arXiv AI
Jun 24
Exploring the relationship between human-centric AI and firm idiosyncratic risks
arXiv:2606. 24224v1 Announce Type: new Abstract: Despite the extensive discussions of human-centric AI (HCAI) in Industry 5.
Exploring the relationship between human-centric AI and firm idiosyncratic risks
Despite the extensive discussions of human-centric AI (HCAI) in Industry 5. 0, its effects on firms' idiosyncratic risks (IR) remains underexplored.
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arXiv AI
Jun 9
Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI Officer
arXiv:2407. 10247v3 Announce Type: replace-cross Abstract: The integration of Artificial Intelligence (AI) into corporate strategy has become critical for organizations seeking to maintain competitive advantage in the digital age.
arXiv AI
Jul 3
Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond
arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.
arXiv AI
Aug 13
Ethics Practices in AI Development: An Empirical Study Across Roles and Regions
arXiv:2508. 09219v3 Announce Type: replace-cross Abstract: Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by these technologies.
arXiv AI
Jun 16
The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products
arXiv:2606. 15485v1 Announce Type: cross Abstract: Agentic AI systems act autonomously, use tools, adapt to context, and operate in complex real-world environments.
arXiv AI
Aug 12
What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research
arXiv:2608. 10431v1 Announce Type: cross Abstract: Responsible AI (RAI) has become a central concern for technology companies, regulators, and the public.
arXiv AI
Jul 20
Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI
arXiv:2607. 15992v1 Announce Type: new Abstract: Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings.
Hugging Face Trending Papers
Jul 17
Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI
Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings. Yet this work has not produced a market that rewards trustworthiness.
arXiv AI
Jun 26
The Governance Inversion Hypothesis: Why More AI Regulation May Produce Less Organisational Control
arXiv:2606. 26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems.
arXiv AI
Sep 12
Buyer Artificial Intelligence-Enabled Environmental Governance and Supplier Environmental Controversies: An Organizational Information Processing and Signaling
The study investigates how exposure to buyers’ artificial intelligence (AI)-enabled environmental governance affects supplier environmental controversies. Using text analysis and panel data from 2,505 suppliers of U.S.-listed firms across 41 countries (2020‑2024), the authors find that such exposure is negatively associated with controversies in the following year. The effect is stronger in countries with higher AI readiness and regulatory quality.
Hugging Face Trending Papers
Sep 10
AI Exposure and AI Resilience: A Two-Dimensional Assessment Framework for Software and Software-Based Business Model
The paper introduces the AI Exposure and Resilience (AI-ER) framework, a two‑dimensional assessment tool for evaluating how artificial intelligence impacts software-based business models. AI exposure measures the pressure AI exerts on a company’s value proposition, competitive stance, margins, and customer access, while AI resilience gauges the firm’s capacity to absorb, adapt to, and economically leverage that pressure. The framework derives metrics from current AI capabilities, deployment contexts, and research on business models and adaptability, and it includes an explicit evaluation of evidence quality and confidence, allowing for a traceable company profile that can be refined from public data to internal insights.
arXiv AI
Aug 14
Why AI Governance Frameworks Are Hard to Adopt: A Role-Based Stress Test of the NIST AI RMF
arXiv:2608. 12352v1 Announce Type: cross Abstract: AI governance frameworks can be known, used, and implemented in form without becoming governance in practice.