Overview

The job market of the 21st century poses a set of unique challenges. Automation through AI and other disruptive technologies will result in unprecedented shocks in the job market, with entire job categories disappearing while new types of jobs requiring new skills and competencies will emerge. While such shifts in the job market have happened before, the impact of the ongoing revolution may be larger and will unfold much faster than it did in the previous industrial revolutions. To mitigate the impact of this disruption, large parts of the workforce will need upskilling and retraining in a matter of years, and continuously thereafter.

The FEAST workshop aims to address the challenges of the contemporary job market and human resources management by means of data-driven solutions that better enable all relevant stakeholders to achieve their goals in a fair, effective, and sustainable manner, including the job seekers, employers, HR agencies, HR managers, policy makers and government agencies, training facilities, and more. The challenges for which data science can make a genuine difference are numerous: Workers need better job recommendations and career advice; Employers need better HR and recruitment strategies, with due consideration of ethical constraints; Policy makers need to understand how to regulate the job market to ensure efficiency, fairness, and inclusion; And much more.

Non-exhaustive list of topics of interest

• Job market analytics
• Job recommender systems (models and systems, explainability/transparency, actionability, fairness/bias,…)
• AI for curriculum and career advice
• NLP for analyzing, writing, and editing CVs and job adverts
• Taxonomies and ontologies for HR data (creation and use cases)
• Knowledge graphs for HR data (creation and use cases)
• Visualisation techniques applied to HR data
• Representation learning for HR data
• Data-driven strategic workforce planning
• Data-driven methods for identifying skills gaps and emerging skills
• Data-driven methods for monitoring and enhancing job satisfaction
• Data-driven methods for the detection of occupational hazards and burn-out
• Ethical and legal issues in the use of AI for HR management (privacy, fairness, transparency,…)
• Inclusion of vulnerable groups in the job market
• Privacy-preserving methods in HR management

Contributions are solicited on methods as well as systems, on theory as well as applications, from academia as well as industry and government agencies. While ethical issues may be a core focus of a contribution, we strongly encourage possible ethical benefits or concerns to be explicitly considered also in contributions where it is not the core focus.