Investigating the governance of AI systems in HR through four questions shows near-universal support for governance frameworks, with trust significantly associated with professional role, AI familiarity, and governance orientation.
Abstract
AI is rapidly transforming HRM, with organizations and universities adopting AI systems to recruit, evaluate, and analyse their workforces. While these technologies promise efficiency, flaws or biases in algorithms can affect individuals' careers and organizational performance, turning technological improvement into a matter of trust. Without proper governance, AI risks perpetuating bias and undermining accountability. Yet current research focuses largely on efficiency and cost savings, and the lack of integration between HRM and IT perspectives means organizations often implement AI without considering how governance structures and design decisions affect people. This study addresses that gap by investigating the governance of AI systems in HR through four questions: how governance structures shape trust, what role ethical frameworks play in mitigating bias, how system design influences transparency and accountability, and whether demographic and professional differences shape trust. Applying a socio-technical systems analysis to survey data from organizational and higher education participants, the findings show near-universal support for governance frameworks, with trust significantly associated with professional role, AI familiarity, and governance orientation.
Generative Artificial Intelligence (GenAI) is reconfiguring authority, accountability, and legitimacy in organizational leadership. This mixed-methods study integrates survey data from 542 leaders across health care, finance, government, education, and nonprofits with twenty-two interviews to examine how digital literacy and ethical infrastructure shape trust in AI-mediated decision systems. Findings show that 77% of respondents now incorporate GenAI into leadership decisions, reflecting a shift from hierarchical control to distributed, participatory orchestration. Ethics policies (β = .48, p < .001) and digital literacy (β = .42, p < .001) significantly predict trust in GenAI governance. Interview evidence demonstrates that confidence relies less on technical accuracy than on the institutionalization of ethics, transparent oversight, clear decision rights, bias audits, and mechanisms for contestation and redress. Sectoral contrasts illustrate how power shapes these safeguards, determining who is protected, who bears risk, and who can influence AI-enabled decisions. The study reframes leadership in the algorithmic age as the design of accountable, adaptive, and equitable decision architectures. Leaders who pair ethical governance with digital fluency are best positioned to sustain legitimacy and justice as GenAI becomes embedded in organizational life.
Ajmal Aminee· Organizational Cultures An I...· 0 citations
An integrative conceptual framework linking algorithmic workplace practices, ethical challenges, and employee well-being, moderated by organizational support, AI transparency, digital capability, and ethical leadership is contributed, alongside a practical framework for implementing sustainable, human-centered AI governance in digital workplaces.
Yulianto, Udin Saryono· Digital Theory, Culture &...· 0 citations
Overall, AI-assisted governance offers substantial potential to strengthen accountability and stakeholder trust when supported by robust ethical safeguards, transparency measures, and clearly defined responsibility structures.
M. Mar, Ing. Nikolai Fabian Sebastián Yucra Añazco, Delia Nieves Coaquira Pari· Journal of Organizational an...· 0 citations
This study aims to examine how institutional legitimacy and governance conditions shape public acceptance of artificial intelligence (AI)-based threat detection systems, demonstrating that technical accuracy is necessary but normatively insufficient for sustainable policy-oriented support.
A survey-based research design was used, with data from 510 valid respondents in South Korea. The study applied a two-step structural equation modeling approach comprising confirmatory factor analysis and structural path analysis, alongside bias-corrected bootstrap mediation analysis with 5,000 resamples.
Trust in government significantly predicts institutional legitimacy (ß = 0.660, p < 0.001), which, in turn, shapes both performance expectancy and perceived social deterrence. Direct path analysis confirmed 11 of 12 hypotheses, and bootstrap analysis verified significant indirect effects for all major antecedents. The model explains 84.8% of variance in behavioral intention (R² = 0.848). Ethical concern did not directly undermine legitimacy, indicating conditional rather than automatic normative resistance.
This research advances public-sector AI governance theory by positioning institutional legitimacy as a mediating filter and introducing perceived social deterrence as a policy-relevant cognitive mediator. It proves that governance readiness – not technical accuracy alone – determines durable policy support. Practically, it highlights the need for transparent oversight to secure public authorization. A limitation is its reliance on a South Korean general public sample, warranting future cross-national, multi-stakeholder comparative research.
Policymakers should complement technical development with governance mechanisms such as transparency, accountability and procedural safeguards.
The study underscores the societal importance of legitimacy-based governance when deploying high-risk AI systems affecting public safety.
This research advances public-sector AI governance theory by structurally positioning institutional legitimacy as a mediating evaluative filter between normative antecedents and cognitive expectations. It introduces perceived social deterrence as a policy-relevant cognitive mediator and provides empirical evidence that governance readiness – not technical accuracy alone – determines whether AI systems receive durable policy-oriented authorization.
The growing integration of Artificial Intelligence (AI) in Human Resource (HR) analytics has transformed
organizational decision-making processes, creating new opportunities for efficiency, accuracy, and strategic workforce
management. However, the increasing reliance on AI-supported HR systems has also raised concerns regarding employee
trust, transparency, and ethical governance. This study aims to analyse the influence of AI-driven HR analytics on employee
trust within organizations, examine the role of transparency in shaping employee trust toward AI-supported HR decisionmaking, and evaluate the contribution of ethical governance practices in strengthening employee trust in AI-driven HR
analytics. Grounded in HR Analytics Models, Trust Theory, the Organizational Trust Model of Mayer, Davis, and
Schoorman, and Stakeholder Theory, the study adopts a quantitative research approach using primary data collected from
employees across various organizations. The findings are expected to demonstrate that transparent AI processes and robust
ethical governance mechanisms positively influence employee trust and acceptance of AI-supported HR practices. The study
contributes to the growing literature on AI-enabled human resource management by highlighting the importance of
responsible AI implementation and trust-building mechanisms. The findings offer practical implications for organizations
seeking to balance technological innovation with ethical responsibility and employee confidence in AI-driven HR systems.
Manasa P., M. P· International Journal of Inn...· 1 citation
This conceptual article examines the conditions under which AI-assisted decisions in education and public governance can strengthen institutional capacity without displacing human judgement, agency, or accountability. It employs a purposive conceptual synthesis of interdisciplinary scholarship and legal and policy materials and compares governance approaches in the European Union, the Republic of Korea, and the United States with regard to legal force, risk classification, human oversight, transparency, contestability, and institutional capacity. Rather than treating the technical system alone as the unit of ethical analysis, the article focuses on the AI-assisted decision episode: the sequence through which data, model outputs, human judgement, and institutional authority combine to affect a learner, citizen, or community. On this basis, it develops a six-element governance framework comprising legitimate purpose and proportionality; explicit allocation of roles and responsibility; traceable data, evidence, and uncertainty; competent human oversight and calibrated reliance; stakeholder participation, contestability, and redress; and continuous monitoring, audit, and institutional learning. Applied to educational assessment and public-service decisions, the framework demonstrates that a nominal human-in-the-loop is insufficient unless reviewers possess the competence, time, authority, alternative evidence, and records necessary to challenge model outputs. The article’s contribution lies in connecting legal safeguards, organisational capacity, and cognitive risks within a process-based model of human-centred AI. The framework is conceptual and requires empirical validation. Human-centred AI ultimately depends not only on technical accuracy but also on a decision architecture that preserves agency, provides effective remedies, and keeps responsibility visible.