27 courses across recruiting, onboarding, HR operations, L&D, and people analytics. Compliance-first: bias testing, privacy frameworks, and governance built into every workflow.
We lead with the question your legal team is already asking: “Can you defend this?”
Every recruiting workflow includes adverse impact testing, documented evaluation criteria, and decision-point mapping showing where human review is required. You build screening systems with audit trails — not black-box rankers that optimize for pattern-matching against your existing workforce.
When the EEOC asks how a hiring decision was made, you need evaluation criteria, data inputs, human review steps, and adverse impact documentation. This program builds that into the workflow — not as an afterthought, but as part of how the system operates.
Your organization probably has AI tools touching hiring, benefits, and performance reviews already — adopted piecemeal, governed by nobody. This program includes governance frameworks for mapping which tools touch protected data and building accountability before a regulator asks for it.
Employees should know when AI is involved, what data is being used, and what decisions it influences. This program builds every capability on that principle.
Trust Framework
| Principle | What it means in practice |
|---|---|
| Consent | Employees know when AI processes their data and have clear communication about scope |
| Transparency | AI-influenced decisions are documented and explainable to the affected employee |
| Boundaries | Clear separation between AI-appropriate tasks and human-only tasks (termination, accommodation, investigation) |
| Data minimization | AI systems access only the data required for the specific task — not the full employee record |
| Audit rights | Employees can request documentation of how AI contributed to decisions affecting them |
The C-suite sees AI as an efficiency play — but a resume screener that hasn’t been tested for adverse impact is a lawsuit in formation.
You need to be both the AI adopter and the AI guardian.
Benefits admin, compliance reporting, onboarding paperwork, data entry. You were hired to be strategic — instead you’re buried in administrative work while the C-suite asks why HR isn’t “more strategic.”
AI is already touching hiring decisions, employee data, and policy interpretation — with no shared framework for bias testing, no compliance documentation, and no way to explain to a regulator how any of it works.
Leadership wants AI to handle the workload. What they need is an operating model that distinguishes between tasks AI should automate, tasks it should augment, and tasks it must never touch without human oversight.
AI handles the volume. Your team makes every decision that carries legal, ethical, or human weight.
AI sorts applications against documented criteria and flags matches. Humans make every screening decision, with bias testing and audit documentation running in parallel.
HRT-01 — AI Recruiting & Talent AcquisitionAI handles document routing, system access, benefits enrollment, and training personalization. Your team handles the human connections that predict retention.
HRT-02 — AI Onboarding & Employee ExperienceBenefits questions, policy lookups, compliance reporting, HRIS data entry — AI handles the repetitive, rules-based tasks. Your team handles the judgment calls and the conversations that require empathy.
HRT-03 — AI HR Operations & PolicyAI surfaces attrition risk, engagement trends, skills gaps, and pipeline bottlenecks. Your team interprets those patterns with organizational context no model has.
HRT-05 — AI People Analytics & CompensationAI identifies organizational factors — compensation misalignment, manager quality, career visibility — that correlate with voluntary departure. Your team designs interventions that address root causes.
HRT-05 — AI People Analytics & CompensationAI maps skills against role requirements and identifies gaps across teams. Your L&D team designs the programs. AI handles the data work that used to take weeks.
HRT-04 — AI Learning & DevelopmentBoth measurable. Neither involves reducing your team.
Hours your team can redirect to the strategic work the C-suite keeps asking for.
| Workflow | Hrs/wk (3-person team) |
|---|---|
| Benefits administration & FAQ | 6–10 |
| Compliance reporting & documentation | 4–8 |
| Onboarding paperwork & provisioning | 3–6 |
| Resume screening (initial sort) | 4–8 |
| Policy lookups & handbook queries | 2–4 |
| HRIS data entry & maintenance | 3–5 |
Conservative total: 22–41 hours per week. At $50/hour fully loaded, that’s $55,000 to $102,500 per year in recaptured capacity — not savings from cuts, but hours your existing team spends on higher-value work.
The outcomes that change how leadership sees HR.
Conservative estimate using your numbers, not ours. Doesn’t account for improved candidate quality, reduced time-to-fill, or compliance issues caught before they become claims.
