In recent years, Artificial Intelligence (AI) has made significant progress, influencing a variety of aspects of our daily existence. Advances in AI technology must be considered with ethical concerns. There are concerns about how autonomous and advanced AI systems may affect human rights, privacy, and society. This led to the creation of AI ethics, a new area that sets standards for responsible AI development and implementation. Descartes’ dualism serves as a fundamental framework for resolving inquiries regarding AI consciousness, moral agency, machine consciousness, moral agency, and ethical responsibility in the context of Artificial Intelligence (AI) ethics. In this paper, the relationship between Cartesian dualism and contemporary AI ethics is investigated, with a focus on the ways in which Descartes’ philosophical concepts inform current discussions regarding the ethical treatment of AI entities, the nature of machine consciousness, and the implications for human-machine interactions. The ethical implications of AI technology must be addressed in conjunction with these advancements. As AI systems become increasingly autonomous and sophisticated, there have been apprehensions about their potential impact on human rights, privacy, and society. This has resulted in the emergence of a new field, AI ethics, which aims to establish guidelines and principles for the responsible development and deployment of AI systems. In this paper, the applicability of Descartes’ concepts to the field of contemporary AI ethics is investigated. Specifically, it examines the ways in which Cartesian Dualism influences our comprehension of AI consciousness, the ethical treatment of AI entities, and the moral obligations of AI developers and users.
Binns, R. (2018). Fairness in machine learning. Proceedings of the 2018 ACM Conference on Fairness, Accountability, and Transparency.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Cath, C. (2018). Governing artificial intelligence: Ethical, legal, and technical opportunities and threats. Philosophy & Technology, 31(4), 521-537. https://doi.org/10.1007/s13347-018-0337-0
Chalmers, D. J. (2010). The character of consciousness. Oxford University Press.
Chen, J., & Chien, S. (2019). Autonomous vehicles: Technology, policy, and practice. Springer.
Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.
Dennett, D. C. (1991). Consciousness explained. (P. Weiner, Illustrator). Little, Brown and Co.
Descartes, R. (1641). Meditations on first philosophy. In E. S. Haldane (Trans.), The philosophical works of Descartes (1911 edition). Internet Encyclopedia of Philosophy. Retrieved from http://www.iep.utm.edu
Dreyfus, H. L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Artificial Intelligence, 171(6), 1137-1160. https://doi.org/10.1016/j.artint.2007.04.002
Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Floridi, L. (2019). The ethics of artificial intelligence. Principles, challenges, and opportunities. Oxford university press.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014) (pp. 3965-3971). https://doi.org/10.1109/IROS.2014.6942946
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Mulligan, D. K., & Binns, R. (2020). The ethics of artificial intelligence: An overview. In Handbook of ethics, values, and technological design.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS 2017). https://arxiv.org/abs/1706.03762
Blackshaw, B. P. (2023). Artificial consciousness is morally irrelevant. AJOB Neuroscience, 14(2), 72-74.
Hildt, E. (2023). The prospects of artificial consciousness: Ethical dimensions and concerns. AJOB Neuroscience, 14(2), 58-71.
Cottingham, J. (2008). Cartesian reflections. Oxford University Press.
Alanen, L. (1989). Descartes’s dualism and the philosophy of mind. Revue de Métaphysique et de Morale, 391-413.
Descartes, R. (1954). Discourse on method. Harmondsworth, Penguin
Descartes, R. (1951). Meditations on first philosophy. Newcomb Livraria Press.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994.
Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications, and future trends. Journal of Management Analytics, 6(1), 1-29.
Aggarwal, C. C. (2018). Neural networks and deep learning (Vol. 10, No. 978, p. 3). Cham: Springer.
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning, and deep learning in advanced robotics: A review. Cognitive Robotics, 3, 54-70.
Gupta, N. (2021). Introduction to hardware accelerator systems for artificial intelligence and machine learning. In Advances in Computers (Vol. 122, pp. 1-21). Elsevier.
Myers, D., Mohawesh, R., Chellaboina, V. I., Sathvik, A. L., Venkatesh, P., Ho, Y. H., … & Jararweh, Y. (2024). Foundation and large language models: Fundamentals, challenges, opportunities, and social impacts. Cluster Computing, 27(1), 1-26.
Suryanarayana, G. (2023). AI applications of drones. In Drone Technology: Future Trends and Practical Applications (p. 153).
Heidari, A., Jafari Navimipour, N., Unal, M., & Toumaj, S. (2022). Machine learning applications for COVID-19 outbreak management. Neural Computing and Applications, 34(18), 15313-15348.
Zhang, Y., Wu, M., Tian, G. Y., Zhang, G., & Lu, J. (2021). Ethics and privacy of artificial intelligence: Understandings from bibliometrics. Knowledge-Based Systems, 222, 106994.
Murdoch, B. (2021). Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Medical Ethics, 22, 1-5.
Bakare, S. S., Adeniyi, A. O., Akpuokwe, C. U., & Eneh, N. E. (2024). Data privacy laws and compliance: A comparative review of the EU GDPR and USA regulations. Computer Science & IT Research Journal, 5(3), 528-543.
Geiger, R. S., Tandon, U., Gakhokidze, A., Song, L., & Irani, L. (2023). Rethinking artificial intelligence: Algorithmic bias and ethical issues | Making algorithms public: Reimagining auditing from matters of fact to matters of concern. International Journal of Communication, 18, 22.
Kerasidou, A. (2021). Ethics of artificial intelligence in global health: Explainability, algorithmic bias, and trust. Journal of Oral Biology and Craniofacial Research, 11(4), 612-614.
Modi, T. B. (2023). Artificial intelligence ethics and fairness: A study to address bias and fairness issues in AI systems, and the ethical implications of AI applications. Revista Review Index Journal of Multidisciplinary, 3(2), 24-35.
Dignum, V. (2020). Responsibility and artificial intelligence. In The Oxford Handbook of Ethics of AI (pp. 4698, 215).
Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technology in Society, 63, 101421.
7 November 2024