AI for Hiring & HR Course

Contents

1. How to use this book
2. Chapter 1. Job Postings and Resume Screening Inputs

   Three inputs handled by hiring AI · Discriminatory expressions in job posting wording · Direct discrimination and indirect discrimination · Scope of application of the Fair Hiring Procedure Act and basic screening materials · Information that cannot be requested as basic screening materials · Job postings and actual working conditions: Prohibition of false job advertisements · Selecting input items for AI resume screening · Proxy variables and indirect discrimination · Bypass information in free-form text · Review procedure for AI-generated job postings and screening criteria · Documenting evidence of job relevance · The boundary between actual job requirements and discriminatory expressions · Preferential expressions and affirmative action · The scope of application varies by law

3. Chapter 2. Evaluations, Interviews, and Automated Decisions

   Definition of an Automated Decision · The boundary between full automation and human-reviewed decisions · Three rights and exceptions · Determining significant impact · Matters that must be disclosed in advance · Procedures for handling requests received · Conditions for a good explanation · Validity of AI interview and evaluation tools · Explainability and interpretability · Accessibility and reasonable accommodation · High-impact areas and recruitment under the AI Basic Act · Designing meaningful human review · What consent resolves and what it does not resolve · Recruitment agencies and the accountability of Automated Decisions · Changing the degree of automation also changes obligations

4. Chapter 3. Applicant and Employee Data

   Personal Information and Combinability · Processing Principles: Purpose, Minimization, Anonymity · Legal Bases for Processing: On What Grounds Is Data Collected and Used? · Use and Provision Beyond the Original Purpose · Sensitive Information · Uniquely Identifiable Information and Resident Registration Numbers · Pseudonymous Information and Its Limitations · Entrustment of Processing and Provision to Third Parties · Overseas Transfer · Retention Periods and Destruction · Safety Measures and Access Logs · Decision-Making Procedure: May This Data Be Entered into This AI? · Checking Data Use Terms in Vendor Contracts · Between "Deleting the Name" and "Being Anonymous" · How Free Is Employee Consent? · The Lifespan of Derived Data

5. Chapter 4. HR Operations and Performance Management

   Tasks assigned to AI in HR operations and the level of risk · Drafting performance evaluations: Restricting to grounding materials · Reviewing AI-written evaluation text · Accountability in feedback and interviews · Source document management for HR chatbots · Query permissions for HR chatbots and HR assistants · HR records and audit trails · Limits of employee monitoring · Taking action based on monitoring results · Prohibition of age discrimination and AI metrics · Prohibition of discrimination against persons with disabilities and reasonable accommodation · Basic principles of equal treatment · The Trap of What Is Easy to Measure Becoming Performance · Why Monitoring Purposes Creep · The Weight of Automated Evaluation Sentences

6. Chapter 5. Fairness Auditing and Governance

   What fairness measures · Selection rate and selection rate ratio · The four-fifths rule: What it says and what it does not say · Sample size and statistical significance · Stage-by-stage analysis · Group disparities in errors · Turning audit findings into action · Audit cycles and triggers · Group data for auditing · Requesting vendor validation data · The four functions of the NIST AI RMF · Accountability Framework and Operating Rules · Incident response · Why group-specific cutoff scores are not the solution · Good fairness metrics are not enough · Governance is not a one-time approval

7. Appendix A. Glossary
8. Appendix B. Self-check answers and explanations
9. Appendix C. Exam day guide
10. Appendix D. Reference documents