A Human Perspective to AI-based Candidate Screening
| Type of publication: | Conference paper |
| Citation: | Vasquez-Rodriguez_HICSS-58_2024 |
| Booktitle: | Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS) |
| Year: | 2024 |
| Month: | January |
| Abstract: | Skill extraction is at the core of algorithmic hiring. It is based on identifying terms commonly found in both targets (i.e., resumes and job offers), aiming at identifying a “match” or correspondence between both. This paper focuses on skill extraction from resumes, as opposed to job offers, and considers this task both from the Human Resource Management (HRM) and AI points of view. We discuss challenges identified by both fields and explain how collaboration is instrumental for a successful digital transformation of HRM. We argue that annotation efforts are an ideal example of where collaboration between both fields is needed and present an annotation effort on 46 resumes with 41 trained annotators, resulting in a total of 116 annotations. We analyze the skills extracted by multiple different systems and compare those to the skills selected by the annotators, and find that the skills extracted differ a lot in terms of length and semantic content. The skills extracted with conversational Large Language Models (LLMs) tend to be very long and detailed, other systems are very concise, whereas humans are in the middle. In terms of semantic similarity, conversational LLMs are closer to human outputs than other systems. Our analysis proposes a different perspective to understand the well-studied, but still unsolved skill extraction task. Finally, we provide recommendations for the skill extraction task that aligns with both HR and computational perspectives. |
| Keywords: | AI, Candidate Screening, Human Resource Management, Natural language processing, Skill Extraction |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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