Yapay Zekada Etik ve Güvenlik
Özet
Yapay zekâ (YZ) teknolojilerinin hızla gelişimi, sadece teknik ilerlemeleri değil, aynı zamanda etik ve güvenlik konularında derin tartışmaları da beraberinde getirmiştir. Sağlık, hukuk, eğitim, güvenlik gibi birçok alanda yaygınlaşan YZ sistemleri, bireysel haklardan toplumsal adalete kadar geniş bir yelpazede etik ikilemler yaratmaktadır. Veri mahremiyeti, algoritmik önyargılar, şeffaflık ve sorumluluk gibi başlıca konular bu teknolojilerin güvenilirliğini doğrudan etkilemektedir. Aynı zamanda kötüye kullanım, siber tehditler ve otonom sistemlerin kontrol dışı hareketleri, YZ'nin güvenlik boyutunu öne çıkarmaktadır.
Çalışmada, yapay zekâ etiği kavramsal ve teorik çerçevede ele alınmış, ardından temel etik ilkeler (adalet, şeffaflık, mahremiyet, hesap verebilirlik, zarar vermeme) tartışılmıştır. Bu ilkelerin mevcut uygulamalardaki karşılıkları örneklerle değerlendirilmiş, YZ teknolojilerinin güncel ve potansiyel güvenlik riskleri ele alınmıştır. Etik ve güvenliğin birbiriyle iç içe geçmiş yapılar olduğu vurgulanarak, sürdürülebilir ve sorumlu YZ geliştirimi için çok paydaşlı ve disiplinler arası bir yaklaşıma ihtiyaç olduğu belirtilmiştir. Çalışma, YZ'nin insanlık yararına etik temelde gelişimini hedefleyen normatif bir çerçeve sunmaktadır.
The rapid development of artificial intelligence (AI) technologies has sparked extensive debates not only on technical advancements but also on ethical and security dimensions. As AI systems are increasingly integrated into fields such as healthcare, law, education, and security, they raise significant ethical dilemmas ranging from individual rights to social justice. Key issues such as data privacy, algorithmic bias, transparency, and accountability directly affect the reliability and trustworthiness of AI. Simultaneously, malicious use, cybersecurity threats, and uncontrolled behavior of autonomous systems highlight the importance of AI safety.
This study discusses AI ethics within a conceptual and theoretical framework, exploring core ethical principles such as fairness, transparency, privacy, accountability, and non-maleficence. It evaluates how these principles are reflected in real-world AI applications through concrete examples and addresses the potential and emerging security risks of AI systems. Emphasizing the interdependence of ethics and safety, the study asserts that both must be considered together for responsible and sustainable AI development. Ultimately, the paper proposes a normative framework to ensure that AI technologies are developed and implemented in a way that aligns with human values and societal well-being through interdisciplinary and multi-stakeholder approaches.
Referanslar
Arslan, M., Topakkaya, Y., & Eyibaş, E. (2019). Yapay Zekâ ve Etik İlişkisi. Felsefe Dünyası, (70), 81-99.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671-732.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., & Garfinkel, B. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation (Report). Future of Life Institute.
Bryson, J. J., Diamantis, M. E., & Grant, T. D. (2017). Of, for, and by the people: the legal lacuna of synthetic persons. Artificial Intelligence and Law, 25(3), 273-291.
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT)* (pp. 77-91). PMLR.
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Vol. 81, pp. 77-91).
Chesney, R., & Citron, D. (2019). Deepfakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753-1820.
Çağatay, H. (2019). Yapay zekâ ve tekillik: teknolojik tekillik bize ne kadar yakın ve neden önemli? MetaZihin: Yapay Zeka ve Zihin Felsefesi Dergisi, 2(2), 231-242.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. New York, NY: St. Martin’s Press.
