This discussion focused on the ethical and societal implications of generative AI technologies such as DALL·E and ChatGPT, in the context of deep learning-based content creation. My initial post raised key concerns around ownership, misinformation, and algorithmic bias. I highlighted how tools trained on vast datasets may unintentionally infringe on intellectual property rights, using the example of AI-generated images mimicking the style of Studio Ghibli (O’Brien and Parvini, 2025). I also noted the risk of misuse in generating misleading content and the need for ethical safeguards (Floridi and Chiriatti, 2020; Weidinger et al., 2022).
Peer contributions extended these concerns to broader issues of alignment, interpretability, and the long-term implications of artificial general intelligence (AGI). Abdulhakim discussed the importance of ensuring that AI systems remain aligned with human values, citing the risk that systems may act autonomously in ways that conflict with societal goals (Ji et al., 2023). They also emphasised the need for interpretability, as opaque decision-making processes reduce our ability to detect bias and harmful outcomes (Selbst, 2019).
Guilherme explored how generative AI supports cultural preservation and enhances access to creative tools. For instance, voice cloning technologies and AI-assisted music generation offer opportunities for education and memory work (Culafic, Popovic, and Cakic, 2025). However, he also raised concerns about the erosion of creative labour and authenticity. Recreating the voice or style of an artist without consent was highlighted as a challenge to traditional copyright frameworks (Yang and Zhang, 2024).
Overall, the discussion reflected a shared understanding that generative AI holds great potential but must be deployed with care. Ethical challenges surrounding authorship, consent, interpretability, and fairness require ongoing attention and consideration. Moving forward, interdisciplinary collaboration and the development of clear policy guidelines will be crucial to balancing innovation with the protection of individual rights and societal values.
References
Culafic, I., Popovic, T. and Cakic, S. (2025) ‘Voice cloning and TTS for preservation and enrichment of cultural heritage’, in 2025 29th International Conference on Information Technology (IT). 2025 29th International Conference on Information Technology (IT), IEEE, pp. 1–5.
Floridi, L. and Chiriatti, M. (2020) ‘GPT-3: Its nature, scope, limits, and consequences’, Minds and machines, 30(4), pp. 681–694.
Ji, J., Qiu, T., Chen, B., Zhang, B., Lou, H., Wang, K., … & Gao, W. (2023). AI Alignment: A Comprehensive Survey. arXiv preprint arXiv:2310.19852. Available online at https://arxiv.org/abs/2310.19852
O’Brien, M. and Parvini, S. (2025) ChatGPT’s viral Studio Ghibli-style images highlight AI copyright concerns, AP News. Available at: https://apnews.com/article/studio-ghibli-chatgpt-images-hayao-miyazaki-openai-0f4cb487ec3042dd5b43ad47879b91f4 (Accessed: 18 April 2025).
Selbst, A.D. (2019) ‘Negligence and AI’s Human Users’.
Weidinger, L. et al. (2021) ‘Ethical and social risks of harm from Language Models’, arXiv [cs.CL]. Available at: http://arxiv.org/abs/2112.04359.
Yang, S.A. and Zhang, A.H. (2024) ‘Generative AI and Copyright: A Dynamic Perspective’, arXiv [econ.TH]. Available at: http://arxiv.org/abs/2402.17801.