Research Article
AI-Enabled Workforce Governance in Public Healthcare:
An Applied Legitimacy-Based Model for Polish Hospital HR Systems
Dawid Krystian Prestini*
Issue:
Volume 1, Issue 2, June 2026
Pages:
64-68
Received:
25 February 2026
Accepted:
5 March 2026
Published:
14 March 2026
Abstract: Artificial Intelligence (AI) is increasingly transforming healthcare systems; however, its structured integration into public-sector human resource management (HRM) remains limited. Polish public hospitals face persistent workforce shortages, recruitment inefficiencies, and regulatory constraints under the General Data Protection Regulation (GDPR) and the EU Artificial Intelligence Act. Building upon prior conceptual work on legitimacy-preserving AI governance architecture, this study advances an applied AI-enabled workforce governance model tailored to public healthcare HR systems. Using a structured conceptual-analytical framework development approach grounded in Institutional Theory, the Resource-Based View, Strategic Human Capital Theory, and algorithmic governance literature, the Public AI-HR Governance Framework (P-AIHR) integrates five operational governance pillars supported by a 36-month implementation roadmap and structured risk matrix. Scenario modelling calibrated against OECD workforce indicators and illustrated through a 300-bed hospital simulation suggests plausible reductions in recruitment cycle time (20–30%), turnover rates (3–6 percentage points), and overtime variability (10–18%) under governance-controlled AI deployment. Rather than presenting empirical outcomes, the model provides analytically bounded projections intended to demonstrate the operational plausibility of governance-aligned AI integration. The study contributes a governance-calibrated framework for high-risk regulatory environments and advances the literature on AI-enabled HR transformation in public healthcare systems.
Abstract: Artificial Intelligence (AI) is increasingly transforming healthcare systems; however, its structured integration into public-sector human resource management (HRM) remains limited. Polish public hospitals face persistent workforce shortages, recruitment inefficiencies, and regulatory constraints under the General Data Protection Regulation (GDPR) ...
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Research Article
Algorithmic Management in AI-Driven Recruitment:
The AI Recruitment Governance Framework (ARGF) for Responsible AI Governance
Dawid Krystian Prestini*
Issue:
Volume 1, Issue 2, June 2026
Pages:
69-77
Received:
24 March 2026
Accepted:
1 April 2026
Published:
15 April 2026
DOI:
10.11648/j.sdai.20260102.12
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Abstract: The rapid integration of artificial intelligence (AI) into organizational recruitment processes is transforming how organizations identify, evaluate, and select job candidates. AI-driven recruitment systems enable firms to process large volumes of applicant data and increase the efficiency of hiring processes. However, the growing reliance on algorithmic decision systems also introduces significant governance challenges related to transparency, accountability, and candidate trust. This study examines AI-driven recruitment systems through the lens of algorithmic management and organizational governance. While existing research has primarily focused on technical performance and bias mitigation in automated hiring systems, relatively limited attention has been devoted to the governance structures required to manage algorithmic decision-making within organizational recruitment processes. Addressing this gap, the paper develops the AI Recruitment Governance Framework (ARGF), a conceptual model that conceptualizes AI-driven recruitment as a form of algorithmic management and proposes a responsible AI governance architecture based on three core dimensions: transparency, accountability, and human oversight. The framework provides a theoretical foundation for future empirical research. The framework highlights governance mechanisms that enable organizations to maintain managerial responsibility and ethical oversight while leveraging the efficiency gains offered by AI technologies. This study contributes to the literature by conceptualizing AI-driven recruitment as a form of algorithmic management and proposing a governance framework for responsible AI deployment in hiring processes. The study contributes to the emerging literature on responsible AI in human resource management by integrating insights from algorithmic management theory, HR governance research, and AI ethics scholarship. The findings suggest that organizations should adopt hybrid recruitment models in which algorithmic screening is complemented by structured human oversight and clear governance mechanisms. Such approaches can enable organizations to benefit from AI-enabled recruitment while preserving fairness, transparency, and legitimacy in hiring decisions.
Abstract: The rapid integration of artificial intelligence (AI) into organizational recruitment processes is transforming how organizations identify, evaluate, and select job candidates. AI-driven recruitment systems enable firms to process large volumes of applicant data and increase the efficiency of hiring processes. However, the growing reliance on algor...
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Research Article
Development of Requirements for Ensuring the Security of Artificial Intelligence Technologies
Issue:
Volume 1, Issue 2, June 2026
Pages:
78-86
Received:
27 February 2026
Accepted:
11 March 2026
Published:
16 April 2026
DOI:
10.11648/j.sdai.20260102.13
Downloads:
Views:
Abstract: This research article proposes comprehensive requirements for securing artificial intelligence systems, focusing on large language models (LLMs) in organizational settings. It addresses risks like unauthorized access, data leakage, service instability, and introduces "veracity" alongside the classic CIA triad (confidentiality, integrity, availability). The paper advocates a two-contour access model: an Open Contour (OC) for public LLMs and an Internal Contour (IC) for corporate/individual models, separated by a gateway for filtered interactions. A mandatory Request Control Module (RCM) monitors all user-LLM exchanges, enforcing limits on request frequency, size, and content to block sensitive data transfers. Secure training mandates dataset cleaning, depersonalization, and documentation, approved by security leads. Key Security Requirements: Corporate LLMs in IC with firewalls and access controls; public ones restricted to OC. Network/Access: Encrypted channels, user authentication, and logging for audits. Availability: Overload protection via RCM limits and reservations. Threat Analysis - drawing from OWASP Top 10 for LLMs (v1.1), it covers prompt injection (LLM01), data poisoning (LLM03), insecure output (LLM02 leading to XSS/SSRF), excess requests (LLM04/06), model theft (LLM10), and info disclosure. The dual-contour setup mitigates many via filtering and separation but notes limits against sophisticated attacks or human errors. Recommends DevSecOps pipelines integrating security across plan, develop, build, test, deploy, and monitor stages (e.g., SAST/DAST, SCA). Tables detail threat coverage, architecture strengths (e.g., centralized monitoring), and weaknesses (e.g., public model opacity). In conclusion, these practical guidelines enhance LLM resilience in enterprises, aligning with NIST AI RMF and ENISA practices, while calling for further automated vulnerability tools.
Abstract: This research article proposes comprehensive requirements for securing artificial intelligence systems, focusing on large language models (LLMs) in organizational settings. It addresses risks like unauthorized access, data leakage, service instability, and introduces "veracity" alongside the classic CIA triad (confidentiality, integrity, availabili...
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