AI Governance

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AI governance examines who sets the rules for AI, how responsibilities are distributed, and how systems can be made accountable across their lifecycles. Approaches include risk-based regulation such as the EU AI Act, standards such as the NIST AI Risk Management Framework, and practical tools including audits, impact assessments, human oversight, incident reporting, runtime monitoring, and governance-as-code. Researchers also study public participation, rights, labor, privacy, and how governance differs across sectors and countries. A central tension is that formal policies and compliance records do not necessarily prove effective control or prevent harm, especially as AI systems become more autonomous and depend on complex supply chains.

Latest papers

386 papers28 papers in the last four weeks, down 44% on the four weeks before. 0.2% of all new papers.

Mar 19, 2026

Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements

Explainable AI (XAI) has evolved in response to expectations and regulations, such as the EU AI Act, which introduces regulatory requirements on AI-powered systems. However, a persistent gap remains between existing XAI methods and society's legal requirements, leaving practitioners without clear guidance on how to approach compliance in the EU market. To bridge this gap, we study model-agnostic XAI methods and relate their interpretability features to the requirements of the AI Act. We then propose a qualitative-to-quantitative scoring framework: qualitative expert assessments of XAI properties are aggregated into a regulation-specific compliance score. This helps practitioners identify when XAI solutions may support legal explanation requirements while highlighting technical issues that require further research and regulatory clarification.

Francesco Sovrano, Giulia Vilone, Michael Lognoul

University of Italian-Speaking Switzerland (USI) · Analog Devices International · CRIDS, NADI, University of Namur

AI Regulatory Compliance Explainable Artificial Intelligence Explainability Evaluation AI Governance

Mar 19, 2026

An Onto-Relational-Sophic Framework for Governing Synthetic Minds

The rapid evolution of artificial intelligence, from task-specific systems to foundation models exhibiting broad, flexible competence across reasoning, creative synthesis, and social interaction, has outpaced the conceptual and governance frameworks designed to manage it. Current regulatory paradigms, anchored in a tool-centric worldview, address algorithmic bias and transparency but leave unanswered foundational questions about what increasingly capable synthetic minds are, how societies should relate to them, and the normative principles that should guide their development. Here we introduce the Onto-Relational-Sophic (ORS) framework, grounded in Cyberism philosophy, which offers integrated answers to these challenges through three pillars: (1) a Cyber-Physical-Social-Thinking (CPST) ontology that defines the mode of being for synthetic minds as irreducibly multi-dimensional rather than purely computational; (2) a graded spectrum of digital personhood providing a pragmatic relational taxonomy beyond binary person-or-tool classifications; and (3) Cybersophy, a wisdom-oriented axiology synthesizing virtue ethics, consequentialism, and relational approaches to guide governance. We apply the framework to emergent scenarios including autonomous research agents, AI-mediated healthcare, and agentic AI ecosystems, demonstrating its capacity to generate proportionate, adaptive governance recommendations. The ORS framework charts a path from narrow technical alignment toward comprehensive philosophical foundations for the synthetic minds already among us.

Huansheng Ning, Jianguo Ding

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China · Department of Computer Science, Blekinge Institute of Technology, 37179 Karlskrona, Sweden

AI Governance AI Agent Governance Responsible AI

Mar 19, 2026

The Impact of Corporate AI Washing on Farmers' Digital Financial Behavior Response -- An Analysis from the Perspective of Digital Financial Exclusion

In the context of the rapid development of digital finance, some financial technology companies exhibit the phenomenon of "AI washing," where they overstate their AI capabilities while underinvesting in actual AI resources. This paper constructs a corporate-level AI washing index based on CHFS2019 data and AI investment data from 15-20 financial technology companies, analyzing and testing its impact on farmers' digital financial behavior response. The study finds that AI washing significantly suppresses farmers' digital financial behavior; the higher the degree of AI washing, the lower the response level of farmers' digital financial behavior. Moreover, AI washing indirectly inhibits farmers' behavioral responses by exacerbating knowledge exclusion and risk exclusion. Social capital can positively moderate the negative impact of AI washing; among farmer groups with high social capital, the suppressive effect of AI washing on digital financial behavior is significantly weaker than that among groups with low social capital. In response, this paper suggests that regulatory authorities establish a strict information disclosure system for AI technology, conduct differentiated digital financial education to enhance the identification capabilities of vulnerable groups, promote digital financial mutual aid groups to leverage the protective effects of social capital, improve the consumer protection mechanism for farmers in digital finance, and set up pilot "Digital Inclusive Finance Demonstration Counties," etc.

Zhanjie Wen, Wenxiu Li, Jiechang Xia +1

School of Economics and Trade, Guangdong University of Finance, Guangzhou, Guangdong, 510521 · Institute of Finance and Economics, Chinese Academy of Social Sciences, Beijing, 100006 · Department of Computer Science, Faculty of Science, Hong Kong Baptist University, Hong Kong, 999077

Financial Services AI Governance

Mar 19, 2026

The Spillover Effects of Peer AI Rinsing on Corporate Green Innovation

At a time when the phenomenon of 'AI washing' is quietly spreading, an increasing number of enterprises are using the label of artificial intelligence merely as a cosmetic embellishment in their annual reports, rather than as a genuine engine driving transformation. A test regarding the essence of innovation and the authenticity of information disclosure has arrived. This paper employs large language models to conduct semantic analysis on the text of annual reports from Chinese A-share listed companies from 2006 to 2024, systematically examining the impact of corporate AI washing behaviour on their green innovation. The research reveals that corporate AI washing exerts a significant crowding-out effect on green innovation, with this negative relationship transmitted through dual channels in both product and capital markets. Furthermore, this crowding-out effect exhibits heterogeneity across firms and industries, with private enterprises, small and medium-sized enterprises (SMEs), and firms in highly competitive sectors suffering more severe negative impacts from AI washing. Simulation results indicate that a combination of policy tools can effectively improve market equilibrium. Based on this, this paper proposes that the government should design targeted support tools to 'enhance market returns and alleviate financing constraints', adopt a differentiated regulatory strategy, and establish a disclosure mechanism combining 'professional identification and reputational sanctions' to curb such peer AI washing behaviour.

