What are the potential risks associated with using student submissions and unpublished works in training advanced AI algorithms?
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Using student submissions and unpublished works in training advanced AI algorithms poses ethical and legal risks. These risks include potential plagiarism issues, as AI tools can generate content that may not be original and could infringe on intellectual property rights. Furthermore, there are concerns regarding the use of copyright-protected works in machine learning processes, which could lead to violations of authors' moral rights, such as attribution and integrity. Additionally, the advancement of AI algorithms has introduced challenges in academic evaluation, particularly in detecting plagiarism, where existing detection tools are vulnerable to attacks by AI-based tools. Overall, careful consideration and adherence to legal and ethical guidelines are essential when utilizing student submissions and unpublished works to train AI algorithms to mitigate these risks.
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| Papers (5) | Insight |
|---|---|
| Using student submissions and unpublished works in training advanced AI algorithms can lead to increased plagiarism risks due to vulnerabilities in existing plagiarism detection tools against AI-based cheating methods. | |
07 Nov 2022 1 Citations | Using student submissions and unpublished works in training advanced AI algorithms can lead to increased plagiarism risks due to vulnerabilities in existing plagiarism detection tools against AI-based cheating methods. |
| Not addressed in the paper. | |
05 May 2023 | Potential risks include legal and ethical concerns when using student submissions and unpublished works for AI training. Compliance with licensing, consent, and FAIR data principles is crucial to mitigate these risks. |
| Potential risks include unintentional plagiarism, lack of proper attribution, and compromising academic integrity when using student submissions and unpublished works to train advanced AI algorithms. |
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