Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)

Toine Bogers, David Graus, Mesut Kaya, Francisco Gutiérrez, Sepideh Mesbah, Chris Johnson
2022 Sixteenth ACM Conference on Recommender Systems  
According to PWC over 40% of HR-functions of international companies use . This so-called HR Technology (HR Tech) aims 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
more » ... assisting the talent acquisition process through matching [10], analyzing resumes or other user representations for candidate screening [22] and automated assessment [14, 16] , to broader tasks such as recommendations for upskilling [21] . The use of AI applications in the recruitment process, such as recommender systems, is considered high-risk by the European Commission [23], 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 [5, 12, 15, 18, 20] . At the same time, there is a rising commercial interest around these technologies from companies and startups alike [18] . We feel the prevalence and rise of recommender system technology in HR Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
doi:10.1145/3523227.3547414 fatcat:nqeyv3diwnao5m3o27q4woqjne