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

The ongoing industrial revolution is the driver of a rapidly advancing shift in the division of labour between humans on the one hand and machines and algorithms on the other.

This is the cause of significant challenges in the job market, such as the emergence of important skills gaps that need to be addressed by extensive upskilling or reschooling of workers.

This requires considerable forethought and hence insight into the future job markets, as well as an understanding of how to best meet the job market's current and future needs.

Substantial value is to be gained at all levels: from individual workers, over talent and human resources management within companies, to the determination of policy at governmental level.This Proof of Concept proposal will address these challenges by leveraging results from the ERC Consolidator Grant FORSIED which lend themselves well to a uniquely suited and elegant data-driven approach.

In particular, the method Conditional Network Embedding (CNE) offers a powerful framework for making sense of the diverse information relevant to human talent and the job market. It provides a platform to tackle a diverse range of use cases in a uniform manner.

Moreover, a distinguishing advantage of CNE is that it offers mechanisms for compensating for existing biases in the job market, ensuring fairness, non-discrimination, and inclusion when deployed to these use cases.During the project, a prototype platform will be developed, in tight collaboration with actors in the private and public sectors.

This prototype will be evaluated, the IPR position investigated, and a market study conducted leading to a road to market strategy.