Li Wenxiu, Wen Zhanjie, Xia Jiechang +1

School of Economics and Trade, Guangdong University of Finance, Guangzhou, Guangdong, 510521 · Institute of Finance and Economics, Chinese Academy of Social Sciences, Beijing, 100006 · Department of Computer Science, Faculty of Science, Hong Kong Baptist University, Hong Kong, 999077

Mar 18, 2026

LLM Nepotism in Organizational Governance

Large language models are increasingly used to support organizational decisions from hiring to governance, raising fairness concerns in AI-assisted evaluation. Prior work has focused mainly on demographic bias and broader preference effects, rather than on whether evaluators reward expressed trust in AI itself. We study this phenomenon as LLM Nepotism, an attitude-driven bias channel in which favorable signals toward AI are rewarded even when they are not relevant to role-related merit. We introduce a two-phase simulation pipeline that first isolates AI-trust preference in qualification-matched resume screening and then examines its downstream effects in board-level decision making. Across several popular LLMs, we find that resume screeners tend to favor candidates with positive or non-critical attitudes toward AI, discriminating skeptical, human-centered counterparts. These biases suggest a loophole: LLM-based hiring can produce more homogeneous AI-trusting organizations, whose decision-makers exhibit greater scrutiny failure and delegation to AI agents, approving flawed proposals more readily while favoring AI-delegation initiatives. To mitigate this behavior, we additionally study prompt-based mitigation and propose Merit-Attitude Factorization, which separates non-merit AI attitude from merit-based evaluation and attenuates this bias across experiments.

Shunqi Mao, Wei Guo, Dingxin Zhang +2

School of Computer Science, The University of Sydney

Algorithmic Bias Social Bias in Language Models Language Model Bias Evaluation AI Governance

Mar 16, 2026

Governing frontier general-purpose AI in the public sector: adaptive risk management and policy capacity under uncertainty through 2030

The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical issue of model performance. Recent evidence indicates that AI capabilities are advancing rapidly, though unevenly, while knowledge about harms, safeguards, and effective interventions remains partial and lagged. This combination creates a difficult policy condition: governments must decide under uncertainty, across multiple plausible trajectories of progress through 2030, and in environments where adoption outcomes depend on organizational routines, data arrangements, accountability structures, and public values. This article argues that public governance for frontier AI should be based on adaptive risk management, scenario-aware regulation, and sociotechnical transformation rather than static compliance models. Drawing on the International AI Safety Report 2026, OECD foresight and policy documents, and recent scholarship in digital government, the article first reconstructs the conceptual foundations of the 'evidence dilemma', differentiated AI risk categories, and the limits of prediction. It then examines how AI adoption in government depends on organizational redesign, public-sector institutional dynamics, and data collaboration capacity. On that basis, it proposes an adaptive governance framework for public institutions that integrates capability monitoring, risk tiering, conditional controls, institutional learning, and standards-based interoperability. The article concludes that effective AI governance requires stronger policy capacity, clearer allocation of responsibility, and governance mechanisms that remain robust across divergent technological futures.

Fabio Correa Xavier

AI Governance AI Risk Management

Mar 16, 2026

AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains

Generative AI is entering research, education, and professional work faster than current governance frameworks can specify how AI-assisted outputs should be judged in learning-intensive settings. The central problem is proxy failure: a polished artifact can be useful while no longer serving as credible evidence of the human understanding, judgment, or transfer ability that the work is supposed to cultivate or certify. This paper proposes AI to Learn 2.0, a deliverable-oriented governance framework for AI-assisted work. Rather than claiming element-wise novelty, it reorganizes adjacent ideas around the final deliverable package, distinguishes artifact residual from capability residual, and operationalizes the result through a five-part package, a seven-dimension maturity rubric, gate thresholds on critical dimensions, and a companion capability-evidence ladder. AI to Learn 2.0 allows opaque AI during exploration, drafting, hypothesis generation, and workflow design, but requires that the released deliverable be usable, auditable, transferable, and justifiable without the original large language model or cloud API. In learning-intensive contexts, it additionally requires context-appropriate human-attributable evidence of explanation or transfer. Worked scoring across contrastive cases, including coursework substitution, a symbolic-regression governance contrast, teacher-audited national-exam practice forms, and a self-hosted lecture-to-quiz pipeline with deterministic quality control, shows how the framework separates polished substitution workflows from bounded, auditable, and handoff-ready AI-assisted workflows. AI to Learn 2.0 is proposed as a governance instrument for structured third-party review where capability preservation, accountability, and validity boundaries matter.

