Jul 2026· Scholedge International Journal of Business Policy & Governance ISSN 2394-3351· 0 citations
TL;DR
It is argued that accountability for algorithmic harm cannot rest on a single actor or a single governance layer, and responsibility instead needs to be distributed across the people and functions that design, approve, deploy, and supervise an AI system.
Abstract
Corporate boards now sit atop decision architectures that no longer belong to them alone. Machine learning systems screen credit applications, price insurance risk, recommend mergers, flag fraud, and increasingly shape the strategic judgments that directors and executives once reached through experience and deliberation. When one of these systems produces a harmful, discriminatory, or commercially damaging outcome, existing governance doctrine struggles to identify who answers for it. This paper examines the widening gap between algorithmic decision-making and the accountability structures built for human agents. Drawing on agency, stakeholder, stewardship, institutional, and resource dependence theories, together with enterprise risk management and responsible AI scholarship, the paper argues that accountability for algorithmic harm cannot rest on a single actor or a single governance layer. Responsibility instead needs to be distributed across the people and functions that design, approve, deploy, and supervise an AI system, with each layer answerable for a distinct category of failure: design flaws, oversight lapses, deployment misjudgment, and monitoring neglect. The paper's central contribution is a multi-level Corporate AI Accountability Governance Framework that assigns differentiated responsibilities to the board, executive leadership, a dedicated AI governance committee, risk management, internal audit, technology teams, external vendors, and regulators. The framework is built around a feature that conventional governance controls were never designed to handle: AI systems continue to change after deployment, so a one-time approval cannot substitute for ongoing supervision. The paper closes with practical implications for boards preparing for algorithmic oversight, a comparative reading of regulatory expectations across the European Union, the United States, the United Kingdom, and selected Asia-Pacific economies, and a research agenda for scholars working on the next phase of digital corporate governance.
Agentic AI is gaining acceptance in asset management, but governance has not kept pace: 88\% of surveyed finance professionals report no operational governance framework for agentic AI, and only 24 of 75 large U.S. money managers disclosing AI use in Form ADV filings report a formal governance policy. We argue this gap is architectural: governance built for static validation does not survive continuously retrained agentic policies. We propose a four-layer framework (Policy, Engineering, Composition, Systemic) grounded in two distinct kinds of evidence, kept explicitly separate: two calibrated synthetic illustrations (a regret-covariance drift monitor; a crowding simulation showing joint drawdown risk rising from 39.2\% to 79.3\%), and three real, documented cases (a deployed LLM-embedding trading strategy, a \$45 billion discretionary fund's forced-deleveraging blowup, and a tribunal ruling holding an airline liable for its chatbot). The synthetic examples demonstrate computability from observable data; the cases demonstrate that the failure modes are not hypothetical. We provide a 90-day implementation sequence spanning trading and payments/customer-facing systems.
How is American government being reconfigured when public authority is exercised through code, platforms, and data systems as much as through statutes, agencies, and courts? Algorithmic Governance and Power: How AI is Reshaping American Democracy shows how decisions about speech, benefits, policing, immigration, and elections are increasingly shaped by technological infrastructure and private governance operating alongside - and often inside - the state.
Nancy S. Lind argues that public power now routinely operates through a government–private sector partnership she calls the “hybrid state.” Through case-based analysis of algorithmic screening, risk assessment tools, content moderation, and microtargeted political advertising, she traces how legal and policy judgments become embedded in system design - often in ways that are hard to see, contest, or appeal through conventional administrative and judicial channels.
Written for scholars, students, and practitioners navigating a post-Chevron landscape, the book offers a practical framework for judging accountability when decision-making is automated or outsourced, clarifying responsibilities for transparency, due process, and effective oversight.
This Article argues that legitimacy is an autonomous regulatory objective, distinct from alignment and not secured by it, which seats consequential AI rule-setting in venues a polity already treats as authoritative.
Artificial intelligence is increasingly becoming a governing medium through which institutions classify persons, allocate opportunities, structure work, produce knowledge and mediate public trust. Current AI governance frameworks emphasise risk classification, technical assurance, transparency, accountability and human oversight. These instruments are necessary, but they remain incomplete when algorithmic decisions reshape the meaning of agency, dignity, responsibility and social recognition. This paper develops a humanities-based framework for algorithmic governance suitable for law, management and public life. Using an interdisciplinary conceptual methodology, it synthesises legal-policy frameworks, AI ethics scholarship, management studies and contemporary philosophical work on ontological instability, AI stakeholder recognition and moral responsibility. The paper argues that algorithmic governance should not be assessed only by whether systems are accurate, explainable or compliant, but also by whether affected persons retain interpretive agency, contestatory power, relational recognition and meaningful participation in institutional life. It proposes the Human Agency Impact Matrix, a six-dimensional framework that evaluates algorithmic systems through interpretability, contestability, relational accountability, dignity preservation, participatory design and institutional reversibility. The analysis shows that risk-based regulation is strongest when complemented by humanistic assessment of how AI changes roles, identities, vulnerabilities and obligations. The paper concludes that responsible AI governance must be understood as a cultural and institutional practice: a way of preserving human agency within socio-technical systems that increasingly act before, beside and sometimes instead of human judgment.
K. Tan· International Journal of Law...· 0 citations
Agentic artificial intelligence (AI) alters the governance problem because model outputs can become multi-step actions with financial, legal, informational, and social consequences. Existing governance instruments widely endorse human oversight, transparency, accountability, and redress, yet they do not consistently specify what people must remain able to do when agency is delegated to an AI system. This qualitative study conducts a comparative document analysis of ten influential governance instruments issued by UNESCO, the OECD, the European Union, the Council of Europe, the United States National Institute of Standards and Technology, the United Kingdom, the Group of Seven, and Singapore. Provision-level coding, abductive pattern analysis, negative-case examination, and a cross-framework coverage matrix identify six themes: human-centric convergence with operational divergence; oversight without empowerment; late-stage contestability; a reversibility deficit; fragmented accountability; and temporal-capability asymmetry. The paper develops cognitive sovereignty as the practically exercisable capacity to understand, authorize, interrupt, contest, restore, and assign responsibility for consequential processes delegated to AI. It then proposes the CLEAR² framework, comprising Comprehension, Legitimate authorization, Effective intervention, Appeal and contestation, Restoration and reversibility, and Responsibility and remedy. CLEAR² integrates ex ante, runtime, and ex post controls and treats the weakest capability as a constraint on meaningful human control. The study advances AI governance theory by shifting the unit of analysis from human presence to preserved agency, while offering organizations a maturity model, lifecycle control architecture, and audit questions for responsible agentic deployment.
K. Tan· Open Access Journal of Multi...· 0 citations
Autonomous and agentic AI systems are turning information technology from a passive automation tool into an active decision-making proxy. Traditional human-centered liability models fall short once AI makes adaptive, high-impact decisions. This conceptual study analyzes the accountability gap that opens when strategic goals are delegated to algorithmic agents. Drawing on three cases (the Uber autonomous vehicle accident, the 2010 Flash Crash, and the COMPAS judicial risk assessment system), it develops the Dynamic Authority Delegation Model (DADM), which distributes responsibility among human strategic intent, algorithmic operational execution, and institutional oversight. By moving from individual blame to organizational governance, the study contributes to the IT management literature and offers a practical framework for corporate accountability, human oversight, algorithmic auditing, and responsible AI governance.
Mustafa Kaya· Kamu Yönetimi ve Teknoloji D...· 0 citations