How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications
The increasing adoption of AI systems in hiring has raised concerns about algorithmic bias and accountability, prompting regulatory responses including the EU AI Act, NYC Local Law 144, and Colorado's AI Act. While existing research examines bias through technical or regulatory lenses, both perspectives overlook a fundamental challenge: modern AI hiring systems operate within complex supply chains where responsibility fragments across data vendors, model developers, platform providers, and deploying organizations. This paper investigates how these dependency chains complicate bias evaluation and accountability attribution. Drawing on literature review and regulatory analysis, we demonstra...
| Authors | Gauri Sharma, Maryam Molamohammadi |
|---|---|
| Primary category | cs.CY |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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