The Question Isn't If AI. It's How
ANGELA'S SYMPOSIUM 📖 Academic Study on Witchcraft, Paganism, esotericism, magick and the OccultShow Notes
I keep being accused of using AI. I've even been accused, more than once, of being AI-generated. So I owe you something better than irritation: an actual explanation of where I stand. In this episode, I work through the real concerns: the scraping of artists' work, the environmental cost, algorithmic bias, the fear of job displacement, the worry about deskilling, and argue that every one of them is a problem of how, not of whether. They are arguments for regulation, not for personal abstention. I talk about my own practice (yes, AI images sometimes; yes, Grammarly; no, not the writing or the thinking), about teaching at university in the middle of all this, and about why, as an anthropologist, I think this debate is really a debate about authorship and authenticity wearing a technological costume. The question, in the end, was never if AI. It was always, only, how.
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REFERENCES 📚 Cognitive offloading and deskilling Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002 Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006 Training data, consent, and copyright Buick, A. (2025). Copyright and AI training data – transparency to the rescue? Journal of Intellectual Property Law & Practice, 20(3), 182–192. https://doi.org/10.1093/jiplp/jpae102 Dornis, T. W., & Stober, S. (2025). Generative AI training and copyright law. Transactions of the International Society for Music Information Retrieval, 8(1). https://doi.org/10.48550/arXiv.2502.15858 Algorithmic bias and discrimination Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31 Rosenthal-von der Pütten, A. M., & Sach, A. (2024). Michael is better than Mehmet: Exploring the perils of algorithmic biases and selective adherence to advice from automated decision support systems in hiring. Frontiers in Psychology, 15, 1416504. https://doi.org/10.3389/fpsyg.2024.1416504 Labour displacement and the reshaping of work Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3 Environmental cost of AI Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355 Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). Environmental cost of AI's energy use: Carbon, water and land footprints. United Nations University Institute for Water, Environment and Health. https://doi.org/10.53328/INR26RMA002 Regulatory competition and the US–EU asymmetry Smuha, N. A. (2021). From a 'race to AI' to a 'race to AI regulation': Regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57–84. https://doi.org/10.1080/17579961.2021.1898300
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