European Commission. (2019). Ethics guidelines for trustworthy AI. High-Level Expert Group on AI, Brussels.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People’s ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., et al. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
Kahn, L. (2022). Lethal autonomous weapon systems and respect for human dignity. Frontiers in Big Data, 5, 999293. https://doi.org/10.3389/fdata.2022.999293
Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175-183.
Moor, J. H. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4), 18-21.
Müller, V. C. (2020). Ethics of Artificial Intelligence and Robotics. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Fall 2020 ed.). Retrieved from https://plato.stanford.edu/entries/ethics-ai/
Okmeydan, S. B. (2017). Yeni iletişim teknolojilerini sorgulamak: Etik, güvenlik ve mahremiyetin kesiştiği nokta. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 5(1), 347-372.
Özdemir, L., & Bilgin, A. (2021). Sağlıkta yapay zekânın kullanımı ve etik sorunlar. Sağlık ve Hemşirelik Yönetimi Dergisi, 8(3), 439-445.
Öztürk Dilek, G. (2019). Yapay zekânın etik gerçekliği. AUSBD, 2(4), 47-59.
Turan, T., Turan, G., & Küçüksille, E. (2022). Yapay zekâ etiği: Toplum üzerine etkisi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 13(2), 292-299.
Veruggio, G. (2005). The birth of roboethics. In Proceedings of the Workshop on Robo-Ethics (pp. 1-4).
Yeşilkaya, N. (2022). Yapay zekâya dair etik sorunlar. Şarkiyat, 14(3), 948-963.
Referanslar
Arslan, M., Topakkaya, Y., & Eyibaş, E. (2019). Yapay Zekâ ve Etik İlişkisi. Felsefe Dünyası, (70), 81-99.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671-732.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., & Garfinkel, B. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation (Report). Future of Life Institute.
Bryson, J. J., Diamantis, M. E., & Grant, T. D. (2017). Of, for, and by the people: the legal lacuna of synthetic persons. Artificial Intelligence and Law, 25(3), 273-291.
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT)* (pp. 77-91). PMLR.
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Vol. 81, pp. 77-91).
Chesney, R., & Citron, D. (2019). Deepfakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753-1820.
Çağatay, H. (2019). Yapay zekâ ve tekillik: teknolojik tekillik bize ne kadar yakın ve neden önemli? MetaZihin: Yapay Zeka ve Zihin Felsefesi Dergisi, 2(2), 231-242.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. New York, NY: St. Martin’s Press.
European Commission. (2019). Ethics guidelines for trustworthy AI. High-Level Expert Group on AI, Brussels.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People’s ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., et al. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
Kahn, L. (2022). Lethal autonomous weapon systems and respect for human dignity. Frontiers in Big Data, 5, 999293. https://doi.org/10.3389/fdata.2022.999293
Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175-183.
Moor, J. H. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4), 18-21.
Müller, V. C. (2020). Ethics of Artificial Intelligence and Robotics. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Fall 2020 ed.). Retrieved from https://plato.stanford.edu/entries/ethics-ai/
Okmeydan, S. B. (2017). Yeni iletişim teknolojilerini sorgulamak: Etik, güvenlik ve mahremiyetin kesiştiği nokta. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 5(1), 347-372.
Özdemir, L., & Bilgin, A. (2021). Sağlıkta yapay zekânın kullanımı ve etik sorunlar. Sağlık ve Hemşirelik Yönetimi Dergisi, 8(3), 439-445.
Öztürk Dilek, G. (2019). Yapay zekânın etik gerçekliği. AUSBD, 2(4), 47-59.
Turan, T., Turan, G., & Küçüksille, E. (2022). Yapay zekâ etiği: Toplum üzerine etkisi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 13(2), 292-299.
Veruggio, G. (2005). The birth of roboethics. In Proceedings of the Workshop on Robo-Ethics (pp. 1-4).
Yeşilkaya, N. (2022). Yapay zekâya dair etik sorunlar. Şarkiyat, 14(3), 948-963.
