Tootfinder

Balancing Innovation and Oversight: Regulatory Sandboxes as a Tool for AI Governance
https://fpf.org/blog/balancing-innovation-and-oversight-regulatory-sandboxes-as-a-tool-for-ai-governance/
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Bridging the Artificial Intelligence Governance Gap: The United States' and China's Divergent Approaches to Governing General-Purpose Artificial Intelligence
Oliver Guest, Kevin Wei
https://arxiv.org/abs/2506.03497

Bridging the Artificial Intelligence Governance Gap: The United States' and China's Divergent Approaches to Governing General-Purpose Artificial Intelligence
The United States and China are among the world's top players in the development of advanced artificial intelligence (AI) systems, and both are keen to lead in global AI governance and development. A look at U.S. and Chinese policy landscapes reveals differences in how the two countries approach the governance of general-purpose artificial intelligence (GPAI) systems. Three areas of divergence are notable for policymakers: the focus of domestic AI regulation, key principles of domestic AI regul…

This https://arxiv.org/abs/2412.03824 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Towards Data Governance of Frontier AI Models
Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for "frontier …

Will Agents Replace Us? Perceptions of Autonomous Multi-Agent AI
Nikola Balic
https://arxiv.org/abs/2506.02055 https://arxiv.org/pdf/…

Will Agents Replace Us? Perceptions of Autonomous Multi-Agent AI
Autonomous multi-agent AI systems are poised to transform various industries, particularly software development and knowledge work. Understanding current perceptions among professionals is crucial for anticipating adoption challenges, ethical considerations, and future workforce development. This study analyzes responses from 130 participants to a survey on the capabilities, impact, and governance of AI agents. We explore expected timelines for AI replacing programmers, identify perceived barri…

This https://arxiv.org/abs/2505.01651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Human-AI Governance (HAIG): A Trust-Utility Approach
This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g., "human-in-the-loop" models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rat…

Toward Effective AI Governance: A Review of Principles
Danilo Ribeiro, Thayssa Rocha, Gustavo Pinto, Bruno Cartaxo, Marcelo Amaral, Nicole Davila, Ana Camargo
https://arxiv.org/abs/2505.23417

Toward Effective AI Governance: A Review of Principles
Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We…

Watermarking Without Standards Is Not AI Governance
Alexander Nemecek, Yuzhou Jiang, Erman Ayday
https://arxiv.org/abs/2505.23814 https://

Watermarking Without Standards Is Not AI Governance
Watermarking has emerged as a leading technical proposal for attributing generative AI content and is increasingly cited in global governance frameworks. This paper argues that current implementations risk serving as symbolic compliance rather than delivering effective oversight. We identify a growing gap between regulatory expectations and the technical limitations of existing watermarking schemes. Through analysis of policy proposals and industry practices, we show how incentive structures di…

Bottom-Up Perspectives on AI Governance: Insights from User Reviews of AI Products
Stefan Pasch
https://arxiv.org/abs/2506.00080 https://

Bottom-Up Perspectives on AI Governance: Insights from User Reviews of AI Products
With the growing importance of AI governance, numerous high-level frameworks and principles have been articulated by policymakers, institutions, and expert communities to guide the development and application of AI. While such frameworks offer valuable normative orientation, they may not fully capture the practical concerns of those who interact with AI systems in organizational and operational contexts. To address this gap, this study adopts a bottom-up approach to explore how governance-relev…

Reconfiguring Digital Accountability: AI-Powered Innovations and Transnational Governance in a Postnational Accounting Context
Claire Li, David Freeborn
https://arxiv.org/abs/2507.00288

Reconfiguring Digital Accountability: AI-Powered Innovations and Transnational Governance in a Postnational Accounting Context
This study explores how AI-powered digital innovations are reshaping organisational accountability in a transnational governance context. As AI systems increasingly mediate decision-making in domains such as auditing and financial reporting, traditional mechanisms of accountability, based on control, transparency, and auditability, are being destabilised. We integrate the Technology Acceptance Model (TAM), Actor-Network Theory (ANT), and institutional theory to examine how organisations adopt A…

Probing Evaluation Awareness of Language Models
Jord Nguyen, Khiem Hoang, Carlo Leonardo Attubato, Felix Hofst\"atter
https://arxiv.org/abs/2507.01786