Each discipline builds on the compliance foundations established at the start.
Job descriptions, sourcing, screening with audit trails, structured interviews, and offer management — every workflow includes bias testing.
Onboarding automation, employee communication, engagement measurement, and culture integration — personalization without profiling.
Policy drafting, handbook management, compliance tracking, and HRIS automation — faster without removing the human judgment employment law requires.
Training design, skills assessment, career pathing, and performance reviews — AI measured against observable outcomes, not algorithmic scores.
Attrition modeling, compensation benchmarking, workforce planning, and pay equity analysis — statistical rigor to survive an audit.
| Course | What You’ll Learn |
|---|---|
| Recruiting Foundations & Bias Safeguards | Compliance architecture, adverse impact testing, decision-point mapping for human oversight |
| AI Job Descriptions & Role Design | Reduce gendered and exclusionary language with AI-assisted writing workflows |
| AI Candidate Sourcing | Expand candidate pools instead of narrowing them — sourcing systems that fight pattern-matching |
| AI Resume Screening & Evaluation | Documented evaluation criteria, bias audit protocols, and human review gates |
| AI Interview Preparation & Assessment | Structured interview prep giving every candidate the same evaluation framework |
| AI Offer Management & Hiring Analytics | Data-driven offer strategy and hiring analytics with compliance audit trails |
| Course | What You’ll Learn |
|---|---|
| Onboarding Foundations | Privacy-first onboarding design — where AI helps and where humans must lead |
| AI Onboarding Flow Design | Intake automation, personalized learning paths, and document routing workflows |
| AI Employee Communication | Scalable communication systems that remain personal, not impersonal |
| AI Engagement Surveys & Sentiment | Engagement measurement that surfaces real problems, not survey-completion optimization |
| AI Culture & Belonging Systems | Culture integration workflows with clear boundaries on personalization |
| Course | What You’ll Learn |
|---|---|
| HR Operations Foundations | Governance frameworks for AI-touched workflows — what to automate, augment, or protect |
| AI Policy Drafting & Review | AI-powered policy drafting that maintains consistency while flagging legal conflicts |
| AI Handbook & Documentation Management | Automated handbook updates and documentation management with review gates |
| AI Compliance Tracking | Monitor regulatory changes and surface policies needing updates before your next audit |
| AI HRIS Automation | Eliminate data entry without eliminating the review steps that catch errors |
| AI HR Service Delivery | Employee-facing service workflows with clear human escalation paths |
| Course | What You’ll Learn |
|---|---|
| L&D Foundations | AI maturity across training design — where it helps and where it creates false confidence |
| AI Training Design & Content Development | Personalized learning paths based on demonstrated skill gaps, not self-reported preferences |
| AI Skills Assessment & Gap Analysis | Skills assessment against observable criteria with bias-aware rating frameworks |
| AI Career Pathing & Succession Planning | Career paths built on capability data, not subjective manager nominations |
| AI Performance Reviews & Feedback Systems | Performance review systems that reduce bias by standardizing evaluation and flagging problematic patterns |
| Course | What You’ll Learn |
|---|---|
| People Analytics Foundations | Ethical analytics framework — organizational patterns, not individual surveillance |
| AI Attrition Modeling & Retention | Flight risk models based on organizational factors, not individual profiling |
| AI Compensation Benchmarking | Market data processing at scale with human judgment on signal weighting |
| AI Workforce Planning | Capacity planning and skills forecasting with defensible methodology |
| AI Pay Equity & Audit | Pay equity analysis that survives a DOL audit, with remediation workflow built in |
The AI HR Readiness Assessment tells you where you stand across six dimensions — so you start where the risk is highest, not where the list begins.
4 minutes. Honest answers only — the results are for you, not for us.
Example readiness score
“Mid-market tech company (1,200 employees). Implemented the AI screening workflow from HRT-01 with full bias audit protocols. Time-to-fill dropped 34%. More importantly, candidate pool diversity increased 28% because we stopped optimizing for pattern-matching against our existing workforce.”
“We used HRT-03 to build an AI compliance tracking system across three jurisdictions. Caught 14 policy conflicts in our handbook that manual review had missed for two years. Saved approximately $180K in potential exposure. The ROI was clear before we finished the second course.”
