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The field of Human Resources (HR) is at the forefront of adopting AI technologies. In 2021, by one count there were over 250 commercial AI-based HR tools available. Over 90% of employers in a study by Accenture and Harvard Business School use automated systems to filter or rank candidates, and over 40% of HR-functions of international companies use AI-applications. These so-called HR Technologies (HR Tech) aim to replace or support Human Resource functions such as talent acquisition and management, employee compensation, workforce analytics, and performance management.
Recommender Systems, broadly defined as systems that aim to support users in decision making by suggesting and offering relevant content, play an integral role in the rapid rise of HR Tech. Their applications range from assisting the talent acquisition process through matching, analyzing resumes or other user representations for candidate screening and automated assessment, to broader tasks such as recommendations for upskilling.
The use of AI applications in the recruitment process, such as recommender systems, is considered high-risk by the European Commission, as automation here can directly impact the (working) lives of people. In this light, the rise of AI-assisted hiring and screening is met with caution, and is a widely-used example application area in AI ethics and fairness literature. At the same time, there is a rising commercial interest around these technologies from companies and startups alike. We feel the prevalence and rise of recommender system technology in HR calls for a central forum where researchers and practitioners alike can study and discuss the domain-specific aspects, challenges, and opportunities of RecSys and other HR Techs.
Workshop focus
The focus of the RecSys in HR workshop series is on all areas of HR: recruitment (or job recommendation), retention, training and development, performance and career management, and talent pool management, and compensation and benefits. We invite submissions of original research on all aspects of recommender systems or related techniques—such as search, descriptive and predictive analytics, and interactive visualizations—applied to any of these key HR areas. In addition, we welcome position papers that discuss and present novel ideas or insights concerning approaches, key challenges, or theoretical or methodological issues that have the potential to inspire substantive discussion and lead to significant advances in the field.
