algorithmic decision-making; human resources; data governance; transparency; accountability; anti-discrimination policy
Date of Ingest
2025-06-26T09:56:35-05:00
Geographic Coverage
USA
Abstract
Organizations automate hiring decision-making algorithmically to help identify top talent via third-party solutions. We examine challenges of algorithmic hiring practices, including power imbalances, disparate treatment, and disparate impact via systematic literature review. Integrated, participatory data governance approaches accomplish two key priorities: they develop data governance practices that reflect organizational culture; and they make trade-offs transparent, disclosing practices and inviting scrutiny. We offer a participatory data governance framework to address bias in algorithmic hiring.
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