Generated from prompt:
Expanded 12-slide version of 'AI Job Matching: How Modern Systems Understand Fit'. Include sections: Title, Agenda, Scientific Era, ATS Era, AI Era, Agentic AI Era, Psychology of Fit, Modern AI Techniques (Embeddings, Graphs, Contrastive Learning), Case Studies (LinkedIn, CareerBuilder, World Bank, IAB), Bias & Regulation, What Leaders Should Do Now, and Conclusion: Fit Redefined.
Traces job matching eras (Scientific to Agentic AI), psychology of fit, modern AI techniques (embeddings, graphs, contrastive learning), case studies (LinkedIn et al.), bias/regulation, leader actions
This title slide introduces the topic "AI Job Matching: How Modern Systems Understand Fit." The subtitle outlines the "Evolution from Science to Agentic AI" and credits the presentation to [Your Name].
Evolution from Science to Agentic AI | Presented by [Your Name]
This presentation agenda outlines the evolution of job matching eras, psychology of fit, and modern AI techniques. It continues with case studies, bias, regulation & leader actions, and concludes with fit redefined.
Source: AI Job Matching: How Modern Systems Understand Fit
The slide outlines a timeline of job matching eras, starting with psychometrics in the 1920s using psychological tests for ability-based fits. It progresses through keyword ATS in the 1990s, ML models in the 2010s for skill analysis, and agentic AI in the 2020s for proactive negotiations.
1920s: Scientific Era: Psychometrics Psychological tests matched workers to jobs based on abilities and traits. 1990s: ATS Era: Keywords Applicant Tracking Systems screened resumes using simple keyword matching. 2010s: AI Era: ML Models Machine learning models analyzed skills and experience for precise fits. 2020s: Agentic AI Era Autonomous agents proactively negotiate and optimize job-candidate matches.
The slide's left column explains the Psychology of Fit using Holland Codes (RIASEC) for interests and Big Five traits for personality-based candidate-job matching. The right column covers Modern AI Techniques, including embeddings for semantic similarity, graphs for skill networks, and contrastive learning for precise fit predictions.
| Psychology of Fit | Modern AI Techniques |
|---|---|
| Holland Codes classify interests into 6 types (RIASEC). Big Five traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) assess personality for holistic candidate-job matching. | Embeddings capture semantic similarity between jobs and profiles. Graphs model networks of skills and connections. Contrastive Learning fine-tunes representations for precise fit predictions. |
This conclusion slide, titled "Fit Redefined," highlights "Semantic + Behavioral Alignment" as the core message. The subtitle urges "Audit Bias, Invest Now, Lead the Future."
Fit Redefined: Semantic + Behavioral Alignment
Audit Bias, Invest Now, Lead the Future
Source: AI Job Matching Presentation
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