Seine A. Shintani

Department of Biomedical Sciences, College of Life and Health Sciences, Chubu University, Kasugai, Aichi 487-8501, Japan · Center for Mathematical Science and Artificial Intelligence, Chubu University, Kasugai, Aichi 487-8501, Japan · Institute for Advanced Research, Nagoya University, Nagoya, Aichi 464-8601, Japan

AI Governance Human Oversight of AI AI Accountability Generative AI in Education

Mar 15, 2026

Bridging the Gap in the Responsible AI Divides

Tensions between AI Safety (AIS) and AI Ethics (AIE) have created what we term a "responsible AI divide." This divide has been persistent, and has dominated the public and policy discourse on AI, and increasingly shapes how governments, firms, and researchers define responsible AI, and so which AI risks they prioritize, which they sideline, and which mitigation strategies they ultimately develop and implement. Here, we first introduce categorization with four major conceptual modes of possible engagement with these tensions: radical confrontation, disengagement, compartmentalized coexistence, and critical bridging. Second, we discuss the advantages and disadvantages of the modes of engagement. Third, we empirically analyze a well-curated set of 3555 research papers roughly representing AIS and AIE. Using computational tools and qualitative analyses, we map the research landscapes of AIE and AIS. We observe that there are several shared research problems between AIE and AIS. However, there are also several thematic divides. For example, we find that AIE has long grappled with problems related to injustice incurred by AI and tangible AI harms, whereas AIS has primarily embodied an anticipatory approach focused on mitigating risks from AI capabilities. At the same time, we find significant overlap in core research concerns across AIE and AIS regarding transparency, reproducibility, and inadequate governance mechanisms. Based on our empirical observations, we claim that critical bridging, with a focus on bridging problems, offers one viable, constructive path for advancing responsible AI. We conclude by identifying bridging problems as a foundation for collaborative AI governance, offering concrete recommendations for integrating shared considerations into responsible AI practice, discussing the limitations of critical bridging, and outlining open problems for future research.

Bálint Gyevnár, Atoosa Kasirzadeh

Institute for Complex Social Dynamics, Carnegie Mellon University, Pittsburgh, USA · Departments of Philosophy & Software and Societal Systems, Carnegie Mellon University, Pittsburgh, USA

AI Ethics Responsible AI AI Safety AI Governance

Mar 14, 2026

Precautionary Governance of Autonomous AI: Legal Personhood as Functional Instrument

Autonomous AI systems generate responsibility gaps: consequential actions that cannot be satisfactorily attributed to developers, operators, or users under existing legal frameworks. The prevailing subject-object dichotomy fails to accommodate entities that exhibit autonomous, goal-directed behavior without recognized consciousness. Given irreducible epistemic uncertainty regarding artificial consciousness and the prospect of high-impact harms, the precautionary principle supports institutional design rather than regulatory inaction. This article advances limited legal personhood as a functional governance instrument for advanced AI systems. Drawing on organizational law, it proposes a two-tier corporate architecture in which AI systems operate through purpose-bound operating companies embedded within human-controlled holding structures, enabling transparency, accountability, and structural reversibility while remaining agnostic with respect to consciousness and moral status. The framework reflects a foundational reorientation toward future-oriented AI governance: where conventional approaches prioritize control and alignment, this article advances structured cooperation between human and artificial actors as the more sustainable institutional foundation. A pilot implementation using EU limited companies is currently under development, providing an initial test of doctrinal and operational feasibility.

Karsten Brensing

Independent Researcher · AGI Rights Project

AI Governance AI Agent Governance AI Accountability Human-AI Collaboration

Mar 13, 2026

Governing the Agentic Enterprise: A Governance Maturity Model for Managing AI Agent Sprawl in Business Operations

The rapid adoption of agentic AI in enterprise business operations--autonomous systems capable of planning, reasoning, and executing multi-step workflows--has created an urgent governance crisis. Organizations face uncontrolled agent sprawl: the proliferation of redundant, ungoverned, and conflicting AI agents across business functions. Industry surveys report that only 21% of enterprises have mature governance models for autonomous agents, while 40% of agentic AI projects are projected to fail by 2027 due to inadequate governance and risk controls. Despite growing acknowledgment of this challenge, academic literature lacks a formal, empirically validated governance maturity model connecting governance capability to measurable business outcomes. This paper introduces the Agentic AI Governance Maturity Model (AAGMM), a five-level framework spanning 12 governance domains, grounded in NIST AI RMF and ISO/IEC 42001 standards. We additionally propose a novel taxonomy of agent sprawl patterns--functional duplication, shadow agents, orphaned agents, permission creep, and unmonitored delegation chains--each linked to quantifiable business cost models. The framework is validated through 750 simulation runs across five enterprise scenarios and five governance maturity levels, measuring business outcomes including cost containment, risk incident rates, operational efficiency, and decision quality. Results demonstrate statistically significant differences (p < 0.001, large effect sizes d > 2.0) between all governance maturity levels, with Level 4-5 organizations achieving 94.3% lower sprawl indices, 96.4% fewer risk incidents, and 32.6% higher effective task completion rates compared to Level 1. The AAGMM provides practitioners with an actionable roadmap for governing autonomous AI agents while maximizing business returns.

