TIM-6074

Council for Open and Distance Education Oslo, Norway. https://search.informit.org/doi/pdf/10.3316/informit.T2024030500022600105330542
This paper investigates the ethical and integrity challenges of using generative AI in academic writing and publishing, focusing on authorship, ownership, and the need for transparency and new mechanisms to ensure integrity.

Chen, Z., Chen, C., Yang, G., He, X., Chi, X., Zeng, Z., & Chen, X. (2024). Research integrity in the era of artificial intelligence: Challenges and responses. Medicine, 103(27), e38811. https://journals.lww.com/md-journal/fulltext/2024/07050/research_integrity_in_the_era_of_artificial.41.aspx
This review examines how AI introduces new forms of academic misconduct like data fabrication and plagiarism, highlighting the need for stronger ethical norms, training, and review mechanisms to protect research integrity.

Limongi, R. (2024). The use of artificial intelligence in scientific research with integrity and ethics. Future Studies Research Journal: Trends and Strategies, 16(1), e845–e845. https://revistafuture.org/FSRJ/article/view/845
This paper discusses the ethical and integrity challenges of integrating AI into scientific research, such as algorithmic bias and issues of authorship, advocating for robust ethical governance, education, transparency, and accountability.

Mehrotra, S., Jorge, C. C., Jonker, C. M., & Tielman, M. L. (2024). Integrity-based explanations for fostering appropriate trust in AI agents. ACM Transactions on Interactive Intelligent Systems, 14(1), 1–36. https://doi.org/10.1145/3610578
This study investigates how expressing principles of integrity, such as honesty, transparency, and fairness, through explanations can help foster appropriate trust in AI agents.

Radanliev, P. (2025). AI ethics: Integrating transparency, fairness, and privacy in AI development. Applied Artificial Intelligence, 39(1), 2463722. https://doi.org/10.1080/08839514.2025.2463722
This research establishes an ethical framework for AI development focusing on transparency, fairness, and privacy, analyzing global policy frameworks and proposing technical and organizational strategies to mitigate biases and promote accountability.

van Genderen, M. E., van de Sande, D., Hooft, L., Reis, A. A., Cornet, A. D., Oosterhoff, J. H., van der Ster, B. J., Huiskens, J., Townsend, R., & van Bommel, J. (2024). Charting a new course in healthcare: Early-stage AI algorithm registration to enhance trust and transparency. NPJ Digital Medicine, 7(1), 119. https://www.nature.com/articles/s41746-024-01104-w
This paper advocates for the mandatory registration of early-stage AI algorithms, particularly in healthcare, to enhance trust, transparency, and mitigate biases in mission-critical applications.