- Bias in training data and results [Note 1]
- Lack of transparency in algorithms “black boxes”
- difficult to understand how specific text analysis results are produced [Note 2]
- Accountability and authorship
- Copyright and Intellectual Property [Note 3]
- Ethical concerns (e.g., plagiarism, misuse of AI-generated content)
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Decline in linguistic diversity [Note 4]
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Over-reliance on AI vs. critical thinking [Note 5]
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Generation of False or Misleading Content: AI models can produce plausible-sounding but inaccurate or fabricated information (known as “hallucinations”), which can mislead users or propagate misinformation if not properly checked. [Note 6] (https://doi.org/10.48550/arXiv.2402.08323)
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Difficulty in Data Correction or Deletion: Once personal data is incorporated into large language models, it can be challenging or impossible for individuals to access, correct, or delete their information, contravening data protection principles. (https://www.pcpd.org.hk/english/news_events/newspaper/newspaper_202309a.html)
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