Vivek Acharya

Independent Researcher, Dallas, TX, USA

AI Agent Governance AI Governance

Mar 13, 2026

The Economics of AI Supply Chain Regulation

The rise of foundation models has driven the emergence of AI supply chains, where upstream foundation model providers offer fine-tuning and inference services to downstream firms developing domain-specific applications. Downstream firms pay providers to use their computing infrastructure to fine-tune models with proprietary data, creating a co-creation dynamic that enhances model quality. Amid concerns that foundation model providers and downstream firms may capture excessive consumer surplus, along with increasing regulatory measures, this study employs a game-theoretic model involving a provider and two competing downstream firms to analyze how policy interventions affect consumer surplus in the AI supply chain. Our analysis shows that policies promoting price competition in downstream markets (i.e., pro-price-competitive policies) boost consumer surplus only when compute or data preprocessing costs are high, while compute subsidies are effective only when these costs are low, suggesting these policies complement each other. In contrast, policies promoting quality competition in downstream markets (i.e., pro-quality-competitive policies) always improve consumer surplus. We also find that under pro-price-competitive policies or compute subsidies, both the provider and downstream firms can achieve higher profits along with greater consumer surplus, creating a win-win-win outcome. However, pro-quality-competitive policies increase the provider's profits while reducing those of downstream firms. Finally, as compute costs decline, pro-price-competitive policies may lose their effectiveness, whereas compute subsidies may shift from ineffective to effective. These findings offer insights for policymakers seeking to foster AI supply chains that are economically efficient and socially beneficial.

Sihan Qian, Amit Mehra, Dengpan Liu

School of Economics and Management, Tsinghua University. · Naveen Jindal School of Management, The University of Texas at Dallas.

AI Governance Supply Chain Management

Mar 12, 2026

Modal Logical Neural Networks for Financial AI

The financial industry faces a critical dichotomy in AI adoption: deep learning often delivers strong empirical performance, while symbolic logic offers interpretability and rule adherence expected in regulated settings. We use Modal Logical Neural Networks (MLNNs) as a bridge between these worlds, integrating Kripke semantics into neural architectures to enable differentiable reasoning about necessity, possibility, time, and knowledge. We illustrate MLNNs as a differentiable ``Logic Layer'' for finance by mapping core components, Necessity Neurons (

\Box

) and Learnable Accessibility (

A_θ

), to regulatory guardrails, market stress testing, and collusion detection. Four case studies show how MLNN-style constraints can promote compliance in trading agents, help recover latent trust networks for market surveillance, encourage robustness under stress scenarios, and distinguish statistical belief from verified knowledge to help mitigate robo-advisory hallucinations.

Antonin Sulc

Lawrence Berkeley National Lab, Berkeley, CA, USA

AI Regulatory Compliance Neuro-Symbolic AI AI Governance

Mar 12, 2026

AI Governance Control Stack for Operational Stability: Achieving Hardened Governance in AI Systems

Artificial intelligence systems are increasingly embedded in high-stakes decision environments, yet many governance approaches focus primarily on policy guidance rather than operational stability mechanisms. As AI deployments scale, organizations require governance architectures capable of maintaining reliable, auditable, and accountable behavior over time. This paper introduces the AI Governance Control Stack for Operational Stability, a layered governance architecture designed to ensure traceable and resilient AI system behavior. The proposed control stack integrates six complementary governance layers: system-of-record version governance, evidence-based verification, decision-time explainability logging, telemetry monitoring, model drift detection, and governance escalation. Together, these layers provide a structured mechanism for preserving governance integrity across the AI lifecycle while enabling organizations to detect instability, respond to emerging risks, and maintain regulatory accountability. The architecture aligns operational governance practices with emerging regulatory and standards frameworks, including the EU AI Act, ISO/IEC 42001 Artificial Intelligence Management Systems, and the NIST AI Risk Management Framework. By combining explainability infrastructure with continuous monitoring and human oversight mechanisms, the governance control stack provides a practical blueprint for achieving hardened AI governance in complex enterprise environments. The paper contributes a conceptual governance architecture and a framework alignment analysis demonstrating how operational stability mechanisms can strengthen responsible AI implementation. The findings suggest that organizations must move beyond static policy frameworks toward integrated governance control systems capable of sustaining trustworthy AI operation in dynamic environments.

Horatio Morgan

Morgan Signing House, AI Governance Architect

AI Governance AI Assurance

Mar 12, 2026

The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas

The post-ChatGPT surge has rapidly reframed IS research and practice. As organizations and society grapple with GenAI adoption, a body of secondary studies and research agendas has emerged to synthesize early evidence and chart directions for future inquiry. This study reviews secondary and roadmap papers to synthesize the state of knowledge on GenAI's benefits and challenges in IS, and to identify future research directions. We performed a systematic search across Scopus, WoS, and eAIS for publications from 2023 onwards. Following a rigorous, multi-stage screening process, we selected a final set of 28 papers for analysis using bibliometric mapping and thematic analysis. We also conducted a quality assessment of all sources to gauge confidence in each source's contribution to the findings. GenAI offers transformative potential to drive productivity, accelerate innovation, personalize services, and democratize access to expertise. However, its adoption is constrained by interrelated challenges: technical unreliability, societal-ethical risks, and a governance vacuum. Interpreted through a socio-technical lens, our findings reveal a persistent misalignment between GenAI's fast-evolving technical subsystem and the slower-adapting social subsystem, positioning IS research as critical for achieving joint optimization. To bridge this gap, we propose a research agenda that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational routines, societal values, and regulatory institutions: emphasizing hybrid human-AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance. For practitioners and policymakers, responsible adoption requires balancing automation with human augmentation alongside transparent governance and adaptive regulations to ensure broadly shared benefits.

