arXiv AI
Aug 18
Software Engineering for AI-driven Building Operation
arXiv:2608. 16237v1 Announce Type: cross Abstract: Building operations are energy-inefficient.
Software Engineering for AI-driven Building Operation
Related stories
arXiv AI
Jun 29
Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
arXiv:2606. 27960v1 Announce Type: cross Abstract: Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems.
arXiv AI
Aug 5
Studying, Identifying, and Fixing Hidden Technical Debt in AI-Intensive Cyber-Physical Systems
arXiv:2608. 02638v1 Announce Type: cross Abstract: Artificial Intelligence (AI) components are increasingly pervasive in several software systems, including Cyber-Physical Systems (CPSs).
arXiv AI
Jul 28
Physical AI Governance: From Theory to Practice Across Life Cycle
arXiv:2607. 22877v1 Announce Type: new Abstract: With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world.
arXiv AI
Sep 10
Towards Trustworthy Physical AI: From Theory to Practice Across Life Cycle
The paper introduces the concept of Physical AI—systems that understand and act within the physical world, where interactions are continuous, uncertain, and irreversible. It surveys trustworthy principles specific to Physical AI, outlines the role of physics in AI, and maps the end‑to‑end life cycle across five core stages, culminating in the Trustworthy Physical AI Operationalization (T‑PAIO) and the broader Trustworthy Physical AI (T‑PAI) framework.
arXiv AI
Sep 10
The Normalization of Deviance in AI Development
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 AI
5d ago
AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems
The paper proposes AI Deployment Accountability Engineering (ADAE), a new subdiscipline focused on establishing measurable, continuous, and actionable accountability for AI systems once they are deployed. ADAE treats accountability as a deployment-layer property, aiming to ensure systems remain within acceptable risk limits, identify failure contexts, attribute failures across technical and human components, and translate technical failures into downstream consequences. The authors outline a research agenda built around four pillars—structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational and institutional risks—to support timely intervention in safety-critical socio-technical environments.
arXiv AI
Aug 5
Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems
arXiv:2608. 03413v1 Announce Type: new Abstract: As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems.
OpenAI Blog
Apr 5, 2023
Our approach to AI safety
Ensuring that AI systems are built, deployed, and used safely is critical to our mission.
arXiv AI
Jul 17
Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI
arXiv:2607. 14353v1 Announce Type: cross Abstract: As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems.
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 3
Beyond Component Testing: Validating Agentic AI Systems
arXiv:2607. 29405v1 Announce Type: new Abstract: Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation.