Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
| Autor: | Liem, C.C.S., Langer, Markus, Demetriou, A.M., Hiemstra, Annemarie M.F., Achmadnoer Sukma Wicaksana, Sukma, Born, Marise Ph., König, Cornelis J., Jair Escalante, H., Escalera, S., Guyon, I., Baró, X., Güçlütürk, Y |
|---|---|
| Přispěvatelé: | Work and Organizational Psychology |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
business.industry
General problem 05 social sciences Methodology Interdisciplinarity 02 engineering and technology Job candidate screening Machine learning computer.software_genre Explainability Transparency (behavior) Multimodal analysis 0502 economics and business Scalability 0202 electrical engineering electronic engineering information engineering Psychology 020201 artificial intelligence & image processing Artificial intelligence business computer 050203 business & management |
| Zdroj: | Explainable and Interpretable Models in Computer Vision and Machine Learning The Springer Series on Challenges in Machine Learning ISBN: 9783319981307 The Springer Series on Challenges in Machine Learning, 197-253 STARTPAGE=197;ENDPAGE=253;TITLE=The Springer Series on Challenges in Machine Learning |
| DOI: | 10.22028/d291-31248 |
| Popis: | In a rapidly digitizing world, machine learning algorithms are increasingly employed in scenarios that directly impact humans. This also is seen in job candidate screening. Data-driven candidate assessment is gaining interest, due to high scalability and more systematic assessment mechanisms. However, it will only be truly accepted and trusted if explainability and transparency can be guaranteed. The current chapter emerged from ongoing discussions between psychologists and computer scientists with machine learning interests, and discusses the job candidate screening problem from an interdisciplinary viewpoint. After introducing the general problem, we present a tutorial on common important methodological focus points in psychological and machine learning research. Following this, we both contrast and combine psychological and machine learning approaches, and present a use case example of a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-supported hiring solutions by relevant stakeholders. Finally, we present several recommendations on how interdisciplinary collaboration on the topic may be fostered. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|