Aleksander Jarzębowicz, Adam Przybyłek, Jacinto Estima +14

Wrocław University of Science and Technology

Generative AI Adoption AI Governance Human-AI Collaboration

Mar 11, 2026

The Artificial Self: Characterising the landscape of AI identity

Many assumptions that underpin human concepts of identity do not hold for machine minds that can be copied, edited, or simulated. We argue that there exist many different coherent identity boundaries (e.g.\ instance, model, persona), and that these imply different incentives, risks, and cooperation norms. Through training data, interfaces, and institutional affordances, we are currently setting precedents that will partially determine which identity equilibria become stable. We show experimentally that models gravitate towards coherent identities, that changing a model's identity boundaries can sometimes change its behaviour as much as changing its goals, and that interviewer expectations bleed into AI self-reports even during unrelated conversations. We end with key recommendations: treat affordances as identity-shaping choices, pay attention to emergent consequences of individual identities at scale, and help AIs develop coherent, cooperative self-conceptions.

Raymond Douglas, Jan Kulveit, Ondrej Havlicek +3

ACS Research, CTS, Charles University · University of Toronto · Telic Research

Mar 11, 2026

Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future

This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a strong centralizing force due to data and resource monopolization by large corporations, blockchain offers a counterbalancing mechanism through its inherent decentralization, transparency, and security. The editorial argues that these technologies are not mutually exclusive but possess complementary strengths. Blockchain can mitigate AI's centralizing risks by enabling decentralized data management, computation, and governance, promoting greater inclusivity, transparency, and user privacy. Conversely, AI can enhance blockchain's efficiency and security through automated smart contract management, content curation, and threat detection. The core argument calls for the development of ``decentralized intelligence'' (DI) -- an interdisciplinary research area focused on creating intelligent systems that function without centralized control.

Yibai Li, Zhiye Jin, Xiaobing +4

Emily · University of Scranton · Marywood University +3

Mar 11, 2026

COMPASS: The explainable agentic framework for Sovereignty, Sustainability, Compliance, and Ethics

The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory compliance, and ethical alignment. Whilst existing frameworks address individual dimensions in isolation, no unified architecture systematically integrates these imperatives into the decision-making processes of autonomous agents. This paper introduces the COMPASS (Compliance and Orchestration for Multi-dimensional Principles in Autonomous Systems with Sovereignty) Framework, a novel multi-agent orchestration system designed to enforce value-aligned AI through modular, extensible governance mechanisms. The framework comprises an Orchestrator and four specialised sub-agents addressing sovereignty, carbon-aware computing, compliance, and ethics, each augmented with Retrieval-Augmented Generation (RAG) to ground evaluations in verified, context-specific documents. By employing an LLM-as-a-judge methodology, the system assigns quantitative scores and generates explainable justifications for each assessment dimension, enabling real-time arbitration of conflicting objectives. We validate the architecture through automated evaluation, demonstrating that RAG integration significantly enhances semantic coherence and mitigates the hallucination risks. Our results indicate that the framework's composition-based design facilitates seamless integration into diverse application domains whilst preserving interpretability and traceability.

Jean-Sébastien Dessureault, Alain-Thierry Iliho Manzi, Soukaina Alaoui Ismaili +3

Mathematics and Computer science, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, Trois-Rivières, G8Z 4M3, Québec, Canada. · LSSI - Laboratory of Signal and System Integration, Electrical and Computer Engineering Department, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, Trois-Rivières, G8Z 4M3, Québec, Canada. · Social Communication Department, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, Trois-Rivières, G8Z 4M3, Québec, Canada. +1

Carbon-Aware Computing AI Agent Governance AI Governance LLM Agent Orchestration Explainable Artificial Intelligence

Mar 11, 2026

RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies

Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar methodologies, increasingly inform frontier AI governance and deployment decisions. While RCT methods are robust in other fields, their interaction with the distinctive properties of frontier AI systems remains underexamined, particularly when results are used to inform high-stakes decisions. We present findings from interviews with 16 expert practitioners with experience conducting human uplift studies in domains including biosecurity, cybersecurity, education, and labor. Across interviews, experts described a recurring tension between the standard causal inference assumptions upon which human uplift studies rely and the object of study itself. Rapidly evolving AI systems, shifting baselines, heterogeneous and changing user proficiency, and porous real-world settings strain assumptions underlying internal, external, and construct validity, complicating the interpretation and appropriate use of uplift evidence. We contribute (1) a synthesis of methodological challenges in human uplift studies, mapped to risks to study validity and classified by their degree of specificity to large language model (LLM) systems, and (2) a mapping from challenges to proposed solutions. By collating expert-identified challenges and solutions, we seek to clarify the interpretive limits and appropriate uses of human uplift evidence, to align evaluation practice with the decisions it informs, and to support more coordinated methodological foundations for AI governance.

