As a researcher using an AI model for academic publication, if you publish the model's code/input-output, there are risks of copyright issues and third-party interference. How do you balance academic integrity and security in such cases? What methods do you apply in your own work? Or do you see the model as a mere 'content generator'? Let's discuss.
How is academic research protected in the realm of artificial intelligence?
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In my research, I use a multi-layered approach to protect sensitive data and models related to artificial intelligence. First and foremost, anonymization and tokenization at each layer are critical—I remove direct identifiers or sensitive examples (such as people's names, companies, etc.) when publishing data and replace them with placeholders. For instance, in medical image analysis, I delete patient names and share only the image data so that third parties cannot reconstruct it.
Regarding the model itself, I publish only the architecture and key parameters (such as hyperparameters), without sharing detailed weight data. This ensures the model's security while also supporting innovation in academia. Additionally, rather than adopting a "content producer" approach, I view the model as a tool—when publishing results, I transparently explain my methodology and the source of the data. While striking this balance, I prioritize originality, security, and academic integrity.