arXiv AI
Aug 13
No One to Blame: A Framework of Constitutive AI Unaccountability
arXiv:2608. 12104v1 Announce Type: cross Abstract: The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms.
Comparing Socio-technical Design Principles with Guidelines for Human-centered AI
arXiv:2607. 10331v1 Announce Type: new Abstract: Human-centered AI (HCAI) refers to guidelines or principles that aim on ethi-cally oriented design of systems.
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arXiv AI
6d ago
What Will Remain Human in Software Architecture? A Focus Group Report
The report examines how software architects view the growing use of AI development agents in their field. A focus group of 22 industry and academic participants discussed current practices, trust, validation, governance, and educational implications, concluding that decision‑making, accountability, and guardrail authoring remain human responsibilities. They introduced the concept of harness engineering—building systems that govern AI‑assisted creation—and identified criticality and cognitive debt as key factors for calibrating human oversight.
arXiv AI
4d ago
Designing a Boundary Negotiating Artifact for Collaborative Socio-Technical Sense-Making in AI Regulatory Sandboxes
arXiv:2609.37109v1 Announce Type: cross Abstract: The rapid, unpredictable advancements in AI system capabilities has seen regulators take adaptive and experimental approaches to policymaking. Establ...
arXiv AI
4d ago
Human-AI Collaboration: From Paradoxes to Patterns
The paper demonstrates that humans and AI systems achieve better performance when collaborating rather than working alone. It investigates how two design dimensions—autonomy and initiative—shape collaboration patterns, using a paradox perspective to uncover internal tensions and map underlying paradoxes. From this analysis, the authors derive four distinct human‑AI collaboration patterns: Instruction, Delegation, Assistance, and Co‑creation.
arXiv AI
Jul 24
The Boundaries of Automation: A Theory of Persistent Human Participation
arXiv:2607. 21547v1 Announce Type: new Abstract: The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible.
arXiv AI
Aug 26
AI Agents Push Humans Out of the Loop
AI agents are increasingly autonomous, posing significant risks that current designs hinder effective human oversight. The paper argues that oversight is degraded by both design choices and the cognitive decline of users who rely heavily on automation. It calls for prioritizing human cognitive needs in AI agent development, proposing design affordances and protocols to maintain critical judgment and counter skill atrophy.
arXiv AI
Jul 1
A Technical Typology of AI Systems in Public Administration
arXiv:2606. 31755v1 Announce Type: cross Abstract: Research on artificial intelligence (AI) in the public sector often treats "AI" as a single category, neglecting technical distinctions between different AI systems.
arXiv AI
Jun 12
From AGI to ASI
arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.
arXiv AI
6d ago
Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development
The paper examines how a large embedded systems company is transitioning to an AI‑first organization, focusing on the role of autonomous AI agents in software engineering. Through a mixed‑method study involving 40 workshop participants—scrum masters, architects, managers, and product owners—the authors identify expected impacts on team structure, required competencies, organizational strategies, and developer roles. The study concludes with a concrete roadmap and discusses implications for federated AI team formation, human‑in‑the‑loop practices, and sustainable AI adoption in embedded software engineering.
arXiv AI
Aug 18
A Regulatory Placebo? The Systemic Failure of Mandatory GenAI Labeling
arXiv:2608. 16470v1 Announce Type: cross Abstract: We examine the worldwide trend of mandatory labeling of generative artificial intelligence(GenAI) as a reactive, symbolic form of legislation triggered by technological panic and institutional responses.
arXiv AI
Aug 25
A Survey on Human-AI Collaboration with Large Foundation Models
The paper surveys how Large Foundation Models (LFMs) can be integrated into Human‑AI Collaboration (HAI) to enhance problem‑solving and decision‑making. It outlines four key areas—human‑guided model development, collaborative design principles, ethical and governance frameworks, and high‑stakes applications—while emphasizing that effective HAI systems arise from careful, human‑centered design rather than merely stronger models. The survey also identifies open challenges related to safety, fairness, and control, aiming to guide future research toward reliable, trustworthy, and beneficial LFM‑based partnerships.
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.