Scholarlink

<p>Contemporary workforce management systems are caught in a tension that rarely receives adequate scholarly attention: the drive toward frictionless digital authentication has outpaced the equally important need to protect worker agency, ensure transparent data governance, and sustain identity trust across the full employment lifecycle. Most existing frameworks treat digital identity as a point-in-time verification problem, yet the employment relationship is inherently longitudinal—spanning hiring, onboarding, role transitions, cross-organizational mobility, and eventual offboarding. This paper introduces a Human-Centric Digital Identity Framework (HCDIF) for workforce lifecycle management that reorients the design logic from system-first efficiency to person-first accountability. The framework integrates adaptive AI-driven credential orchestration with federated identity governance, privacy-by-design principles, and a structured worker transparency layer that gives individuals meaningful visibility into—and contestability over—decisions made about their identity data. Drawing on design science research methodology, we specify the HCDIF architecture across six lifecycle stages, detail a federated trust negotiation protocol, and evaluate the framework against simulated multi-organization workforce scenarios. Results are indicative and based on synthetic scenario modelling: HCDIF demonstrates a reduction in unauthorized cross-system identity propagation of 91.4%, achieves workforce onboarding verification in under 2.1&#xa0;s under simulated conditions, and sustains broadly equitable performance across demographic cohorts with a maximum inter-group Equal Error Rate differential of 0.19% points in the simulation. Beyond technical metrics, the framework is assessed for its alignment with the principles underlying GDPR, CCPA, and ISO/IEC 24,760—including transparency, contestability, data minimization, and accountability—and the public-policy dimensions of worker dignity and data sovereignty. The work contributes a design-level, policy-coherent architecture for organizations seeking to modernize workforce identity infrastructure without sacrificing the human trust that makes such infrastructure legitimate.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Designing a human-centric digital identity framework for workforce lifecycle management using AI and federated systems

  • 期刊论文

  • 发表日期：

    2026年5月29日

查看全文
  • Rahul Raj

摘要

Contemporary workforce management systems are caught in a tension that rarely receives adequate scholarly attention: the drive toward frictionless digital authentication has outpaced the equally important need to protect worker agency, ensure transparent data governance, and sustain identity trust across the full employment lifecycle. Most existing frameworks treat digital identity as a point-in-time verification problem, yet the employment relationship is inherently longitudinal—spanning hiring, onboarding, role transitions, cross-organizational mobility, and eventual offboarding. This paper introduces a Human-Centric Digital Identity Framework (HCDIF) for workforce lifecycle management that reorients the design logic from system-first efficiency to person-first accountability. The framework integrates adaptive AI-driven credential orchestration with federated identity governance, privacy-by-design principles, and a structured worker transparency layer that gives individuals meaningful visibility into—and contestability over—decisions made about their identity data. Drawing on design science research methodology, we specify the HCDIF architecture across six lifecycle stages, detail a federated trust negotiation protocol, and evaluate the framework against simulated multi-organization workforce scenarios. Results are indicative and based on synthetic scenario modelling: HCDIF demonstrates a reduction in unauthorized cross-system identity propagation of 91.4%, achieves workforce onboarding verification in under 2.1 s under simulated conditions, and sustains broadly equitable performance across demographic cohorts with a maximum inter-group Equal Error Rate differential of 0.19% points in the simulation. Beyond technical metrics, the framework is assessed for its alignment with the principles underlying GDPR, CCPA, and ISO/IEC 24,760—including transparency, contestability, data minimization, and accountability—and the public-policy dimensions of worker dignity and data sovereignty. The work contributes a design-level, policy-coherent architecture for organizations seeking to modernize workforce identity infrastructure without sacrificing the human trust that makes such infrastructure legitimate.