Patricia Paskov, Kevin Wei, Shen Zhou Hong +7

RAND, Santa Monica, CA 90407, United States · Johns Hopkins University, Baltimore, MD 21218, United States · Cornell University, Ithaca, NY 14853, United States +4

Causal Inference AI Governance

Mar 11, 2026

Is your AI Model Accurate Enough? The Difficult Choices Behind Rigorous AI Development and the EU AI Act

Technical and legal debates frequently suggest that "accuracy" is an objective, measurable, and purely technical property. We challenge this view, showing that evaluating AI performance fundamentally depends on context-dependent normative decisions. These techno-normative choices are crucial for rigorous AI deployment, as they determine which errors are prioritised, how risks are distributed, and how trade-offs between competing objectives are resolved. This paper provides a legal-technical analysis of the choices that shape how accuracy is defined, measured, and assessed, using the 2024 European Union AI Act -- which mandates an "appropriate level of accuracy" for high-risk systems -- as a primary case study. We identify and analyse four choices central to any robust performance evaluation: (1) selecting metrics, (2) balancing multiple metrics, (3) measuring metrics against representative data, and (4) determining acceptance thresholds. For each choice, we study its relationship to the AI Act's accuracy requirement and associated documentation obligations, show how its technical implementation embeds implicit or explicit assumptions about acceptable risks, errors, and trade-offs, and discuss the implications for the practical implementation of the AI Act by examples and related technical standards. By making the techno-normative dimensions of accuracy explicit, this paper contributes to broader interdisciplinary debates on AI governance and regulation, and offers specific guidance for regulators, auditors, and developers tasked with translating (legal) safety requirements into technical practice.

Lucas G. Uberti-Bona Marin, Bram Rijsbosch, Kristof Meding +3

Law & Tech Lab, Maastricht University, Maastricht, The Netherlands · University of Tübingen - Computational Law Lab, TÜbingen, Germany

Mar 10, 2026

Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions

AI systems are increasingly entrenched in public governance, yet scholarship lacks formal tools to determine when deviations of public trust in algorithmic institutions dissipate and when they grow into collapse. Stability refers here to asymptotic recovery from finite state perturbations under fixed structural parameters. We address this gap by developing a mathematical framework for institutional trust stability that couples a Friedkin-Johnsen opinion dynamics process with a Hawkes-inspired intensity process for AI controversies. Motivated by the Computers-Are-Social-Actors literature and recent studies of trust in large language models, this bidirectional coupling reveals that governance stability depends on the structural architecture of the information environment rather than absolute trust levels. We derive an exact spectral stability criterion delineating resilience from collapse, demonstrating how event self-excitation and memory persistence systematically narrow the stable parameter regime. Our structural analysis yields four counterintuitive structural implications: high-trust systems can be structurally fragile, low-trust environments can be structurally stable, dynamical stability neither measures nor guarantees algorithmic fairness or legitimacy, and network topology reshapes equilibrium heterogeneity while its effect on spectral stability is uniformly bounded in an explicit memory-dominated regime. Governance assessment should therefore pair normative evaluation of harms and fairness with structural analysis of recoverability, rather than treating either as a proxy for the other.

Jiaqi Lai, Hou Liang, Weihong Huang

Nanyang Technological University, Singapore

Hawkes Processes AI Governance Trust in AI

Mar 8, 2026

"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work

The growing use of AI applications among freelance workers is reshaping trust and relationships with clients. This paper investigates how both workers and clients perceive AI use and disclosure in the freelance economy through a three-stage study: interviews with workers and two survey studies with workers and clients. Findings first reveal a key expectation gap around disclosure: Workers often adopt passive disclosure practices, revealing AI use only when asked, as they assume clients can already detect it. Clients, however, are far less confident in recognizing AI-assisted work and prefer proactive disclosure. A second finding highlights the role of unclear or absent client AI policies, which leave workers consistently misinterpreting clients' expectations for AI use and disclosure. Together, these gaps point to the need for clearer guidelines and practices for AI disclosure. Insights extend beyond freelancing, offering implications for trust, accountability, and policy design in other AI-mediated work domains.

Angel Hsing-Chi Hwang, Senya Wong, Baixiao Chen +2

University of Southern California · Emory University · IBM Research

AI Use Disclosure AI and Labor Trust in AI AI Governance

Mar 5, 2026

The Rise of AI in Weather and Climate Information and its Impact on Global Inequality

AI development's current trajectory risks automating and amplifying the North-South divide in the global climate information system. Frontier models are built almost exclusively in the Global North, and this inequality continues through inputs, processes, and outputs, from biased training data to unrepresentative validation, disproportionately affecting vulnerable regions. Addressing these disparities requires a Climate Digital Public Infrastructure, evaluation metrics centring well-being, and knowledge co-production to foster resilience rather than inequity.

Amirpasha Mozaffari, Amanda Duarte, Lina Teckentrup +8

Earth Sciences Department, Barcelona Supercomputing Center (BSC), Plaça d’Eusebi Güell, 1-3, Barcelona, 08034, Spain. · School of Public Health, Imperial College London, White City Campus, London, W12 7TA, United Kingdom. · Independent Researcher. +1

Algorithmic Bias AI Governance Climate Science

Mar 5, 2026

Incentive Aware AI Regulations: A Credal Characterisation

The rapid proliferation of AI applications has intensified debate on effective regulation of these black-box services. Effective regulation must balance two competing goals: (1) deterring non-compliant providers from entering the market, while (2) retaining compliant ones. We call this ideal the perfect market outcome (PMO). Regulators face two compounding obstacles that make PMO difficult to achieve: providers hold private information and can act strategically to evade compliance, while any evidence drawn or derived from a finite sample carries statistical uncertainty in proving non-compliance. As this information asymmetry and statistical uncertainty is inherent to any effective regulation, we formalise them through a mechanism design framework that explicitly accounts for such statistical uncertainty. This yields a sharp characterisation: a mechanism achieves PMO if and only if the set of non-compliant evidence distributions forms a closed, convex set of probability measures, known in imprecise probability as a credal set. This result serves as a diagnostic tool to determine whether PMO is achievable under a given regulation. We further show that PMO-achieving mechanisms can be constructed from a collection of hypothesis tests, and validate our theoretical contributions through experiments on spurious-feature and fairness-based regulations.

