Unveiling the Benefits, Limitations, and Mitigation of Bias in Artificial Intelligence within Organizational Contexts-A Systematic Review
- DOI
- 10.2991/978-2-38476-146-3_9How to use a DOI?
- Keywords
- Artificial intelligence; machine learning; organization; bias
- Abstract
This essay offers an extensive discussion of the advantages, drawbacks, and potential remedies for reducing bias in artificial intelligence (AI) systems used in corporate settings. As AI technology continues to permeate various sectors, concerns regarding bias have emerged as a critical issue. The presence of bias in AI systems can perpetuate discrimination and social disparities, leading to ethical, legal, and social implications for organizations. By examining existing literature and exploring real-world examples, this study aims to provide a comprehensive understanding of the advantages and drawbacks of AI in organizational settings. Furthermore, it delves into potential strategies and solutions to mitigate bias. Through this systematic review, organizations can gain valuable insights into the impact of AI and interactions with human capital, thus developing informed strategies to address this pressing challenge.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xinyi Li PY - 2023 DA - 2023/11/29 TI - Unveiling the Benefits, Limitations, and Mitigation of Bias in Artificial Intelligence within Organizational Contexts-A Systematic Review BT - Proceedings of the 2023 8th International Conference on Modern Management and Education Technology (MMET 2023) PB - Atlantis Press SP - 55 EP - 65 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-146-3_9 DO - 10.2991/978-2-38476-146-3_9 ID - Li2023 ER -