Probing Evaluation Awareness of Language Models
Language models can distinguish between testing and deployment phases -- a capability known as evaluation awareness. This has significant safety and policy implications, potentially undermining the reliability of evaluations that are central to AI governance frameworks and voluntary industry commitments. In this paper, we study evaluation awareness in Llama-3.3-70B-Instruct. We show that linear probes can separate real-world evaluation and deployment prompts, suggesting that current models inte…

This https://arxiv.org/abs/2505.00616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Catastrophic Liability: Managing Systemic Risks in Frontier AI Development
As artificial intelligence systems grow more capable and autonomous, frontier AI development poses potential systemic risks that could affect society at a massive scale. Current practices at many AI labs developing these systems lack sufficient transparency around safety measures, testing procedures, and governance structures. This opacity makes it challenging to verify safety claims or establish appropriate liability when harm occurs. Drawing on liability frameworks from nuclear energy, aviati…

For your Saturday reading pleasure, Metacurity offers our free and paid readers a run-down of the top infosec-related long reads of the week.
This week's selection covers,
--Microsoft's Chinese "Digital Escorts" are a huge threat to DoD's digital security,
--Myanmar's scam centers are proliferating rapidly,
--Are VCs unpatriotic for investing in Chinese AI?,
--Afghanistan's extensive data systems need proper governance,
--AI c…

Best infosec-related long reads for the week of 7/12/25
Microsoft's Chinese "Digital Escorts" are a huge threat to DoD's digital security, Myanmar's scam centers are proliferating rapidly, Are VCs unpatriotic for investing in Chinese AI?, Afghanistan's extensive data systems need proper governance, AI can't replace humans in bug hunting

Annual DC Privacy Forum: Convening Top Voices in Governance in the Digital Age
https://fpf.org/blog/annual-dc-privacy-forum-convening-top-voices-in-governance-in-the-digital-age/
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Epistemic Scarcity: The Economics of Unresolvable Unknowns
Craig S Wright
https://arxiv.org/abs/2507.01483 https://arxiv.org/pdf/2507…

Epistemic Scarcity: The Economics of Unresolvable Unknowns
This paper presents a praxeological analysis of artificial intelligence and algorithmic governance, challenging assumptions about the capacity of machine systems to sustain economic and epistemic order. Drawing on Misesian a priori reasoning and Austrian theories of entrepreneurship, we argue that AI systems are incapable of performing the core functions of economic coordination: interpreting ends, discovering means, and communicating subjective value through prices. Where neoclassical and beha…

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Learn more:

Strategic Motivators for Ethical AI System Development: An Empirical and Holistic Model
Muhammad Azeem Akbar, Arif Ali Khan, Saima Rafi, Damian Kedziora, Sami Hyrynsalmi
https://arxiv.org/abs/2507.20218

Strategic Motivators for Ethical AI System Development: An Empirical and Holistic Model
Artificial Intelligence (AI) presents transformative opportunities for industries and society, but its responsible development is essential to prevent unintended consequences. Ethically sound AI systems demand strategic planning, strong governance, and an understanding of the key drivers that promote responsible practices. This study aims to identify and prioritize the motivators that drive the ethical development of AI systems. A Multivocal Literature Review (MLR) and a questionnaire-based sur…

Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks
Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos
https://arxiv.org/abs/2507.23718

Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks
Risk-based approaches to AI governance often center the technological artifact as the primary focus of risk assessments, overlooking systemic risks that emerge from the complex interaction between AI systems and society. One potential source to incorporate more societal context into these approaches is the news media, as it embeds and reflects complex interactions between AI systems, human stakeholders, and the larger society. News media is influential in terms of which AI risks are emphasized …

Mgmt: We don't have the resources to make our own LLM. Can we just monetize AI prompts?
Business School Student: Hold my beer 🍺
#blockchain

PromptChain: A Decentralized Web3 Architecture for Managing AI Prompts as Digital Assets
We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. O…

This https://arxiv.org/abs/2502.07287 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Why (not) use AI? Analyzing People's Reasoning and Conditions for AI Acceptability
In recent years, there has been a growing recognition of the need to incorporate lay-people's input into the governance and acceptability assessment of AI usage. However, how and why people judge acceptability of different AI use cases remains under-explored, despite it being crucial towards understanding and addressing potential sources of disagreement. In this work, we investigate the demographic and reasoning factors that influence people's judgments about AI's development via a survey admin…

