Deploy AI in HR you can defend in any room
Master IBM's AI Fairness 360 toolkit and the legal frameworks behind it — so you can audit, mitigate, and govern algorithmic bias in hiring and workforce decisions before a regulator, plaintiff's attorney, or board member asks you to.
Learn how to ethically use AI for HR decision making. Find out how to use IBM's AI Fairness 360 toolkit to remove bias in hiring, promotions, and workforce analytics. Create HR practices you can defend - in court, in conscience, and statistically.— Jennifer Young
What you'll learn
What you'll be able to do
- Explain the core ethical frameworks (fairness, accountability, transparency) that govern AI use in HR and apply them to real hiring-pipeline decisions.
- Audit an existing AI-driven HR tool or dataset for demographic bias using IBM's AI Fairness 360 (AIF360) metrics and bias detection methods.
- Implement at least two AIF360 bias-mitigation algorithms (e.g., Reweighing, Equalized Odds) on a workforce dataset and interpret the fairness-accuracy trade-offs.
- Map AI fairness requirements to key legal and regulatory obligations including EEOC guidelines, the EU AI Act, and Title VII disparate-impact doctrine.
- Design an internal AI Ethics governance framework — including documentation, audit trails, and stakeholder review checkpoints — ready for executive or legal review.
- Communicate algorithmic fairness findings and remediation plans to non-technical HR leadership, legal counsel, and executive stakeholders with confidence and clarity.
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 20 lessons
Foundations of AI Ethics in HR
Establishes the conceptual bedrock of the course. Students learn how AI systems are woven into modern HR workflows, understand the three pillars of ethical AI (fairness, accountability, transparency), and develop a precise vocabulary for discussing algorithmic bias — its origins, forms, and downstream consequences. This module is deliberately placed first because every subsequent technical and legal lesson assumes fluency with these ideas.
- 1.1How AI Enters the HR PipelineIncluded
- 1.2Core Ethical Frameworks — Fairness, Accountability, TransparencyIncluded
- 1.3What Is Algorithmic Bias — and Where Does It Come From?Included
Legal and Regulatory Landscape for AI in HR
Translates abstract ethics into concrete legal obligations. Students explore U.S. federal doctrine (EEOC, Title VII disparate-impact), the EU AI Act's high-risk AI classification, and the leading edge of state and local regulation. Placing this module second — before any technical tooling — ensures that when students later run AIF360 audits, they are calibrating their fairness thresholds against actual legal standards, not arbitrary metrics.
- 2.1EEOC, Title VII, and the Disparate Impact DoctrineIncluded
- 2.2The EU AI Act — High-Risk AI and HR ObligationsIncluded
- 2.3NYC Local Law 144 and Emerging State-Level AI RegulationsIncluded
Getting Hands-On with IBM AI Fairness 360
Bridges conceptual knowledge and practical tooling. Students install and navigate AIF360, learn to read its core fairness metrics in plain English, and run their first structured bias audit on a real HR-adjacent dataset. This module is sequenced after legal grounding so students interpret metric outputs with legal thresholds — not just statistical curiosity — in mind. It deliberately precedes mitigation so students audit before they fix.
- 3.1AIF360 Architecture and Your First Bias AuditIncluded
- 3.2Core AIF360 Fairness Metrics — Reading and Interpreting ResultsIncluded
- 3.3Auditing a Real HR Dataset with AIF360Included
Bias Mitigation with AIF360 — Algorithms and Trade-Offs
Moves from detection to remediation. Students implement pre-processing, in-processing, and post-processing mitigation strategies using AIF360, interpret the resulting fairness-accuracy trade-off curves, and develop a principled decision framework for choosing among competing remediation options. An in-processing lesson is added to the draft to ensure full coverage of AIF360's mitigation capability and to prevent a sequencing gap between pre- and post-processing techniques.
- 4.1Pre-Processing Mitigation — Reweighing and Disparate Impact RemoverIncluded
- 4.2In-Processing Mitigation — Prejudice Remover and Adversarial DebiasingIncluded
- 4.3Post-Processing Mitigation — Equalized Odds and Calibrated Equalized OddsIncluded
- 4.4Fairness-Accuracy Trade-Offs — Making Defensible DecisionsIncluded
Building an AI Ethics Governance Framework for HR
Translates technical and legal knowledge into institutional infrastructure. Students design the policies, processes, documentation standards, and human-review checkpoints that make ethical AI use sustainable and auditable over time. This module is sequenced after technical mitigation so that governance artifacts reference real mitigation workflows — not abstract platitudes. An added lesson on vendor and procurement governance closes a gap in the original draft.
