGPT (ad hoc, recruiter-prompted) | Revised job description copy presented as "more inclusive" | Hiring managers and recruiters who treat the output as final | SHADOW — Output is not checked against approved language bank; banned terms have appeared undetected | CANNOT EXPLAIN — No one reviews against a standard; team cannot predict or audit what language the model will introduce | | 3 | Resume Screening | Eightfold (AI resume ranking) | 1–5 match score per candidate | Recruiters — who review only 4s and 5s, making the 1–3 cohort effectively invisible | AUTONOMOUS — Candidates scored 1–3 are never seen by a human; rejection is tool-decided with no human review |

…output truncated at 250 words.

Step 2 output

MEMO: AI Delegation Risk Assessment — Q3 2025 TO: [CHRO Name], Chief Human Resources Officer; [GC Name], General Counsel FROM: [Director of Talent] DATE: [Current Date] CLASSIFICATION: Privileged and Confidential — Attorney-Client Communication (recommend GC review prior to distribution) --- ## 1. Executive Summary Seven of eight active hiring stages now involve AI-generated outputs that directly shape who advances; in at least three of those stages, human review is nominal or absent entirely. Our biggest legal exposure is Eightfold's resume screening: we are operating what almost certainly qualifies as an Automated Employment Decision Tool under NYC Local Law 144, we have never run a bias audit, we have not notified candidates, and we have been in production for 14 months. That is not a future risk — it is a current violation if any candidate screened was based in New York City. Secondary to that, Bryq's undefined "culture fit" threshold and Payfactors' near-fully-delegated compensation recommendations create compounding disparate impact exposure under Title VII that we cannot defend because we cannot explain the underlying models. --- ## 2. Highest-Exposure Findings ### Finding 1 — Eightfold Resume Screening: Likely Active Violation of NYC Local Law 144 The finding: We are using an AI tool to filter candidates at scale, with no human review of anyone scored below a 4, no bias audit, and no candidate notification — for 14 consecutive months. Legal frame: NYC Local Law 144 requires employers using an AEDT in hiring decisions affecting NYC-based candidates to (a) commission an

…output truncated at 250 words.