From Firms to Computation: AI Governance and the Evolution of Institutions
Michael S. Harre
https://arxiv.org/abs/2507.13616 https://…

From Firms to Computation: AI Governance and the Evolution of Institutions
The integration of agential artificial intelligence into socioeconomic systems requires us to reexamine the evolutionary processes that describe changes in our economic institutions. This article synthesizes three frameworks: multi-level selection theory, Aoki's view of firms as computational processes, and Ostrom's design principles for robust institutions. We develop a framework where selection operates concurrently across organizational levels, firms implement distributed inference via game-…

This https://arxiv.org/abs/2502.09716 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Principles and Policy Recommendations for Comprehensive Genetic Data Governance
Genetic data collection has become ubiquitous, producing genetic information about health, ancestry, and social traits. However, unregulated use, especially amid evolving scientific understanding, poses serious privacy and discrimination risks. These risks are intensified by advancing AI, particularly multi-modal systems integrating genetic, clinical, behavioral, and environmental data. In this work, we organize the uses of genetic data along four distinct "pillars", and develop a risk assessme…

A Conceptual Framework for AI Capability Evaluations
Mar\'ia Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco, Luca Nicol\'as Forziati Gangi, Matheo Sandleris Musa, Lola Ramos Pereyra, Mario Leiva, Juan Gustavo Corvalan, Mar\'ia Vanina Martinez, Gerardo Simari
https://arxiv.org/abs/2506.18213

A Conceptual Framework for AI Capability Evaluations
As AI systems advance and integrate into society, well-designed and transparent evaluations are becoming essential tools in AI governance, informing decisions by providing evidence about system capabilities and risks. Yet there remains a lack of clarity on how to perform these assessments both comprehensively and reliably. To address this gap, we propose a conceptual framework for analyzing AI capability evaluations, offering a structured, descriptive approach that systematizes the analysis of …

AI and Agile Software Development: From Frustration to Success -- XP2025 Workshop Summary
Tomas Herda, Victoria Pichler, Zheying Zhang, Pekka Abrahamsson, Geir K. Hanssen
https://arxiv.org/abs/2506.20159

AI and Agile Software Development: From Frustration to Success -- XP2025 Workshop Summary
The full-day workshop on AI and Agile at XP 2025 convened a diverse group of researchers and industry practitioners to address the practical challenges and opportunities of integrating Artificial Intelligence into Agile software development. Through interactive sessions, participants identified shared frustrations related to integrating AI into Agile Software Development practices, including challenges with tooling, governance, data quality, and critical skill gaps. These challenges were system…

Benchmarking Multimodal Understanding and Complex Reasoning for ESG Tasks
Lei Zhang, Xin Zhou, Chaoyue He, Di Wang, Yi Wu, Hong Xu, Wei Liu, Chunyan Miao
https://arxiv.org/abs/2507.18932

Benchmarking Multimodal Understanding and Complex Reasoning for ESG Tasks
Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency. However, these documents are often lengthy, structurally diverse, and multimodal, comprising dense text, structured tables, complex figures, and layout-dependent semantics. Existing AI systems often struggle to perform reliable document-level reasoning in such settings, and no dedicated benchmark currently exists in ESG …

Flexible Hardware-Enabled Guarantees for AI Compute
James Petrie, Onni Aarne, Nora Ammann, David Dalrymple
https://arxiv.org/abs/2506.15093 https://…

Flexible Hardware-Enabled Guarantees for AI Compute
As artificial intelligence systems become increasingly powerful, they pose growing risks to international security, creating urgent coordination challenges that current governance approaches struggle to address without compromising sensitive information or national security. We propose flexible hardware-enabled guarantees (flexHEGs), that could be integrated with AI accelerators to enable trustworthy, privacy-preserving verification and enforcement of claims about AI development. FlexHEGs consi…

We've now also preprinted our paper on
«Perspectives on Crowdsourced #CitizenScience and the Data Governance of AI Applications»
given that it's so timely :D
https://papers.ssrn.com/abstract=5291262

