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AI-Powered Recruitment: Data-Driven, Responsible Talent Acquisition

Section 1: AI Recruitment Landscape and Value

  • Where AI fits in modern talent acquisition workflows

  • Key AI capabilities: NLP, prediction, ranking, and search

  • High-impact use cases across sourcing to offer stages

  • Business outcomes: speed, quality, cost, and candidate experience

Section 2: Data Foundations for Recruiting AI 

  • Recruiting data types: resumes, profiles, and interviews

  • Job data: role requirements, skills, and success signals

  • Data quality risks: bias, missing data, and label leakage

  • Privacy and retention basics for candidate data in HR tech

Section 3: AI for Screening and Matching

  • Resume parsing and skills extraction at scale

  • Semantic search vs. keyword matching in talent pools

  • Ranking and recommendation logic for shortlisting

  • Handling nontraditional signals and career changes

  • Human-in-the-loop review models and control points

  • Measuring model performance beyond “accuracy”

Section 4: AI for Assessment and Interviewing 

  • Assessment types: skills tests, simulations, and structured data

  • Interview intelligence: transcription, summaries, and insights

  • Predictive validity vs. proxy signals in candidate evaluation

  • Candidate experience: transparency and trust considerations

Section 5: Responsible AI and Compliance in Hiring 

  • Fairness concepts: disparate impact and job relevance

  • Bias sources across data, models, and process design

  • Governance: documentation, approvals, and audit readiness

  • Vendor evaluation: model explainability and reporting

  • Regulatory landscape: EEOC, GDPR, and emerging laws

  • Monitoring: drift, adverse impact, and ongoing controls

Section 6: Final Review

  • Review of key concepts

  • Future learning directions

Skill Level: Beginner

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