arXiv AI

AI Literacy for Legal Translation: Developing Digital Resilience

arXiv:2608. 04641v1 Announce Type: new Abstract: Generative AI is transforming legal translation by introducing opportunities alongside linguistic, technical, legal, ethical and cognitive risks.

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.

By Behrooz Razeghi
Hugging Face Trending Papers
Aug 3

Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education

Generative artificial intelligence (GenAI) has entered classrooms faster than teachers have been prepared to use it well, producing a GenAI literacy lag in which technological diffusion outpaces educators' conceptual, pedagogical, and ethical readiness. Established AI literacy frameworks predate the widespread adoption of large language models and, while acknowledging ethics, position it as a discrete competency rather than a constitutive commitment, with equity and agency as supplementary design principles.

arXiv AI
Aug 25

Why we need an AI-resilient society- Profiling Large Language Models

The article discusses the evolution of AI across three generations—from explicit logic to neural networks to large language models (LLMs)—and how LLMs introduce new systemic risks. It applies a forensic‑psychology profiling method to identify ten key features of LLMs, such as hallucinations, bias, and cognitive atrophy, revealing an entity that confabulates, amplifies user biases, and erodes human competence. The report concludes with a four‑pillar framework for AI resilience, emphasizing cognitive sovereignty, measurable control, partial autonomy, and openness to safeguard society.

By Thomas Bartz-Beielstein, Eva Bartz