Artificial Intelligence for Promoting Equity, Diversity, and Inclusion
In the pursuit of a more inclusive, equitable, and diverse workplace, many organizations are turning to artificial intelligence as a transformative approach to enhance hiring practices. By automating resume evaluations, AI systems can reduce the impact of human biases that often lead to the underrepresentation of certain demographic groups. However, the use of AI in recruitment presents its own set of challenges, including the potential for biased algorithms, lack of transparency, and over-reliance on automation. This paper explores the benefits, challenges, and limitations of using AI to assess resumes, with a particular focus on how AI can enhance EDI in hiring practices. Through the review of various AI tools and case studies, the paper examines how AI can be leveraged to promote fairness, consistency, and access to diverse talent pools. Additionally, the paper discusses solutions to mitigate the risks of bias, such as diversifying training datasets, increasing transparency through explainable AI models, and ensuring human oversight in decision-making. Ultimately, this paper provides recommendations for organizations seeking to integrate AI into their recruitment process while ensuring that it fosters an inclusive and equitable hiring environment.
Authors
- Touraj Khodadadi (ORCID: https://orcid.org/0000-0002-4914-0544)
- Mazdak Zamani (ORCID: https://orcid.org/0000-0002-6421-0053)
- Ali Motamedi (ORCID: https://orcid.org/0000-0002-7012-440X)
- Saman Shojae Chaeikar (ORCID: https://orcid.org/0000-0002-2958-6901)
- Mojtaba Alizadeh (ORCID: https://orcid.org/0000-0002-5601-8387)
- Sasan Karamizadeh (ORCID: https://orcid.org/0000-0001-9445-8044)
Institutions
- Rutgers, The State University of New Jersey (US)
- University of Technology Sydney (AU)
- Lorestan University (IR)
- Malaysia University of Science and Technology (MY)
- Islamic Azad University of Damavand (IR)
- Empire State University (US)
Publication Details
- Published
- 2025-08-07
- DOI
- https://doi.org/10.1109/acdsa65407.2025.11166379
- Citations
- 2
- Primary Topic
- Healthcare Systems and Public Health
- Type
- article
- Field-Weighted Citation Impact
- 2.71