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Balancing Minds and Data:The Privacy Dilemma of LLMs and Anthropomorphism in LLMs
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作者 Raffael Meier 《Journal of Social Computing》 2025年第3期173-183,共11页
This essay examines the intricate relationship between large language models(LLMs)and privacy,investigating the ethical and practical issues stemming from cutting-edge artificial intelligence(AI)technologies.The resea... This essay examines the intricate relationship between large language models(LLMs)and privacy,investigating the ethical and practical issues stemming from cutting-edge artificial intelligence(AI)technologies.The research delves into the evolving understanding of privacy in the digital era,with a specific emphasis on the risks posed by anthropomorphic AI design.The analysis highlights critical privacy concerns:(1)Trust and accountability:The lack of true moral agency in AI systems complicates traditional notions of trust and responsibility;(2)Nissenbaum’s Contextual Integrity Framework as a tool to explore privacy issues in general and with LLM;(3)Data collection challenges:LLMs collect extensive user data,often without explicit consent,potentially breaching contextual privacy norms;(4)Anthropomorphism risks:Human-like AI interfaces can foster over-trust,leading users to share sensitive information inappropriately.This article underscores that privacy is a complex,multidimensional concept profoundly shaped by technological,cultural,and social forces.As AI technologies continue to advance,safeguarding privacy will necessitate a nuanced approach that strikes a balance between individual rights,societal needs,and technological progress.We conclude with useroriented guidelines and future research directions,offering a comprehensive framework for understanding and addressing the privacy implications of LLMs. 展开更多
关键词 large language models(LLMs) PRIVACY anthropomorphic artificial intelligence(AI) trust calibration contextual integrity data protection membership inference attack human-AI interaction
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Leveraging AI in the Translation of Chinese Buddhist Canons:Applications,Challenges,and Preventative Strategies
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作者 Miaoguang 《The Voice of Dharma》 2025年第1期22-29,共8页
The Chinese Buddhist Canons have played a pivotal role in the global spread of Buddhism, serving as a foundational resource for understanding Buddhist teachings. Leveraging AI for translation presents significant adva... The Chinese Buddhist Canons have played a pivotal role in the global spread of Buddhism, serving as a foundational resource for understanding Buddhist teachings. Leveraging AI for translation presents significant advantages, including enhanced speed, accuracy, and accessibility, which can facilitate wider dissemination and engagement with these canonical texts. The Fo Guang Dictionary of Buddhism Translation Project exemplifies how AI tools can be used effectively to accelerate the translation of Buddhist terminology, while also ensuring consistency. However, ensuring quality control and preserving cultural context are crucial preventative measures to mitigate potential misinterpretations and inaccuracies. Large Language Models(LLMs) currently offer a robust solution due to their expansive data processing and linguistic flexibility. The next step may involve integrating AI technologies with human expertise to ensure nuanced, context-sensitive translations. This paper aims to explore how AI can be utilized effectively in translating the Chinese Buddhist Canons, while safeguarding the cultural and doctrinal integrity of the texts. 展开更多
关键词 Chinese Buddhist Canons Fo Guang Dictionary of Buddhism AI translation Large Language Models(LLM) cultural context Buddhist text dissemination AI-assisted translation canonical texts contextual integrity machine learning human-AI collaboration
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