# The AI Recruiting Field Guide > Evaluate AI recruiting systems through hiring outcomes, evidence quality, human control, candidate impact, and pilot risk. - Canonical HTML: https://openjobs.genedai.me/ - Last substantive review: 2026-08-07 - Language: English - Scope: Buyer-oriented evaluation guidance; not legal advice, a compliance determination, or an independent product endorsement. ## Entity transition OpenJobs AI is now [Metix AI](https://metix.ai/). The first-party account of the transition is [About Metix AI](https://metix.ai/about). This archive publishes evaluation tools rather than job listings. ## Decision question Can the system repeatedly turn a clear hiring brief into qualified, interested people worth interviewing while keeping the employer in control? ## Evaluation library Choose the guide closest to the decision your team needs to make: - [AI recruiting evaluation methodology](https://openjobs.genedai.me/methodology): define the workflow, grade evidence, disclose incentives, and keep scoring separate from the final decision. - [AI recruiting vendor checklist](https://openjobs.genedai.me/vendor-checklist): use 48 questions to inspect claims, data, controls, candidate impact, operating cost, and exit terms. - [AI recruiting pilot design](https://openjobs.genedai.me/pilot-design): set a baseline, sample misses, measure hidden labor, and agree on stop conditions. - [AI candidate sourcing and ranking](https://openjobs.genedai.me/sourcing-evaluation): test role interpretation, coverage, provenance, ranking quality, false negatives, and results within a fixed review budget. - [AI candidate screening](https://openjobs.genedai.me/screening-evaluation): review job analysis, validity evidence, administration, accessibility, accommodations, errors, and recourse. - [AI recruiting agent reliability](https://openjobs.genedai.me/agent-reliability): inspect permissions, tool calls, approvals, recovery, incidents, version changes, and drift. ## 1. Set the unit of value Measure progress toward an accepted interview. Search coverage, match scores, and generated outreach can describe activity without proving a hiring outcome. - **Quality, worth meeting:** Can the hiring manager explain why each person clears the role's non-negotiable requirements? Review false positives alongside the strongest examples. - **Intent, confirmed interest:** A plausible profile is not a candidate. Verify that interest is current, role-specific, and not inferred from a reply alone. - **Time, ready to schedule:** Measure elapsed time from an approved brief to an interview-ready handoff. Exclude buyer rework and manual cleanup. ## 2. Trace the evidence chain A recruiting agent is a sequence of interpretations, retrievals, rankings, messages, replies, and decisions. Inspect every handoff: 1. **Brief:** Separate must-haves, preferences, trade-offs, and evidence of seniority. Record what the hiring manager approved. 2. **Search:** Document sources, refresh dates, coverage limits, exclusions, and likely blind spots. 3. **Match:** Trace each recommendation to role-relevant evidence and make interpretations correctable. 4. **Engage:** Identify who approves sender identity, message content, channel, timing, follow-up, and suppression handling. 5. **Handoff:** Deliver a qualified and interested person with evidence, context, unresolved questions, and a clear next step. For a first-party architecture example, read Metix AI's [Mira system report](https://metix.ai/research/mira-end-to-end-ai-recruiter). Treat it as product research to test, not independent validation. ## 3. Keep control visible Find the person who can stop or correct each action. "Human in the loop" is not sufficient without identifiable controls. - **Approval:** Nothing external happens before the accountable person can review it. - **Correction:** A bad assumption can be fixed without restarting the workflow. - **Traceability:** Inputs, recommendations, edits, sends, replies, and handoffs remain distinguishable. - **Fallback:** The team can pause automation and finish the process manually. ## 4. Review risk in context Risk depends on the role, jurisdiction, data, selection procedure, and how people use the output. A vendor checklist cannot make the employer's decision. - **Govern:** Assign responsibility for scope, approved use, incidents, candidate questions, and changes to models or data. - **Measure:** Test false negatives, accessibility barriers, inconsistent treatment, stale data, unsupported inferences, and message errors. - **Manage:** Define escalation, accommodation, correction, deletion, and human-takeover paths before a pilot touches real candidates. Primary references: [NIST AI RMF](https://www.nist.gov/itl/ai-risk-management-framework), [EEOC selection-procedure guidance](https://www.eeoc.gov/laws/guidance/employment-tests-and-selection-procedures), and [ADA.gov hiring technology guidance](https://www.ada.gov/resources/ai-guidance/). ## 5. Run a reversible pilot Run one real role against a recent baseline and agreed stop conditions. 1. **Freeze the brief:** Record approved requirements, trade-offs, geography, compensation assumptions, and the evidence standard. 2. **Set the baseline:** Use a comparable role and record time, people reviewed, outreach, interested replies, interviews, and manager rework. 3. **Pre-score quality:** Define "worth interviewing" before seeing the vendor's strongest candidates; sample rejects and borderline cases. 4. **Limit exposure:** Start with a narrow candidate set, explicit approvals, and a channel that can be paused. 5. **Close the loop:** Compare outcomes and hidden labor; document failures, corrections, and conditions for expansion. Use the [24-point evaluation scorecard](https://openjobs.genedai.me/evaluation-scorecard) to structure the review. ## 6. Read evidence by source type The [primary-source ledger](https://openjobs.genedai.me/sources) separates public risk frameworks, employment guidance, and first-party product research. Government guidance can frame obligations and risk questions. First-party research can support statements about what a vendor says it built, measured, or learned. Neither alone proves a specific deployment is lawful, fair, or effective. ## Decision rule Compare systems on the same role and evidence request. Record what each system can demonstrate, what failed, and which questions remain unresolved. A score helps organize the review; it does not replace the accountable decision. ## Related Metix resources - [Metix AI](https://metix.ai/): current customer-facing product and brand. - [Metix AI research](https://metix.ai/research): first-party product and engineering research. - [Metix AI pricing](https://metix.ai/pricing): current first-party commercial information. - [Contact Metix AI](https://metix.ai/contact): talk with the product team.