ENTERPRISES OF THE FUTURE WITHIN THE FRAMEWORK OF ETHICAL ARTIFICIAL INTELLIGENCE: TRANSFORMATION AND PARADIGM CHANGES

Year 2020, Volume: 8 Issue: 5, 290 - 305, 29.12.2020

Utku Köse

https://doi.org/10.21923/jesd.833224

Cited By: 4

Abstract

Although the 21st Century is a time period in which the innovative solutions of Artificial Intelligence are felt intensely in daily life, it is engraved in the memories as a rapidly advancing century under the leadership by Artificial Intelligence based technologies. While Artificial Intelligence continue to build the future of humanity and the world with autonomous intelligent systems, they also bring various anxieties. Especially, it is a matter of curiosity how ethical and moral factors pushing people to paradoxical situations will be evaluated by intelligent systems, and it is often discussed whether such systems will be a threat for human life. Based on the explanations so far, objective of this study is to discuss various transformation processes and also recent paradigm changes that may be important for enterprises of the future, by considering the scope of Ethical Artificial Intelligence. In this context, general information regarding essentials of Artificial Intelligence and its applications in enterprises were given first, and then possible problems on ethical scope and solution suggestions were discussed. It is thought that this study will be a reference for Artificial Intelligence applications in enterprises of the future, and its management in the related context.

Keywords

Artificial intelligence, Ethical Artificial Intelligence, Enterprises of the Future, Machine Learning, Future of Artificial Intelligence

YAPAY ZEKA ETİĞİ ÇERÇEVESİNDE GELECEĞİN İŞLETMELERİ: DÖNÜŞÜM VE PARADİGMA DEĞİŞİKLİKLERİ

Abstract

21. Yüzyıl, Yapay Zeka’nın yenilikçi çözümlerinin günlük hayatta yoğun bir şekilde hissedildiği bir zaman periyodu olmakla birlikte, Yapay Zeka tabanlı teknolojilerin önderliğinde hızla ilerleyen bir yüzyıl olarak hafızalara kazınmış durumdadır. Yapay Zeka insanlığın ve dünyanın geleceğini otonom zeki sistemler üzerinde inşa etmeye devam etmekle beraber, çeşitli endişeleri de beraberinde getirmektedir. Özellikle insanları da paradoksal durumlara iten etik ve ahlaki unsurların zeki sistemler tarafından nasıl değerlendirileceği merak konusu olmakta; hatta bu tür sistemlerin insan hayatına karşı tehdit taşıyıp taşımayacakları da sıklıkla tartışılmaktadır. Açıklamalardan hareketle bu çalışmanın amacı, Yapay Zeka Etiği ölçeğinde geleceğin işletmeleri açısından önem arz edebilecek çeşitli dönüşüm süreçlerini ve aynı zamanda güncel paradigma değişikliklerini ele almaktır. Bu bağlamda, öncelikli olarak Yapay Zeka’nın temellerine ve işletmeler tarafında nasıl uygulandığına yönelik genel bilgiler verilmiş, akabinde etik ölçekte olası problemler ve çözüm önerileri üzerine tartışılmıştır. Çalışmanın geleceğin işletmelerinde Yapay Zeka uygulamalarına ve Yapay Zeka’nın bu çerçevede yönetimine ilişkin çalışmalara ışık tutacağı düşünülmektedir.

Keywords

Yapay Zeka, Yapay Zeka Etiği, Geleceğin İşletmeleri, Makine Öğrenmesi, Yapay Zeka’nın Geleceği

