In this blog series, international experts on the topic of analyzing online job advertisements present their work. The presentations were given at the 9th OJA Forum, which is jointly organized twice a year by the Federal Institute for Vocational Education and Training and the Bertelsmann Stiftung. Topics at the 9th OJA Forum included the further development of existing taxonomies. Taxonomies are systematic classification systems used to organize information according to specific criteria – for example, to classify occupations, skills, or qualifications. Another key topic was how to link training content with labor market requirements. In this video blog series, the presentations will be published one after the other. The sixth talk is by Vincent Slot and Kasper Kok from Bullhorn.

Trade-offs in AI-based Matching of Jobs to Resumes: Algorithms, Representations, and Regulation

This talk outlines Bullhorn’s technology for matching job ads with resumes and vice versa, focusing on two contrasts: (1) content-based versus outcome-based algorithms, and (2) explicit (code-based) versus implicit (vector-based) document representations. Vincent Slot and Kasper Kok examine how these choices influence the quality and relevance of matches, highlighting trade-offs in performance and interpretability. We also cover transparency challenges posed by the different approaches and their implications under evolving AI legislation.

 

 

Here you can find all 6 videos from this series:

9th OJA Forum (Part 1) – What to add

9th OJA Forum (Part 2) – ESCO development

9th OJA Forum (Part 3) – Building and Evolving Green and AI Skill Taxonomies

9th OJA Forum (Part 4) – App to provide labor market guidance to students

9th OJA Forum (Part 5) – CareerBERT: Mapping Resumes to ESCO Jobs

9th OJA Forum (Part 6) – Trade-offs in AI-based Matching of Jobs to Resumes