Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2023 International Conference on Management Research and Economic Development
Series Vol. 23 , 13 September 2023
Open
Access | Article
Gender Bias in Hiring: An Analysis of the Impact of Amazon's Recruiting Algorithm
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Abstract
Algorithmic bias in artificial intelligence (AI) is a growing concern, especially in the employment sector, where it can have devastating effects on both individuals and society. Gender discrimination is one of the most prevalent forms of algorithmic bias seen in numerous industries, including technology. The underrepresentation of women in the field of information technology is a well-known issue, and several organizations have made tackling this issue a top priority. Amazon, one of the world's top technology businesses, has been at the forefront of initiatives to increase inclusiveness and diversity in the sector. Concerns exist, however, that algorithmic bias in their recruitment process may perpetuate discrimination based on gender. This study intends to investigate these issues by employing an interpretive epistemology and utilizing interviews and focus groups to acquire a more nuanced knowledge of the subject, with key factors contributing to algorithmic gender bias in Amazon's recruitment process and recommend strategies for improving women's employment in information technology.
Keywords
algorithmic bias, gender discrimination, interpretivism, interviews, focus groups, and diversity
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Management Research and Economic Development
- ISBN (Print)
- 978-1-915371-89-8
- ISBN (Online)
- 978-1-915371-90-4
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/23/20230367
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited