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Advancing Algorithmic Fairness in HR Decision-Making: A Review of DE&I-Focused Machine Learning Models for Bias Detection and Intervention
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
This review examines the growing integration of machine learning (ML) models in human resource (HR) decision-making and the associated challenges of algorithmic bias, particularly within the frameworks of diversity, equity, and inclusion (DE&I). As organizations increasingly adopt data-driven tools to support talent acquisition, performance evaluations, and promotion decisions, concerns over fairness, transparency, and ethical accountability have intensified. This paper evaluates existing ML-based bias detection and mitigation strategies, including fairness-aware classification, adversarial debiasing, and interpretable model architectures. Drawing from a multidisciplinary body of literature published between 2017 and 2021?including studies on socio-technical systems, ethics in AI, and DE&I audits?this review identifies gaps in current practices and proposes a research agenda for equitable algorithmic governance in HR contexts. Furthermore, the review synthesizes conceptual frameworks and practical case studies demonstrating how inclusive model design and continuous fairness evaluation can reduce systemic discrimination and promote inclusive work cultures. By emphasizing the intersection of algorithmic development and organizational values, this study contributes to the advancement of responsible AI in workforce management and underscores the need for transparent, auditable, and equitable ML solutions across industry sectors.
Keywords
Algorithmic Fairness, DE&I (Diversity, Equity & Inclusion), Machine Learning Bias, HR Decision-Making, Ethical AI, Bias Mitigation Models
How to cite this paper
@article{1709298,
author = {Immaculata Omemma Evans-Uzosike, Chinenye Gbemisola Okatta, Bisayo Oluwatosin Otokiti, Onyinye Gift Ejike, Omolola Temitope Kufile},
title = {Advancing Algorithmic Fairness in HR Decision-Making: A Review of DE&I-Focused Machine Learning Models for Bias Detection and Intervention},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {5},
number = {1},
pages = {530-542},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709298.pdf},
abstract = {This review examines the growing integration of machine learning (ML) models in human resource (HR) decision-making and the associated challenges of algorithmic bias, particularly within the frameworks of diversity, equity, and inclusion (DE&I). As organizations increasingly adopt data-driven tools to support talent acquisition, performance evaluations, and promotion decisions, concerns over fairness, transparency, and ethical accountability have intensified. This paper evaluates existing ML-based bias detection and mitigation strategies, including fairness-aware classification, adversarial debiasing, and interpretable model architectures. Drawing from a multidisciplinary body of literature published between 2017 and 2021?including studies on socio-technical systems, ethics in AI, and DE&I audits?this review identifies gaps in current practices and proposes a research agenda for equitable algorithmic governance in HR contexts. Furthermore, the review synthesizes conceptual frameworks and practical case studies demonstrating how inclusive model design and continuous fairness evaluation can reduce systemic discrimination and promote inclusive work cultures. By emphasizing the intersection of algorithmic development and organizational values, this study contributes to the advancement of responsible AI in workforce management and underscores the need for transparent, auditable, and equitable ML solutions across industry sectors.},
keywords = {Algorithmic Fairness, DE&I (Diversity, Equity & Inclusion), Machine Learning Bias, HR Decision-Making, Ethical AI, Bias Mitigation Models},
month = {July},
}