Managing Algorithmic Initiative in Autonomous Systems: A Governance Framework for Corporate Accountability

Collaborative Governance and Algorithmic Accountability: Regulating Risks and Restructuring Organizations for AI Integration

A normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation suggests that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance.

AI Governance Engineering: A Framework for Secure, Explainable, and Trustworthy Enterprise Artificial Intelligence Systems

This paper argues for a transition from AI Governance as Compliance to AI Governance Engineering , a systems-oriented discipline in which governance is embedded throughout the enterprise intelligence lifecycle, enabling enterprise intelligence systems that are secure, explainable, trustworthy, and governable by design.

From Algorithmic Decision-Making to Administrative Legitimacy: Rethinking Legal Accountability in Digital Governance

The growing use of algorithmic systems in public administration is transforming how public decisions are produced, justified, and contested. As automated and data-driven processes become embedded in administrative practice, traditional foundations of administrative authority, including human reasoning, procedural transparency, reason-giving, and institutional responsibility, are increasingly placed under strain. This shift creates new tensions between administrative efficiency and legal legitimacy, raising important questions about how accountability frameworks should adapt to opaque, distributed, and dynamic algorithmic decision-making processes. This paper examines the implications of algorithmic governance for administrative legitimacy and argues that existing legal accountability models are insufficient when decision-making authority is dispersed across public agencies, private technology providers, data infrastructures, and technical systems. Adopting a conceptual and normative analytical approach, the study identifies key challenges, including algorithmic opacity, fragmented responsibility, automation bias, limited contestability, and data-driven discrimination. In response, it develops a conceptual-normative accountability framework that links these challenges to corresponding legal risks and institutional responses. The framework emphasizes meaningful transparency, answerability, accessible contestation, effective human oversight, structured responsibility, auditability, and impact assessment. The analysis suggests that sustaining administrative legitimacy in the digital era requires more than regulatory adjustment. It requires lifecycle-based accountability mechanisms capable of reconnecting algorithmic decision-making with legality, procedural fairness, institutional responsibility, and public justification.

Agentic Artificial Intelligence in Business Decision-Making: A Framework for Human–AI Collaborative Governance and Strategic Value Creation

The rapid maturation of agentic artificial intelligence (AI) systems, capable of autonomously planning, executing, and adjusting multi-step actions with minimal human intervention, is reshaping how organizations arrive at strategic and operational decisions. Executive surveys indicate that a majority of business leaders now routinely rely on AI to inform decisions, and industry forecasts anticipate that a substantial share of business decisions will be augmented or automated by AI agents within the next several years. Yet the diffusion of decision-support and decision-making AI has outpaced the governance structures, skill sets, and trust mechanisms needed to deploy it responsibly. This paper develops and tests a conceptual framework, termed the Human–AI Collaborative Decision Governance (HACDG) model, that positions human agency, algorithmic transparency, and organizational trust as the three pillars mediating the relationship between AI adoption and decision quality. Using a mixed-methods design that combines a structured survey of 168 mid- and senior-level managers across manufacturing, financial services, retail, and information-technology sectors with semi-structured interviews of 14 senior executives, the study examines how the intensity of AI involvement in decision workflows interacts with governance maturity to influence perceived decision quality, decision speed, and employee confidence in outcomes. Findings suggest that AI involvement improves decision speed almost uniformly, but improves perceived decision quality only when paired with moderate-to-high governance maturity; in its absence, heavy AI reliance is associated with lower confidence and higher post-decision regret, mirroring patterns of automation complacency documented in other high-stakes domains. The paper contributes a validated, practitioner-usable framework for calibrating the degree of AI autonomy granted to decision workflows against the governance capacity of the organization, and offers implications for management education, internal audit, and enterprise risk functions responsible for overseeing algorithmic decision-making.

Authority and Responsibility Allocation in Human–AI Collaborative Decision-Making: Governance Mechanisms for Enterprises

The paper contributes a decision-level theory of enterprise AI governance and provides managers with an auditable method for allocating rights, responsibilities, evidence, and lifecycle controls.

From automation to ethical authority: governing AI-mediated decision-making in global digital trade