References

• Affonso, C., Rossi, A. L.D., Vieira, F.H.A., de Leon Ferreira, A.C.P., 2017. Deep learning for biological image classification. Expert Systems with Applications, 85, 114-122.
• Alpaydın, E., 2016. Machine learning: the new AI. MIT Press.
• Apte, C., 2010. The role of machine learning in business optimization. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 1-2.
• Auer, M.E. (Ed.), 2019. Cyber-physical Systems and Digital Twins: Proceedings of the 16th International Conference on Remote Engineering and Virtual Instrumentation (Vol. 80). Springer.
• Barrat, J., 2013. Our final invention: Artificial intelligence and the end of the human era. Macmillan.
• Bose, I., Mahapatra, R.K., 2001. Business data mining—a machine learning perspective. Information & management, 39(3), 211-225.
• Bostrom, N., 2014. Superintelligence: Paths, dangers, strategies. Oxford.
• Bostrom, N., Yudkowsky, E., 2014. The ethics of artificial intelligence. The Cambridge handbook of artificial intelligence, 316, 334.
• Brink, H., Richards, J., Fetherolf, M. 2016. Real-world machine learning. Manning Publications Co..
• Bruun, E.P., Duka, A., 2018. Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13(2).
• Buckley, W., Wiener, N., 2017. Cybernetics in History. In Systems Research for Behavioral Science (pp. 31-36). Routledge.
• Cai, X., Zhang, N., Venayagamoorthy, G. K., Wunsch, D. C., 2004. Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) (Vol. 2, pp. 1647-1652). IEEE.
• Calvo, R.A., Peters, D., Vold, K., Ryan, R.M., Burr, C., Floridi, L., 2020. Supporting human autonomy in AI systems: A framework for ethical enquiry. In Ethics of Digital Well-Being: A Multidisciplinary Approach. Springer.
• Carvalho, D.R., Freitas, A.A., 2004. A hybrid decision tree/genetic algorithm method for data mining. Information Sciences, 163(1-3), 13-35.
• Castillo-Chavez, C., Curtiss, R., Daszak, P., Levin, S.A., Patterson-Lomba, O., Perrings, C., ..., Towers, S., 2015. Beyond Ebola: Lessons to mitigate future pandemics. The Lancet Global Health, 3(7), e354-e355.
• Cellan-Jones, R., 2014. Stephen Hawking warns artificial intelligence could end mankind. BBC news, 2, 2014.
• Cevizoğlu, H., 2019a. Beden ve Teknoloji (Felsefi ve Antropolojik Soruşturma), Ankara: Bilim ve Sanat Yayınevi.
• Cevizoğlu, H., 2019b. Kitle Psikolojisi (Benlik, İntihar ve Kolektif Narsisizm Çözümlemeleri), Ankara: Bilim ve Sanat Yayınevi.
• Cevizoğlu, H., 2019c. Yapay Zekâ, Teknoloji Felsefesi ve Toplumsal Yaşam, Yapay Zekâ ve Gelecek, İstanbul: Doğu Kitabevi.
• Chang, J., 2020. 50+ Vital Artificial Intelligence Statistics: 2020 Data Analysis & Market Share. Finances Online. Çevrimiçi: https://financesonline.com/artificial-intelligence-statistics/ (Erişim 06 Aralık 2020).
• Charniak, E., 2019. Introduction to deep learning. MIT Press.
• Cios, K.J., Pedrycz, W., Swiniarski, R.W., 2012. Data mining methods for knowledge discovery (Vol. 458). Springer Science & Business Media.
• Copeland, J., 1993. Artificial intelligence: A philosophical introduction. Oxford.
• Dean, J., 2014. Big data, data mining, and machine learning: value creation for business leaders and practitioners. John Wiley & Sons.
• Deperlioğlu, Ö., 2018. Classification of phonocardiograms with convolutional neural networks. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2), 22-33.
• Deperlioğlu, Ö., 2019. Classification of segmented phonocardiograms by convolutional neural networks. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 10(2), 5-13.
• Dirican, C., 2015. The impacts of robotics, artificial intelligence on business and economics. Procedia-Social and Behavioral Sciences, 195, 564-573.
• Eigenstetter, M., 2020. Ensuring Trust in and Acceptance of Digitalization and Automation: Contributions of Human Factors and Ethics. In International Conference on Human-Computer Interaction (pp. 254-266). Springer, Cham.
• Floridi, L., 2020. What the near future of artificial intelligence could be. In The 2019 Yearbook of the Digital Ethics Lab (pp. 127-142). Springer, Cham.
• Ford, M., 2018. Robotların yükselişi: Yapay zeka ve işsiz bir gelecek tehlikesi. Duran, Cem (Çev.), İstanbul: Kronik Yayınevi.
• Forsyth, D.A., Ponce, J., 2002. Computer vision: a modern approach. Prentice Hall Professional Technical Reference.
• Frank, M., Roehrig, P., Pring, B., 2017. What to do when machines do everything: How to get ahead in a world of AI, algorithms, bots, and Big Data. John Wiley & Sons.
• Fuller, A., Fan, Z., Day, C., Barlow, C., 2020. Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971.
• Gadre, M., Deoskar, A., 2020. Industry 4.0–Digital Transformation, Challenges and Benefits. International Journal of Future Generation Communication and Networking, 13(2), 139-149.
