Ethics of Artificial Intelligence: Impact on Society

Öz

We can define artificial intelligence systems as systems that serve the basic roles of society today, benefit us in many application areas, and can make autonomous decisions in the coming years, perhaps without the need for humans. In order for artificial intelligence systems to work with more and more autonomy, that is, with less human control, ethical structures must first be established. Ethical AI is AI that adheres to well-defined ethical guidelines regarding core values such as individual rights, privacy, equality and non-prejudice. Artificial intelligence ethical practices will help organizations operate more efficiently, produce cleaner products, reduce harmful environmental impacts, increase public safety and improve human health. Unethical artificial intelligence applications may cause serious harmful effects for society. The most important solution to responsibly manage these negative effects and direct artificial intelligence systems for the benefit of society is the development of ethical artificial intelligence systems. In recent years, studies on the ethics of artificial intelligence by academia, industry, government and civil society have begun to provide a basis. In this study, the ethics of artificial intelligence and its impact on society, labor market, inequality, privacy and prejudice are discussed, possible risks and threats are pointed out, and suggestions are made for solutions.

Anahtar Kelimeler

• Inequality
• privacy
• the labor market
• bias
• ethics of artificial intelligence

Yapay Zekâ Etiği: Toplum Üzerine Etkisi

Öz

Yapay zekâ sistemlerini, günümüzde toplumun temel rollerine hizmet eden, birçok uygulama alanında bizlere fayda sağlayan ve gelecek yıllarda belki biz insanlara ihtiyaç duymadan, özerk kararlar alabilen sistemler olarak tanımlayabiliriz. Yapay zekâlı sistemlerin giderek daha fazla özerklikle, yani daha az insan denetimi ile çalışabilmesi için öncelikle etik yapılarının oluşturulması gerekmektedir. Etik yapay zekâ, bireysel haklar, mahremiyet, eşitlik ve ön yargı yapmama gibi temel değerlerle ilgili iyi tanımlanmış etik yönergelere bağlı kalan yapay zekâdır. Yapay zekâ etik uygulamaları, kuruluşların daha verimli çalışmasına, daha temiz ürünler üretmesine, zararlı çevresel etkileri azaltmasına, kamu güvenliğini artırmasına ve insan sağlığını iyileştirmesine yardımcı olacaktır. Etik olmayan yapay zekâ uygulamaları ise toplum için ciddi zararlı etkilere neden olabilecektir. Bu olumsuz etkileri sorumlu bir şekilde yönetmek ve yapay zekâ sistemlerini toplum yararına yönlendirmek için en önemli çözüm, etik yapay zekâ sistemlerinin geliştirilmesidir. Son yıllarda akademi, endüstri, hükümet ve sivil toplum tarafından yapay zekâ etiği ile ilgili yapılan çalışmalar bir temel sağlamaya başlamıştır. Bu çalışmada, yapay zekâ etiği ve toplum üzerine etkisi, iş gücü piyasası, eşitsizlik, gizlilik ve ön yargı konu başlıkları üzerinde tartışılıp, olası risklere ve tehditlere dikkat çekilmekte ve çözümü için önerilerde bulunulmaktadır.

Anahtar Kelimeler

• Eşitsizlik
• gizlilik
• iş piyasası
• ön yargı
• yapay zeka etiği

Kaynakça

1. Aksoy, H. (2021). Yapay zekâlı varlıklar ve ceza hukuku. Uluslararası Ekonomi Siyaset İnsan ve Toplum Bilimleri Dergisi, 4(1): 10-27.
2. Ashok, M., Madan, R., Joha, A., Sivarajah, U. (2022). Ethical framework for artificial intelligence and digital technologies. International Journal of Information Management, 62, 102433; DOI: 10.1016/j.ijinfomgt.2021.102433
3. Baker Brunnbauer, J. (2021). Management perspective of ethics in artificial intelligence. AI and Ethics, 1(2); 173-181.
4. Bélisle Pipon, J. C., Monteferrante, E., Roy, M.C., Couture, V. (2022). Artificial intelligence ethics has a black box problem. AI & SOCIETY, 1-16.
5. Biavaschi, C., Eichhorst, W., Giulietti, C., Kendzia, M. J., Muravyev, A., Schmidl, R., Zimmermann, K. F. (2013). Youth unemployment and vocational training. Now Publishers Incorporated, Germany.
6. Bostrom, N., Yudkowsky, E. (2014). The Cambridge handbook of artificial intelligence. In: The ethics of artificial intelligence. Frankish, K., Ramsey, W. M. (eds.), Cambridge University Press, Cambridge, UK, 316-334.
7. Brendel, A.B., Mirbabaie, M., Lembcke, T.B., Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4): 1974.
8. Bryson, J.J. (2019). The past decade and future of AI’s impact on society. Towards a new enlightenment, Turner, Madrid, Spain, 150-185.