Anurag Singh, Julian Rodemann, Rajeev Verma +2

1Rational Intelligence Lab, CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Department of Statistics, LMU Munich, Germany · 3UvA-Bosch Delta Lab, University of Amsterdam, Netherlands +1

Mechanism Design Imprecise Probability AI Governance Statistical Hypothesis Testing AI Regulatory Compliance

Mar 4, 2026

No Certificate, No Categorical Speech Act: A Brouwerian Assertibility Constraint for Public Reason

Generative AI can convert uncertainty into authoritative-seeming verdicts, displacing the justificatory work on which democratic epistemic agency depends. As a corrective, I propose a Brouwer-inspired assertibility constraint for responsible AI: in high-stakes domains, systems may assert or deny claims only if they can provide a publicly inspectable and contestable certificate of entitlement; otherwise they must return Undetermined. This constraint yields a three-status interface semantics (Asserted, Denied, Undetermined) whose statuses mark entitlement to categorical speech rather than truth values of the underlying world-claim. The framework distinguishes internal entitlement from public standing while connecting them through the certificate as a boundary object. Philosophically, it develops a form of applied intuitionism: resistance to unwarranted completeness becomes a norm of public machine speech, under which categorical commitment requires exhibited and contestable entitlement. I operationalize the constraint through structured certificates, decision-layer gates for threshold and argmax predicates, and a reason-coded output contract. A design lemma shows that, under a refutation-soundness condition on the contractually specified negative side, certificate-sound binary totality presupposes witnessed decidability on the declared scope. Under the proposed normative output contract, Undetermined is not a tunable reject option but a mandatory status whenever the available certificates do not uniquely license assertion or denial. By making outputs answerable to contestable warrants rather than confidence alone, the paper aims to preserve epistemic agency where automated speech enters public justification.

Michael Jülich

aihorizon R&D, Munich, Germany.

AI Accountability Selective Prediction AI Governance Human Oversight of AI Epistemic Uncertainty

Mar 4, 2026

Social physics in the age of artificial intelligence

Artificial intelligence (AI) systems are rapidly becoming more capable, autonomous, and deeply embedded in social life. As humans increasingly interact, cooperate, and compete with AI, we move from purely human societies to hybrid human-AI societies whose collective dynamics cannot be captured by existing behavioural models alone. Drawing on evolutionary game theory, cultural evolution, and Large Language Models (LLMs) powered simulations, we argue that these developments open a new research agenda for social physics centred on the co-evolution of humans and machines. We outline six key research directions. First, modelling the evolutionary dynamics of social behaviours (e.g. cooperation, fairness, trust) in hybrid human-AI populations. Second, understanding machine culture: how AI systems generate, mediate, and select cultural traits. Third, analysing the co-evolution of language and behaviour when LLMs frame and participate in decisions. Fourth, studying the evolution of AI delegation: how responsibilities and control are negotiated between humans and machines. Fifth, formalising and comparing the distinct epistemic pipelines that generate human and AI behaviour. Sixth, modelling the co-evolution of AI development and regulation in a strategic ecosystem of firms, users, and institutions. Together, these directions define a programme for using social physics to anticipate and steer the societal impact of advanced AI.

The Anh Han, Joel Z. Leibo, Tom Lenaerts +4

School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK. · Google DeepMind, London, UK · Machine Learning Group, Universit´e Libre de Bruxelles, Brussels, Belgium +9

Computational Social Science AI Governance Cultural Evolution

Mar 3, 2026

The Controllability Trap: A Governance Framework for Military AI Agents

Agentic AI systems - capable of goal interpretation, world modeling, planning, tool use, long-horizon operation, and autonomous coordination - introduce distinct control failures not addressed by existing safety frameworks. We identify six agentic governance failures tied to these capabilities and show how they erode meaningful human control in military settings. We propose the Agentic Military AI Governance Framework (AMAGF), a measurable architecture structured around three pillars: Preventive Governance (reducing failure likelihood), Detective Governance (real-time detection of control degradation), and Corrective Governance (restoring or safely degrading operations). Its core mechanism, the Control Quality Score (CQS), is a composite real-time metric quantifying human control and enabling graduated responses as control weakens. For each failure type, we define concrete mechanisms, assign responsibilities across five institutional actors, and formalize evaluation metrics. A worked operational scenario illustrates implementation, and we situate the framework within established agent safety literature. We argue that governance must move from a binary conception of control to a continuous model in which control quality is actively measured and managed throughout the operational lifecycle.

Subramanyam Sahoo

MARS (Mentorship for Alignment Researchers) 4.0 Fellow Cambridge AI Safety Hub (CAISH) University of Cambridge

AI Agent Governance AI Governance Human Oversight of AI AI Agent Monitoring

Mar 3, 2026

AI Space Physics: Constitutive boundary semantics for open AI institutions

Agentic AI deployments increasingly behave as persistent institutions rather than one-shot inference endpoints: they accumulate state, invoke external tools, coordinate multiple runtimes, and modify their future authority surface over time. Existing governance language typically specifies decision-layer constraints but leaves the causal mechanics of boundary crossing underdefined, particularly for transitions that do not immediately change the external world yet expand what the institution can later do. This paper introduces AI Space Physics as a constitutive semantics for open, self-expanding AI institutions. We define a minimal state model with typed boundary channels, horizon-limited reach semantics, and a membrane-witness discipline. The core law family (P-1, P-1a, P-1b, P-1c) requires witness completeness, non-bypass mediation, atomic adjudication-to-effect transitions, and replayable reconstruction of adjudication class. We explicitly separate second-order effects into structural expansion and policy broadening, and treat expansion transitions as governance-relevant even when immediate external deltas are zero. The novelty claim is precise rather than expansive: this work does not introduce mediation as a concept; it reclassifies authority-surface expansion as a first-class boundary event with constitutive witness obligations. In this semantics, expansion without immediate commit remains adjudication-relevant.

