Hall (nee Zdravkova), Sunny (2025) AI, Ethics and Women in Employment: The Impact upon Existing Biases. Masters thesis, University of Wales Trinity Saint David.
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Abstract
Artificial Intelligence is vastly changing our lives and environment with unprecedented speed (BBC, 2018; Elliott, 2019; Roser, 2022). The traditional workplace landscape is also being driven and shaped by this technology, with a focus on tools promising efficiency and objectivity (O’Connor and Liu, 2024; Feeney and Fusi, 2021). This study examines the connection between AI, discrimination against women, and, more importantly, the inequalities they face in the working environment shaped by this phenomenon. The dissertation also aims to understand the ethical implications of AI and how historical and systemic biases in data and algorithms can perpetuate discrimination against women. A narrative literature review was conducted using secondary sources as a critical reflection of the current state of AI and its impact on women in employment. The findings revealed AI’s ability to present both challenges and opportunities. While historical biases in datasets and algorithms pose risks of reproducing workplace inequalities, the inclusion of women in AI development and policymaking is crucial to overcoming gender discrimination. The study highlights that a dual approach, considering both ethical and real-life experiences, is necessary to address the impact of AI, alongside the implementation of strategies to raise awareness of AI’s bias and upskill women in its design. At the same time, government bodies and policymakers must work in collaboration to establish a connection between gender-related matters and actively integrate AI ethics into present policy.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HQ The family. Marriage. Woman Q Science > Q Science (General) |
| Divisions: | Theses and Dissertations > Masters Dissertations |
| Depositing User: | Victoria Hankinson |
| Date Deposited: | 19 Jan 2026 08:54 |
| Last Modified: | 19 Jan 2026 08:54 |
| URI: | https://repository.uwtsd.ac.uk/id/eprint/4095 |
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