The Ethics of Machine Learning in Human Resources: Navigating Fairness and Efficiency

Corporations can examine how they utilize and manage human resources, or HR, in novel approaches thanks to machine learning equipment, which will improve management. However, it also poses difficulties for HR professionals who are ill-prepared. While ethical issues centre on worker confidentiality and fairness, legal concerns involve possible breaches of US workplace bias statutes including the European General Privacy Regulation. Projects involving the examination of legal information may not always be acceptable and may even be harmful to staff members, resulting in poor performance or unproductive conduct. HR managers may use the suggestions provided in this article to determine if machine learning initiatives are acceptable.

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Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/ic3i61595.2024.10828730
Citations
6
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
3.16
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article

The Ethics of Machine Learning in Human Resources: Navigating Fairness and Efficiency

Geetha Manoharan, Lakshmikanth Raja
6 citations
Ethics and Social Impacts of AI
3.16
article

The Ethics of Machine Learning in Human Resources: Navigating Fairness and Efficiency

Geetha Manoharan, Lakshmikanth Raja
article en
6 citations

Abstract

Corporations can examine how they utilize and manage human resources, or HR, in novel approaches thanks to machine learning equipment, which will improve management. However, it also poses difficulties for HR professionals who are ill-prepared. While ethical issues centre on worker confidentiality and fairness, legal concerns involve possible breaches of US workplace bias statutes including the European General Privacy Regulation. Projects involving the examination of legal information may not always be acceptable and may even be harmful to staff members, resulting in poor performance or unproductive conduct. HR managers may use the suggestions provided in this article to determine if machine learning initiatives are acceptable.

Decent work and economic growth
Openalex Percentile: Top 7%
Ethics and Social Impacts of AI
3.16
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