A new arXiv paper analyzes how value is defined and negotiated across the increasingly automated language and translation sector. It draws on interviews with 29 industry stakeholders gathered through the LT-LiDER project. The author discusses what these stakeholder perspectives could mean for how translation is taught.
A new arXiv paper examines the trend of users consulting large language models as all-knowing authorities on subjective personal questions. The authors propose a typology of this behavior and LLM-based measurement methods, citing concerns about user autonomy and well-being. The work is categorized as a cross-listing on arXiv cs.AI.
A new arXiv preprint argues that while debates about machine moral agency and sentience are well established, the question of whether AI systems could possess free will has received little attention. The author outlines a conceptual framework for approaching that question and sketching how it might be evaluated.
A Hacker News discussion argues that responsibility for damage attributed to AI systems rests with the people who build, deploy, and use them rather than with the models themselves. Commenters frame the issue as a question of accountability and intent, echoing familiar debates about technology as a tool. The thread reflects ongoing disagreement over how much blame or regulation should target AI systems directly.
A new arXiv paper introduces the concept of cognitive digital twins: computational models of an individual's cognition that are continuously updated from behavioral and personal data. The authors argue these systems raise ethical and governance concerns, and they outline risks tied to persistent, personalized AI representations of specific people. The work calls for governance approaches to address those risks.
A doctoral thesis posted to arXiv surveys how machine learning systems shape consequential decisions and everyday life. It argues that because these data-driven tools are often built without adequate attention to their broader effects, their social consequences warrant closer study. The work falls under the computer science machine learning category as a revised preprint.
OpenAI is accepting applications for a $5 million grant program that will fund independent studies on how generative AI influences adolescent development, well-being, and safety. The initiative is aimed at researchers outside the company who are examining the risks and benefits of AI use among teenagers.
WHY IT MATTERS ↘By bankrolling independent research on adolescent AI use, OpenAI is helping shape the evidence base that regulators, courts, and school buyers will likely cite, while positioning youth safety as a credibility battleground competitors must address. For practitioners, this signals that age-appropriate design, safety evaluations, and possibly age assurance are moving toward standard product and compliance expectations rather than optional features.