arXiv AI
Jul 14
Introducing Human-Centeredness in AI-Assisted Lexicography
arXiv:2607. 11808v1 Announce Type: cross Abstract: This paper proposes a human-centered artificial intelligence (HCAI) framework for AI-assisted lexicography.
Toward Human-Centered AI-Assisted Terminology Work
Related stories
arXiv AI
Jul 7
Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions
arXiv:2607. 03233v1 Announce Type: cross Abstract: The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intelligence, cybersecurity, and cyber investigation.
arXiv Computation and Language
Oct 1
Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations
arXiv:2609.38486v1 Announce Type: cross Abstract: Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomen...
arXiv AI
Jun 12
Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI
arXiv:2606. 12713v1 Announce Type: new Abstract: Claims that artificial general intelligence has already arrived and claims that it remains decades away are often defended from overlapping evidence.
arXiv AI
Aug 3
Unanticipated Effects of Generative AI on Expertise Pathways and Performance Perception in System Administration
arXiv:2607. 28650v1 Announce Type: cross Abstract: While industry discourse often emphasizes immediate productivity gains and frames GenAI primarily as a tool for automation, the integration of GenAI into system administration may involve deeper shifts in professional practice that are not yet fully understood.
arXiv AI
Sep 15
Investigating the Impacts of Generative AI on Information Seeking
arXiv:2609.14638v1 Announce Type: cross Abstract: This paper is an encore submission of our 2026 journal article "Expertise and Information Seeking in the Age of Generative AI: New Procedures, New Pr...
arXiv AI
Aug 11
Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents
arXiv:2608. 08601v1 Announce Type: new Abstract: To anticipate socio-technical risks from AI agents, organizations need taxonomies to classify them.
arXiv AI
Jul 3
A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content
arXiv:2607. 01248v1 Announce Type: cross Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation.
arXiv AI
Aug 7
Vibe Compiler: A Research-Logic Synthesis Tool That Runs without Prompt Engineering -Toward Enhancing Metacognition for Sustaining Agency in the Age of Generative AI-
arXiv:2608. 05545v1 Announce Type: cross Abstract: Generative AI used as a capable servant has greatly accelerated intellectual work, but it also risks eroding human epistemic agency by encouraging uncritical acceptance of AI-generated reasoning.
arXiv AI
Aug 24
Six misconceptions about large language models: A minimal model and diagnostic taxonomy
The article presents a minimal working model for large language model (LLM) systems, emphasizing four key distinctions—pretraining vs. deployment, distribution vs. samples, types of memory, and task competence vs. agency. Using this framework, it diagnoses six common misconceptions about LLMs (next‑token prediction, regression to the mean, training‑data regurgitation, model memory, alignment, and understanding), explaining what each misconception captures correctly, where it conflates distinctions, and the implications for evaluation, design, and governance. The model is applied to AI policy language, illustrating how policy can misrepresent these distinctions and offering a diagnostic toolkit to correct such errors.
arXiv AI
Sep 7
Role-Aware Artificial Intelligence Across Augmentation and Automation in Human-Machine Symbiosis
The paper explores how to trace the functional role of AI in natural language generation, distinguishing between AI acting as an assistive editor or a creative generator. It proposes a methodology that infers the latent role from prompts, embeds it during generation, and recovers the role from the output. Experiments demonstrate that the approach can discriminate roles, remains robust to perturbations, and preserves linguistic quality.
arXiv AI
Sep 2
Human-AI Co-Interpretation for Responsible AI: A Hermeneutic Perspective
The paper examines how large language model (LLM) outputs are increasingly used in contexts that demand justified interpretations, such as law, education, policy analysis, and public moral debate. It identifies a recurring failure—interpretive misplacement—where model-generated readings are treated as settled meanings without explicit interpretive frames, provenance, or defensible alternatives, leading to accountability loss. Drawing on philosophical hermeneutics, the author proposes design principles for human‑AI co‑interpretation, reorganizes existing LLM techniques into hermeneutically responsible patterns, and discusses implications for legal practice, education, scholarship, and public discourse, while framing digital hermeneutics as a literacy for critically engaging with AI‑mediated texts.