Responsible AI in Talent Management: A Human Capital Framework for Empowering American Workers in the Age of AI

Artificial intelligence (AI) is rapidly entering recruitment, assessment and talent development in U.S. organizations, yet most deployments optimize for speed and cost rather than for fair, accurate and developmental decisions about people. America's AI Action Plan (2025) makes empowering American workers in the age of AI a national priority, but offers organizations little guidance on how AI should be used inside talent management itself. This conceptual paper addresses that gap. Drawing on human capital and social capital theory, research on soft skills, HR analytics and cultural competence in recruitment, and the literature on algorithmic bias and human-AI collaboration, it proposes the Responsible AI Talent Management (RAITM) framework. The framework links three layers: what organizations should measure (competencies, including hard-to-observe soft skills), which data they may use (governed HR and workforce analytics), and how decisions are made (human-machine teaming with bias auditing and cultural competence). Governance principles drawn from the NIST AI Risk Management Framework connect the layers. The paper translates the framework into practical criteria for U.S. employers, universities and public workforce programs and sets out propositions for empirical testing. It contributes a human-centered model for using AI to identify, develop and retain talent while protecting workers' trust.