“Built the pay equity audit framework from HRT-05. Identified a 6.2% unexplained pay gap in one department that three years of manual reviews hadn’t surfaced. Remediated before it became a claim. Leadership now runs the analysis quarterly.”
Metrics represent outcomes reported by program participants. Individual results vary. See disclaimers below.
You build the system — including the guardrails — as you go.
Each course follows a consistent progression: understand the regulatory landscape, build your own workflow with audit trails, test it against failure scenarios. The output is something deployable — not a certificate and a set of notes.
15–20 minute working sessions. Short enough to fit between a benefits meeting and a candidate interview.
Shared prompt libraries with compliance guardrails, workflow documentation templates, and manager-facing adoption guides included in every curriculum.
Model changes: prompt libraries updated within two weeks. Employment law changes: compliance frameworks updated within 30 days. The foundational architecture survives both.
Course-level projects graded against observable criteria — not “demonstrates understanding of AI bias” but “documents at least two sources of bias with specific triggers and mitigation steps.”
That concern is exactly why this program exists. It teaches you to detect bias, build audit trails, and design human-oversight systems. Every recruiting workflow includes adverse impact testing and decision-point mapping showing where human review is required.
Every capability is built on privacy-first design. Sentiment analysis uses documented consent frameworks. Workforce analytics use organizational patterns, not individual surveillance. Employees understand what data is being used and where the human-only boundary is.
Your vendor won’t teach you how to evaluate whether those features introduce bias, how to document decisions for regulatory review, or how to build governance above any single tool. Vendor training teaches one tool. This teaches the operating model that makes every tool work.
The curriculum aligns with SHRM and HRCI continuing professional education requirements. CPE credit eligibility is being finalized — specific allocations will be published before the program’s full launch.
Model changes: prompt libraries updated within two weeks. Employment law changes: compliance frameworks updated within 30 days. The underlying architecture — auditable evaluations, bias testing, defensible compensation analysis — doesn’t change when the next model drops.
27 courses plus compliance templates, bias testing frameworks, and governance documentation. If your team saves 8 hours per week — conservative for a three-person team — that’s $20,000–$35,000 per year in recaptured capacity. That’s $100 per month — less than one hour of employment attorney billing.
Your membership includes every Aiversity path. Here’s where HR professionals go next.
The same systems-first, compliance-aware approach — across project management, process improvement, supply chain, and facilities.
Strategic frameworks for AI roadmaps, ROI measurement, organizational readiness, and enterprise adoption.
AI-powered legal research, contract analysis, regulatory tracking, and compliance frameworks from the legal perspective.
Curriculum design, assessment methodology, personalized learning systems, and edtech for L&D professionals.
27 courses across five disciplines. Defensible workflows with compliance built in — bias testing, audit documentation, privacy frameworks, and governance you can defend when it matters.
$1,200 per year. 3-day free trial. Credit card required. 30-day full refund if any of it disappoints.
Questions? info@aiversity.io
This program does not constitute employment law advice. The compliance frameworks, bias testing protocols, and governance documentation taught in this program are educational tools designed to improve HR professional practice. AI-assisted screening and any AI-influenced employment decisions require human oversight and compliance with applicable EEOC guidelines, state and local employment regulations, ADA requirements, and all other relevant employment law. Organizations should consult qualified employment counsel before implementing AI systems that affect hiring, termination, compensation, accommodation, or other employment decisions.
AI systems can introduce, amplify, or automate bias present in training data and historical organizational patterns. The bias testing protocols taught in this program reduce risk but do not eliminate it. Organizations are responsible for ongoing monitoring, testing, and remediation of AI-assisted HR systems.
Organizations implementing AI in HR functions are responsible for compliance with all applicable data privacy regulations, including state-specific employee privacy laws, GDPR where applicable, and industry-specific data handling requirements. The consent frameworks taught in this program are educational frameworks, not legal compliance certifications.
Metrics cited on this page represent outcomes reported by program participants and are not guaranteed. Individual and organizational results will vary based on implementation quality, organizational context, and existing HR infrastructure.