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@Select Types{,
Journal = "Band-1",
Title= "Does Data-Driven Recruitment Lead to Less Discrimination? – A Technical Perspective",
Author= "Kian Schmalenbach, Sven Laumer",
Doi= "https://doi.org/10.30844/wi_2020_q2-schmalenbach",
Abstract= "Due to its large cost-saving potential, data-driven recruitment is becoming increasingly popular across various industries. However, several cases were reported where the use of corresponding technologies had caused systematic discrimination against certain candidate groups. While existing approaches to discover and prevent discrimination in data classification mostly perform well within a specific context, it remains unclear to what extent datadriven recruitment can be conducted discrimination-free in real-world business applications, where the respective context-specific assumptions do not necessarily hold. Hence, we first define a generic discrimination model that allows for arbitrary descriptions of job candidate characteristics, before applying two sophisticated discrimination-prevention algorithms on a sample data set generated from our model to evaluate the algorithms’ performance. Our analysis shows that the amount of removed discrimination highly depends on the application context and its underlying definitions and assumptions, making it hard to provide a holistic answer to our research question.
",
Keywords= "data-driven recruitment, discrimination-aware data mining, discrimination discovery, discrimination prevention",
}
Kian Schmalenbach, Sven Laumer: Does Data-Driven Recruitment Lead to Less Discrimination? – A Technical Perspective. Online: https://doi.org/10.30844/wi_2020_q2-schmalenbach (Abgerufen 25.12.24)
Open Access
Abstract
Abstract
Due to its large cost-saving potential, data-driven recruitment is becoming increasingly popular across various industries. However, several cases were reported where the use of corresponding technologies had caused systematic discrimination against certain candidate groups. While existing approaches to discover and prevent discrimination in data classification mostly perform well within a specific context, it remains unclear to what extent datadriven recruitment can be conducted discrimination-free in real-world business applications, where the respective context-specific assumptions do not necessarily hold. Hence, we first define a generic discrimination model that allows for arbitrary descriptions of job candidate characteristics, before applying two sophisticated discrimination-prevention algorithms on a sample data set generated from our model to evaluate the algorithms’ performance. Our analysis shows that the amount of removed discrimination highly depends on the application context and its underlying definitions and assumptions, making it hard to provide a holistic answer to our research question.
Keywords
Schlüsselwörter
data-driven recruitment, discrimination-aware data mining, discrimination discovery, discrimination prevention
References
Referenzen
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