- 5.1Designing an AI Governance Policy for HRIncluded
- 5.2Vendor and Third-Party AI Procurement GovernanceIncluded
- 5.3Audit Trails, Documentation Standards, and Accountability StructuresIncluded
- 5.4Stakeholder Review Checkpoints and Executive Buy-InIncluded
Communicating Fairness — Reporting, Remediation, and Stakeholder Confidence
Develops the communication and presentation skills needed to translate complex technical and legal findings into actionable recommendations for non-technical audiences. The module culminates in a capstone project that integrates every prior outcome: a full bias audit, mitigation implementation, governance framework, and stakeholder presentation — delivered as if to a real executive and legal audience.
- 6.1Translating Technical Findings for Non-Technical AudiencesIncluded
- 6.2Presenting to Legal Counsel — Risk, Liability, and RemediationIncluded
- 6.3Capstone — Full Fairness Audit and Governance PresentationIncluded
Who it's for
Is this you?
HR Technology Managers
You're responsible for evaluating and deploying AI-powered HR tools and need the technical and regulatory vocabulary to hold vendors accountable and govern what you procure.
Talent Acquisition Leaders
Your hiring pipeline increasingly relies on AI screening and scoring — and you need to audit those systems for demographic bias before a regulator or plaintiff's attorney does it for you.
People Ops Directors
You own the workforce analytics and promotion frameworks that AI now influences, and you need a governance structure that holds up to executive and legal scrutiny.
HR Compliance Specialists
You're mapping organizational AI practices to EEOC, Title VII, the EU AI Act, and emerging state laws — and you need the technical fluency to translate legal requirements into audit-ready processes.
CHROs and VP-Level HR Leaders
You're accountable to the board and legal counsel for how AI is used in people decisions, and you need to understand fairness trade-offs well enough to make and defend policy calls.
People Analytics Practitioners
You work with workforce data day-to-day and are ready to move from descriptive reporting to bias-auditing rigor using AIF360 metrics and mitigation algorithms.
Questions
Frequently asked
Your teacher
A note from your teacher
Jennifer Young
You know AI is already part of your HR workflows - resume parsing, candidate scoring, workforce analytics - and you know, deep in your heart where it keeps you up at night, that you cannot fully explain what those systems are doing. Maybe a salesperson told you their product was “unbiased” and you didn’t know the words to argue about it. Maybe your legal counsel is asking tough questions you can’t answer yet. Maybe you’re performing due diligence on an AI procurement and you’re not even sure what you’re supposed to evaluate. That space between what you know you should know and what you know how to do—that’s what this program aims to fill. I created this because I saw companies run towards AI-related legal risk and reputational risk in HR without anyone ever having been empowered to look under the hood. The EEOC is releasing AI guidance. The EU has passed AI legislation. NYC just passed Local Law 144, which mandates bias audits. Individual states and localities are following suit. I could go on. Compliance requirements you haven’t even heard of yet are coming for your organization because the regulatory environment has already moved. Most HR teams will fall into compliance by trying really hard. That’s not good enough. Not because your team doesn’t care about doing the right thing, but because no one has armed you with the tools to do it. This program will arm you. You will learn on the IBM AI Fairness 360 toolkit. You will run real-world bias audits on workforce data. You will implement mitigation algorithms throughout the entire model lifecycle. You will understand your solutions’ fairness-accuracy tradeoffs - because every serious professional in this field has to deal with those tradeoffs. I’m not going to sugarcoat this content. You’re an HR professional, which means you know people, and how organizations work, and what risk looks like. I will meet you there and layer on the stats and law. By the end of this program, you’ll be able to walk into a room with your company’s legal counsel, your CHRO, or an external auditor and walk through a documented, defensible fairness audit and governance process for your team. You’ll have something to show for it. No slides about “our commitment to fair AI”. An actual artifact you can point to that demonstrates you identified the problem, know what it is, took steps to mitigate it, and put processes into place to continue to monitor it. That’s the baseline level of professional knowledge required to operate in this field now. Join me and build it.
— Jennifer Young
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- 6 modules, 20 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
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