International Security Applications of Flexible Hardware-Enabled Guarantees
Onni Aarne, James Petrie
https://arxiv.org/abs/2506.15100 https://

International Security Applications of Flexible Hardware-Enabled Guarantees
As AI capabilities advance rapidly, flexible hardware-enabled guarantees (flexHEGs) offer opportunities to address international security challenges through comprehensive governance frameworks. This report examines how flexHEGs could enable internationally trustworthy AI governance by establishing standardized designs, robust ecosystem defenses, and clear operational parameters for AI-relevant chips. We analyze four critical international security applications: limiting proliferation to address…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- Artificial Intelligence Governance for Businesses
Johannes Schneider, Rene Abraham, Christian Meske, Jan vom Brocke

An Overview of the Risk-based Model of AI Governance
Veve Fry
https://arxiv.org/abs/2507.15299 https://arxiv.org/pdf/2507.15299

An Overview of the Risk-based Model of AI Governance
This paper provides an overview and critique of the risk based model of artificial intelligence (AI) governance that has become a popular approach to AI regulation across multiple jurisdictions. The 'AI Policy Landscape in Europe, North America and Australia' section summarises the existing AI policy efforts across these jurisdictions, with a focus of the EU AI Act and the Australian Department of Industry, Science and Regulation's (DISR) safe and responsible AI consultation. The 'Analysis' sec…

Domestic frontier AI regulation, an IAEA for AI, an NPT for AI, and a US-led Allied Public-Private Partnership for AI: Four institutions for governing and developing frontier AI
Haydn Belfield
https://arxiv.org/abs/2507.06379

Domestic frontier AI regulation, an IAEA for AI, an NPT for AI, and a US-led Allied Public-Private Partnership for AI: Four institutions for governing and developing frontier AI
Compute governance can underpin international institutions for the governance of frontier AI. To demonstrate this I explore four institutions for governing and developing frontier AI. Next steps for compute-indexed domestic frontier AI regulation could include risk assessments and pre-approvals, data centre usage reports, and release gate regulation. Domestic regimes could be harmonized and monitored through an International AI Agency - an International Atomic Energy Agency (IAEA) for AI. This …

Toward a Global Regime for Compute Governance: Building the Pause Button
Ananthi Al Ramiah, Raymond Koopmanschap, Josh Thorsteinson, Sadruddin Khan, Jim Zhou, Shafira Noh, Joep Meindertsma, Farhan Shafiq
https://arxiv.org/abs/2506.20530

Toward a Global Regime for Compute Governance: Building the Pause Button
As AI capabilities rapidly advance, the risk of catastrophic harm from large-scale training runs is growing. Yet the compute infrastructure that enables such development remains largely unregulated. This paper proposes a concrete framework for a global "Compute Pause Button": a governance system designed to prevent dangerously powerful AI systems from being trained by restricting access to computational resources. We identify three key intervention points -- technical, traceability, and regulat…

This https://arxiv.org/abs/2505.23836 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Large Language Models Often Know When They Are Being Evaluated
If AI models can detect when they are being evaluated, the effectiveness of evaluations might be compromised. For example, models could have systematically different behavior during evaluations, leading to less reliable benchmarks for deployment and governance decisions. We investigate whether frontier language models can accurately classify transcripts based on whether they originate from evaluations or real-world deployment, a capability we call evaluation awareness. To achieve this, we const…

Human-AI Technology Integration and Green ESG Performance: Evidence from Chinese Retail Enterprises
Jun Cui
https://arxiv.org/abs/2507.03057 https://

Human-AI Technology Integration and Green ESG Performance: Evidence from Chinese Retail Enterprises
This study examines the relationship between human-AI technology integration transformation and green Environmental, Social, and Governance (ESG) performance in Chinese retail enterprises, with green technology innovation serving as a mediating mechanism. Using panel data comprising 5,400 firm-year observations from 2019 to 2023, sourced from CNRDS and CSMAR databases, we employ fixed-effects regression models to investigate this relationship. Our findings reveal that human-AI technology integr…

