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 demonstrate that fragmented responsibilities create two critical problems. First, bias emerges from component interactions rather than isolated elements, yet proprietary configurations prevent integrated evaluation. A resume parser may function without bias independently but contribute to discrimination when integrated with specific ranking algorithms and filtering thresholds. Second, information asymmetries mean deploying organizations bear legal responsibility without technical visibility into vendor-supplied algorithms, while vendors control implementations without meaningful disclosure requirements. Each stakeholder may believe they are compliant; nevertheless, the integrated system may produce biased outcomes. Analysis of implementation ambiguities reveals these challenges in practice. We propose multi-layered interventions including system-level audits, vendor guidelines, continuous monitoring mechanisms, and documentation across dependency chains. Our findings reveal that effective governance requires coordinated action across technical, organizational, and regulatory domains to establish meaningful accountability in distributed development environments.

翻译：随着AI系统在招聘领域的广泛应用，算法偏见与问责问题引发关注，促使欧盟《人工智能法案》、纽约市地方法律144号及科罗拉多州《人工智能法案》等监管措施出台。现有研究从技术或监管视角审视偏见问题，但两种视角均忽视了一个根本性挑战：现代AI招聘系统运作于复杂供应链中，责任在数据供应商、模型开发者、平台提供商及部署组织之间分散。本文探讨这些依赖链如何使偏见评估与归责复杂化。通过文献综述与监管分析，我们证实责任分散引发两个关键问题。其一，偏见产生于组件交互而非孤立要素，然而专有配置阻碍了集成评估——简历解析器独立运行时可能无偏见，但与特定排名算法及筛选阈值整合后却可能导致歧视。其二，信息不对称导致部署组织虽承担法律责任却缺乏对供应商提供算法的技术可见性，而供应商在无实质性信息披露要求的情况下掌控算法实现。各利益相关方可能自认合规，但集成系统仍可能产生有偏结果。实施歧义性分析揭示了这些现实挑战。我们提出多层干预措施，包括系统级审计、供应商指南、持续监控机制及跨依赖链文档记录。研究结果表明，有效的治理需协调技术、组织与监管领域的行动，方能在分布式开发环境中建立实质性问责机制。