Project

Fair, Effective, and Sustainable Talent Management using Conditional Network Embedding

Acronym
FEAST
Code
41B08921
Duration
01 April 2021 → 30 September 2022
Funding
European funding: framework programme
Principal investigator
Research disciplines
  • Natural sciences
    • Bioinformatics data integration and network biology
  • Social sciences
    • Human resource management
Keywords
employability
Other information
 
Project description

The ongoing industrial revolution poses significant challenges to the job market regarding upskilling and re-education, job-matching, curriculum advice, strategic workforce management, and more. To help tackle these challenges, the conditional network embedding (CNE) method enables the building of an innovative AI platform that unifies the diverse information related to human talent and the job market. This platform is naturally capable of compensating any existing biases in the data, thus avoiding unfairness or discrimination when it is deployed. The EU-funded proof-of-concept FEAST project will leverage results from the European Research Council project FORSIED and develop this platform in close collaboration with the private and public sectors. FEAST will evaluate the platform, investigate the intellectual property rights and conduct a market study.

 
 
 
Disclaimer
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency (ERCEA). Neither the European Union nor the authority can be held responsible for them.