9. Datta, A., Tschantz, M. C., Datta, A. (2015). Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination. Proceedings on Privacy Enhancing Technologies, 1: 92–112.
10. David, H.J.J.O.E.P. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3): 3-30.
11. Deaton, A. (2013). The great escape: health, wealth, and the origins of inequality. Princeton University Press, ABD.
12. 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.
13. Furman, J., Seamans, R. (2019). AI and the economy. Innovation policy and the economy, 19(1): 161-191.
14. Hovy, D., Yang, D. (2021). The importance of modeling social factors of language: Theory and practice. The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 6–11, 2021, Book of Proceedings, 588–602.
15. Hurtado, J.V., Londoño, L., Valada, A. (2021). From Learning to relearning: A framework for diminishing bias in social robot navigation. Frontiers in Robotics and AI, 8: 69-88.
16. Iyengar, S., Sood, G., Lelkes, Y. (2012). Affect, not ideologya social identity perspective on polarization. Public Opinion Quarterly, 76(3): 405-431.
17. McCarty, N.M., Poole, K.T., Rosenthal, H. (2016). Polarized America: The dance of ideology and unequal riches. MIT Press, Cambridge, UK.
18. Mokyr, J., Vickers, C., Ziebarth, N. L. (2015). The history of technological anxiety and the future of economic growth: Is this time different? Journal of Economic Perspectives, 29(3): 31-50.
19. Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Gibson, J. (2021). Artificial intelligence for good health: a scoping review of the ethics literature. BMC Medical Ethics, 22(1); 1-17.
20. Nath, R., Manna, R. (2021). From posthumanism to ethics of artificial intelligence. AI & SOCIETY, 1-12.
21. Owe, A., Baum, S. D. (2021). Moral consideration of nonhumans in the ethics of artificial intelligence. AI and Ethics, 1(4); 517-528.
22. Pasquale, F. (2015). The Black Box Society: The secret algorithms that control money and information. Harvard University Press, UK.
23. Perkins, C. D. (1983). The long-term impact of technology on employment and unemployment. National Academy Press, USA.
24. Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., Stern, M. J. (2015). Digital inequalities and why they matter. Information, Communication & Society, 18(5): 569-582.
25. Roosen, J. (2020). AI Ethics: Why does it matter. Institute for Ethics in Artificial Intelligence, 1-7.
26. Selinger, E., Hartzog, W. (2017). Obscurity and privacy. In: Spaces for the future: A companion to philosophy of technology, Pitt, J., Shew, A. (eds.), New York, USA.
27. Spielkamp, M. (2017). Inspecting algorithms for bias. Technology Review, 120(4): 96-98.
28. Stahl, B. C., Antoniou, J., Ryan, M., Macnish, K., Jiya, T. (2022). Organisational responses to the ethical issues of artificial intelligence. AI & SOCIETY, 37(1); 23-37.
29. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Teller, A. (2016). Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence. Report of the 2015 Study Panel, September.
30. Turing, A. M. (2009). Computing machinery and intelligence. In: Parsing the turing test, 1: 23-65.
31. URL-1 (2019). https://www.canasean.com/the-montreal-declaration-for-the-responsible-development-of-artificial-intelligence-launched/ (Erişim Tarihi: 17.12.2021)
32. URL-10 (2020). https://www.ibm.com/tr-tr/cloud/watson-openscale (Erişim Tarihi: 19.12.2021)
33. URL-2 (2020). https://www.un.org/en/chronicle/article/towards-ethics-artificial-intelligence (Erişim Tarihi: 17.12.2021)
34. URL-3 (2018). https://futureoflife.org/2018/01/10/shared-benefit-principle/ Erişim Tarihi: 17.12.2021)
35. URL-4 (2018). https://www.ntv.com.tr/galeri/teknoloji/tesla-semi-ilk-defa-yollarda,0kjbif0chEW6BtYpKzPC2w/wsbI1-DF1UqWVK_eA2xdkA (Erişim Tarihi: 18.12.2021)
36. URL-5 (2016). https://obamawhitehouse.archives.gov/sites/whitehouse.gov/files/documents/Artificial-Intelligence-Automation-Economy.pdf (Erişim Tarihi: 18.12.2021)
37. URL-6 (2017). https://www.theguardian.com/politics/2017/feb/26/robert-mercer-breitbart-war-on-media-steve-bannon-donald-trump-nigel-farage (Erişim Tarihi: 18.12.2021)
38. URL-7 (2018). https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scrapssecret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G (Erişim Tarihi: 19.12.2021)
39. URL-8 (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing (Erişim Tarihi: 20.12.2021)
40. URL-9 (2020). https://www.acm.org/binaries/content/assets/public-policy/ustpc-facial-recognition-tech-statement.pdf (Erişim Tarihi: 20.12.2021)
41. Zhang, B., Anderljung, M., Kahn, L., Dreksler, N., Horowitz, M. C., Dafoe, A. (2021). Ethics and governance of artificial intelligence: Evidence from a survey of machine learning researchers. Journal of Artificial Intelligence Research, 71; 591-666.
42. 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.