Mitigating Diversity Biases of AI in the Labor Market
Version
Published
Date Issued
2023-06-09
Author(s)
Type
Conference Paper
Language
English
Abstract
In recent years, artificial intelligence (AI) systems have been increasingly utilized in the labor market,
with many employers relying on them in the context of human resources (HR) management. However,
this increasing use has been found to have potential implications for perpetuating bias and discrimination. The BIAS project kicked off in November 2022 and is expected to develop an innovative technology (hereinafter: the Debiaser) to identify and mitigate biases in the recruitment process. For this purpose, an essential step is to gain a nuanced understanding of what constitutes AI bias and fairness in the labor market, based on cross-disciplinary and participatory approaches. What follows is a preliminary overview of the design and expected implementation of the project, as well as how our project aims to contribute to the existing literature on law, AI, bias, and fairness.
with many employers relying on them in the context of human resources (HR) management. However,
this increasing use has been found to have potential implications for perpetuating bias and discrimination. The BIAS project kicked off in November 2022 and is expected to develop an innovative technology (hereinafter: the Debiaser) to identify and mitigate biases in the recruitment process. For this purpose, an essential step is to gain a nuanced understanding of what constitutes AI bias and fairness in the labor market, based on cross-disciplinary and participatory approaches. What follows is a preliminary overview of the design and expected implementation of the project, as well as how our project aims to contribute to the existing literature on law, AI, bias, and fairness.
Subjects
QA75 Electronic computers. Computer science
QA76 Computer software
Conference
EWAF’23: European Workshop on Algorithmic Fairness
Submitter
Kurpicz-Briki, Mascha
Citation apa
Rigotti, C., Puttick, A. R., Fosch-Villaronga, E., & Kurpicz-Briki, M. (2023). Mitigating Diversity Biases of AI in the Labor Market. EWAF’23: European Workshop on Algorithmic Fairness. https://doi.org/10.24451/arbor.20738
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