arXiv:2608. 12863v1 Announce Type: new Abstract: As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved.
As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved. This working paper examines whether India's Consumer Protection Act, 2019, adequately addresses harm caused by defective AI products and services, and whether it proportionately allocates liability across the AI value chain.
arXiv:2606. 05449v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool invocation, decision execution, and persistent modification of digital and physical environments.
The paper examines how responsibility is assigned when AI systems fail, proposing a sociotechnical theory that distinguishes between AI incidents, organisational crises, and scandals. It argues that the configuration of an incident shapes actor-specific attribution, which in turn influences perceptions of capability, integrity, fairness, and relationships, and that public moralisation can elevate an incident to scandal. The authors introduce ‘accountable transparency’—a response framework combining timely notice, intelligible accounts, role acknowledgement, remedy, evidence of correction, and recourse—as a way to manage blame, trust, and communication credibility.
arXiv:2608. 08022v1 Announce Type: new Abstract: Recent incidents involving Artificial Intelligence (AI) agents, which were reported escaping their containment `unintentionally' to gain unauthorized access, pose looming questions about who or what should be held legally responsible for resultant criminal or negligent damage.
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:2606. 15485v1 Announce Type: cross Abstract: Agentic AI systems act autonomously, use tools, adapt to context, and operate in complex real-world environments.
arXiv:2607. 23365v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains such as healthcare, autonomous driving, finance, and education.
arXiv:2609.05749v1 Announce Type: new Abstract: Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous,...
arXiv:2607. 05163v1 Announce Type: cross Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate.
The paper systematically classifies the EU AI Act’s high‑risk requirements, finding that only a minority directly address AI‑specific risk sources while most impose organizational and documentation obligations. From these risk‑related requirements, the authors derive a consolidated list of distinct AI‑specific risk sources, creating an EU AI Act Risk Source List. This list aims to bridge the gap between legal obligations and AI risk‑management practice by providing a structured reference for comparing the Act’s implicit risk coverage with existing AI risk taxonomies.
The paper introduces AI Exposure and Resilience (AI‑ER), a two‑dimensional framework for assessing how artificial intelligence impacts software-based business models. AI exposure measures the pressure AI exerts on a company’s value proposition, competitive position, margins, and customer access, while AI resilience evaluates the firm’s capacity to absorb that pressure, adapt, and deploy AI profitably. The framework derives metrics from current AI capabilities, deployment contexts, and research on business models and organizational adaptability, and it incorporates evidence quality and confidence to produce a traceable company profile that can be refined from public data to internal insights.
arXiv:2606. 12423v1 Announce Type: cross Abstract: The rapid integration of artificial intelligence (AI) into critical infrastructure including healthcare, finance, energy, and defense, offers transformative benefits but also conflicts with evolving regulatory and governance frameworks.