AI Governance InternationaL Evaluation Index (AGILE Index) 2025
Yi Zeng, Enmeng Lu, Xiaoyang Guo, Cunqing Huangfu, Jiawei Xie, Yu Chen, Zhengqi Wang, Dongqi Liang, Gongce Cao, Jin Wang, Zizhe Ruan, Xin Guan, Ammar Younas
https://arxiv.org/abs/2507.11546

AI Governance InternationaL Evaluation Index (AGILE Index) 2025
The year 2024 witnessed accelerated global AI governance advancements, marked by strengthened multilateral frameworks and proliferating national regulatory initiatives. This acceleration underscores an unprecedented need to systematically track governance progress--an imperative that drove the launch of the AI Governance InternationaL Evaluation Index (AGILE Index) project since 2023. The inaugural AGILE Index, released in February 2024 after assessing 14 countries, established an operational a…

Towards an Approach for Evaluating the Impact of AI Standards
Julia Lane
https://arxiv.org/abs/2506.13839 https://arxiv.org/pdf/2506.…

Towards an Approach for Evaluating the Impact of AI Standards
There have been multiple calls for investments in the development of AI standards that both preserve the transformative potential and minimize the risks of AI. The goals of AI standards, particularly with respect to AI data, performance, and governance, are to promote innovation and public trust in systems that use AI. However, there is a lack of a formal or shared method to measure the impact of these standardization activities on the goals of innovation and trust. This concept paper proposes …

Data-Centric Safety and Ethical Measures for Data and AI Governance
Srija Chakraborty
https://arxiv.org/abs/2506.10217 https://arxiv.…

Data-Centric Safety and Ethical Measures for Data and AI Governance
Datasets play a key role in imparting advanced capabilities to artificial intelligence (AI) foundation models that can be adapted to various downstream tasks. These downstream applications can introduce both beneficial and harmful capabilities -- resulting in dual use AI foundation models, with various technical and regulatory approaches to monitor and manage these risks. However, despite the crucial role of datasets, responsible dataset design and ensuring data-centric safety and ethical pract…

Hungary and AI: efforts and opportunities in comparison with Singapore
Andr\'as Ferenczy
https://arxiv.org/abs/2507.05280 https://

Hungary and AI: efforts and opportunities in comparison with Singapore
The study assesses Hungary's National AI Strategy and its implementation through the analysis of strategic documents, publicly available financial records, and expert interviews with the Hungarian AI Coalition President and Chief Strategic Advisor to the Government Commissioner for AI. 22 goals from Hungary's strategy were evaluated through conceptual, governance, temporal, and financial dimensions before being benchmarked against Singapore's National AI Strategies (NAIS 1.0 and NAIS 2.0). Key …

Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
Umer Sadiq Khan, Saif Ur Rehman Khan
https://arxiv.org/abs/2507.04249

Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data collection to deployment. Addressing these concerns is essential for ensuring that AI systems are developed and implemented in a manner that respects patient rights and promotes fairness. This study aims to explore the ethical implications of AI in medical imaging, focusing on five key stages: data collection, data processing, model training, model eval…

Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape
Jakub Kry\'s, Yashvardhan Sharma, Janet Egan
https://arxiv.org/abs/2507.07765

Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape
Advances in low-communication training algorithms are enabling a shift from centralised model training to compute setups that are either distributed across multiple clusters or decentralised via community-driven contributions. This paper distinguishes these two scenarios - distributed and decentralised training - which are little understood and often conflated in policy discourse. We discuss how they could impact technical AI governance through an increased risk of compute structuring, capabili…

Oversight Structures for Agentic AI in Public-Sector Organizations
Chris Schmitz, Jonathan Rystr{\o}m, Jan Batzner
https://arxiv.org/abs/2506.04836 https:/…

Oversight Structures for Agentic AI in Public-Sector Organizations
This paper finds that the introduction of agentic AI systems intensifies existing challenges to traditional public sector oversight mechanisms -- which rely on siloed compliance units and episodic approvals rather than continuous, integrated supervision. We identify five governance dimensions essential for responsible agent deployment: cross-departmental implementation, comprehensive evaluation, enhanced security protocols, operational visibility, and systematic auditing. We evaluate the capaci…

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
Naresh Tiwari
https://arxiv.org/abs/2506.11412 https://…

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data sovereignty and governance considerations unique to healthcare, strategic competitive advantages afforded by proprietary AI infrastructure, and th…