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DOI :10.26650/JECS2023-1415085   IUP :10.26650/JECS2023-1415085    Full Text (PDF)

Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market

Mahmut ÖzerMatjaz PercHayri Eren Suna

Artificial intelligence (AI) is now present in nearly every aspect of our daily lives. Furthermore, while this AI augmentation is generally beneficial, or at worst, nonproblematic, some instances warrant attention. In this study, we argue that AI bias resulting from training data sets in the labor market can significantly amplify minor inequalities, which later in life manifest as permanently lost opportunities and social status and wealth segregation. The Matthew effect is responsible for this phenomenon, except that the focus is not on the rich getting richer, but on the poor becoming even poorer. We demonstrate how frequently changing expectations for skills, competencies, and knowledge lead to AI failing to make impartial hiring decisions. Specifically, the bias in the training data sets used by AI affects the results, causing the disadvantaged to be overlooked while the privileged are frequently chosen. This simple AI bias contributes to growing social inequalities by reinforcing the Matthew effect, and it does so at much faster rates than previously. We assess these threats by studying data from various labor fields, including justice, security, healthcare, human resource management, and education.


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APA

Özer, M., Perc, M., & Suna, H.E. (2024). Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society, 0(69), 159-168. https://doi.org/10.26650/JECS2023-1415085


AMA

Özer M, Perc M, Suna H E. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society. 2024;0(69):159-168. https://doi.org/10.26650/JECS2023-1415085


ABNT

Özer, M.; Perc, M.; Suna, H.E. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society, [Publisher Location], v. 0, n. 69, p. 159-168, 2024.


Chicago: Author-Date Style

Özer, Mahmut, and Matjaz Perc and Hayri Eren Suna. 2024. “Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market.” Journal of Economy Culture and Society 0, no. 69: 159-168. https://doi.org/10.26650/JECS2023-1415085


Chicago: Humanities Style

Özer, Mahmut, and Matjaz Perc and Hayri Eren Suna. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market.” Journal of Economy Culture and Society 0, no. 69 (Nov. 2024): 159-168. https://doi.org/10.26650/JECS2023-1415085


Harvard: Australian Style

Özer, M & Perc, M & Suna, HE 2024, 'Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market', Journal of Economy Culture and Society, vol. 0, no. 69, pp. 159-168, viewed 14 Nov. 2024, https://doi.org/10.26650/JECS2023-1415085


Harvard: Author-Date Style

Özer, M. and Perc, M. and Suna, H.E. (2024) ‘Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market’, Journal of Economy Culture and Society, 0(69), pp. 159-168. https://doi.org/10.26650/JECS2023-1415085 (14 Nov. 2024).


MLA

Özer, Mahmut, and Matjaz Perc and Hayri Eren Suna. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market.” Journal of Economy Culture and Society, vol. 0, no. 69, 2024, pp. 159-168. [Database Container], https://doi.org/10.26650/JECS2023-1415085


Vancouver

Özer M, Perc M, Suna HE. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society [Internet]. 14 Nov. 2024 [cited 14 Nov. 2024];0(69):159-168. Available from: https://doi.org/10.26650/JECS2023-1415085 doi: 10.26650/JECS2023-1415085


ISNAD

Özer, Mahmut - Perc, Matjaz - Suna, HayriEren. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market”. Journal of Economy Culture and Society 0/69 (Nov. 2024): 159-168. https://doi.org/10.26650/JECS2023-1415085



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Submitted05.01.2024
Accepted08.02.2024
Published Online28.03.2024

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