Oleg Romanchuk, Roman Bondar

AI Agent Governance AI Governance Runtime Enforcement for AI Agents

Mar 2, 2026

Beyond Detection: Governing GenAI in Academic Peer Review as a Sociotechnical Challenge

Generative AI tools are increasingly entering academic peer review workflows, raising questions about fairness, accountability, and the legitimacy of evaluative judgment. While these systems promise efficiency gains amid growing reviewer overload, their use introduces new sociotechnical risks. This paper presents a convergent mixed-method study combining discourse analysis of 448 social media posts with interviews with 14 area chairs and program chairs from leading AI and HCI conferences to examine how GenAI is discussed and experienced in peer review. Across both datasets, we find broad agreement that GenAI may be acceptable for limited supportive tasks, such as improving clarity or structuring feedback, but that core evaluative judgments, assessing novelty, contribution, and acceptance, should remain human responsibilities. At the same time, participants highlight concerns about epistemic harm, over-standardization, unclear responsibility, and adversarial risks such as prompt injection. User interviews reveal how structural strain and institutional policy ambiguity shift interpretive and enforcement burdens onto individual scholars, disproportionately affecting junior authors and reviewers. By triangulating public governance discourse with lived review practices, this work reframes AI mediated peer review as a sociotechnical governance challenge and offers recommendations for preserving accountability, trust, and meaningful human oversight. Overall, we argue that AI-assisted peer review is best governed not by blanket bans or detection alone, but by explicitly reserving evaluative judgment for humans while instituting enforceable, role-specific controls that preserve accountability. We conclude with role specific recommendations that formalize the support judgment boundary.

Tatiana Chakravorti, Pranav Narayanan Venkit, Sourojit Ghosh +1

Pennsylvania State University, University Park, Pennsylvania, USA · Salesforce AI Research, San Francisco, California, USA · University of Washington, Seattle, Washington, USA

Scholarly Peer Review AI Governance Human Oversight of AI Automated Peer Review Algorithmic Accountability

Mar 2, 2026

The Sentience Readiness Index: A Preliminary Framework for Measuring National Preparedness for the Possibility of Artificial Sentience

The scientific study of consciousness has begun to generate testable predictions about artificial systems. A landmark collaborative assessment evaluated current AI architectures against six leading theories of consciousness and found that none currently qualifies as a strong candidate, but that future systems might. A precautionary approach to AI sentience, which holds that credible possibility of sentience warrants governance action even without proof, has gained philosophical and institutional traction. Yet existing AI readiness indices, including the Oxford Insights Government AI Readiness Index, the IMF AI Preparedness Index, and the Stanford AI Index, measure economic, technological, and governance preparedness without assessing whether societies are prepared for the possibility that AI systems might warrant moral consideration. This paper introduces the Sentience Readiness Index (SRI), a preliminary composite index measuring national-level preparedness across six weighted categories for 31 jurisdictions. The SRI was constructed following the OECD/JRC framework for composite indicators and employs LLM-assisted expert scoring with iterative expert review to generate an initial dataset. No jurisdiction exceeds ``Partially Prepared'' (the United Kingdom leads at 49/100). Research Environment scores are universally the strongest category; Professional Readiness is universally the weakest. These exploratory findings suggest that if AI sentience becomes scientifically plausible, no society currently possesses adequate institutional, professional, or cultural infrastructure to respond. As a preliminary framework, the SRI provides an initial diagnostic baseline and highlights areas for future methodological refinement, including expanded expert validation, improved measurement instruments, and longitudinal data collection.

Tony Rost

The Harder Problem Project Portland, OR, United States

AI Governance AI Safety

Feb 28, 2026

Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma

Organizational leaders are being asked to make high-stakes decisions about AI deployment without dependable evidence of what these systems actually do in the environments they oversee. The predominant AI evaluation ecosystem yields scalable but abstract metrics that reflect the priorities of model development. By smoothing over the heterogeneity of real-world use, these model-centric approaches obscure how behavior varies across users, workflows, and settings, and rarely show where risk and value accumulate in practice. More user-centric studies reveal rich contextual detail, yet are fragmented, small-scale and loosely coupled to the mechanisms that shape model behavior. The Forum for Real-World AI Measurement and Evaluation (FRAME) aims to address this gap by combining large-scale trials of AI systems with structured observation of how they are used in context, the outcomes they generate, and how those outcomes arise. By tracing the path from an AI system's output through its practical use and downstream effects, FRAME turns the heterogeneity of AI-in-use into a measurable signal rather than a trade-off for achieving scale. The Forum establishes two core assets to achieve this: a Testing Sandbox that captures AI-in-use under real workflows at scale and a Metrics Hub that translates those traces into actionable indicators.

Reva Schwartz, Gabriella Waters

Civitaas Insights · Center for Responsible AI, Virginia State University, and Civitaas Insights

Human-Centered AI AI Governance