• Ginsberg, M., 2012. Essentials of artificial intelligence. Newnes.
• Goertzel, B., 2007. Artificial general intelligence (Vol. 2). C. Pennachin (Ed.). New York: Springer.
• Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep learning. MIT Press.
• Han, J., Kamber, M., Pei, J., 2000. Data mining: Concepts and techniques. Morgan Kaufmann.
• Hansell, G.R. 2011. H+/-: Transhumanism and its Critics. Xlibris Corporation.
• Hartmann, D., Van der Auweraer, H., 2020. Digital Twins. arXiv preprint arXiv:2001.09747.
• Herbst, J., 2000. A machine learning approach to workflow management. In European conference on machine learning (pp. 183-194). Springer, Berlin, Heidelberg.
• Ionescu, L., Andronie, M., 2019. The Future of Jobs in the Digital World. In International Conference ICESBA, Bucharest (pp. 15-16).
• Jarrahi, M.H., 2018. Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
• Karaboğa, D., 2014. Yapay zeka optimizasyon algoritmaları. Nobel Akademik Yayıncılık.
• Khokhar, S., Zin, A.A.B.M., Mokhtar, A.S.B., Pesaran, M., 2015. A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews, 51, 1650-1663.
• Kline, R.R., 2015. The cybernetics moment: Or why we call our age the information age. JHU Press.
• Kobayashi, T., Simon, D.L., 2005. Hybrid neural-network genetic-algorithm technique for aircraft engine performance diagnostics. Journal of Propulsion and Power, 21(4), 751-758.
• Köse, U., 2017. Yapay zeka tabanlı optimizasyon algoritmaları geliştirilmesi. Doktora Tezi, Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği ABD.
• Köse, U., 2018a. Are we safe enough in the future of artificial intelligence? A discussion on machine ethics and artificial intelligence safety. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2), 184-197.
• Köse, U., 2018b. Yapay zeka ve gelecek: Endişelenmeli miyiz? Bilim ve Ütopya, 24(284), 39-44.
• Köse, U., 2018c. Yapay zeka: Geleceğin biliminde paradokslar. Popüler Bilim Dergisi, 25 (261), 12-21.
• Köse, U., 2019. Yapay zeka ve geleceğin siber savaşları. Bilim ve Teknik Dergisi, 52(618), 76-84.
• Kurzweil, R., 2005. The singularity is near: When humans transcend biology. Penguin.
• Lee, J., Suh, T., Roy, D., Baucus, M., 2019. Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.
• Leonhard, G. (2018). Teknolojiye Karşı İnsanlık. Akkartal, Cihan ve Akkartal, İlker (Çev.), İstanbul: Siyah Kitap.
• Lin, Y., 2020. 10 Artificial Intelligence Statistics You Need to Know in 2020. Oberlo.com Blog. Çevrimiçi: https://www.oberlo.com/blog/artificial-intelligence-statistics (Erişim 06 Aralık 2020).
• Livet, P., Varenne, F., 2020. Artificial Intelligence: Philosophical and Epistemological Perspectives. In A Guided Tour of Artificial Intelligence Research (pp. 437-455). Springer, Cham.
• Makridakis, S., 2017. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
• Manning, C.D., Manning, C.D., Schütze, H., 1999. Foundations of statistical natural language processing. MIT Press.
• McCarthy, J., 1988. Mathematical logic in artificial intelligence. Daedalus, 297-311.
• McCarthy, J., 1989. Artificial intelligence, logic and formalizing common sense. In Philosophical logic and artificial intelligence (pp. 161-190). Springer, Dordrecht.
• McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E., 2006. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Magazine, 27(4), 12-12.
• Meihami, B., Meihami, H., 2014. Knowledge Management a way to gain a competitive advantage in firms (evidence of manufacturing companies). International letters of social and humanistic sciences, 3(14), 80-91.
• Michalewicz, Z., Schmidt, M., Michalewicz, M., Chiriac, C., 2006. Adaptive business intelligence (pp. 37-46). Springer Berlin Heidelberg.
• Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D., 2020. Image segmentation using deep learning: A survey. arXiv preprint arXiv:2001.05566.
• Mitchell, T.M., 1997. Machine learning. McGraw Hill.
• Mohammadi, K., Shamshirband, S., Tong, C.W., Arif, M., Petković, D., Ch, S., 2015. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion and Management, 92, 162-171.
• Muntean, M., Mircea, G., 2007. Business intelligence solutions for gaining competitive advantage. Informatica Economica Journal, XI, 3, 22-25.
• Nabiyev, V.V., 2005. Yapay zeka: problemler-yöntemler-algoritmalar. Seçkin Yayıncılık.
• Nielsen, M.A., 2015. Neural networks and deep learning (Vol. 25). Determination Press.
• Novikov, D.A., 2015. Cybernetics: From past to future (Vol. 47). Springer.
• Paschek, D., Luminosu, C.T., Draghici, A., 2017. Automated business process management–in times of digital transformation using machine learning or artificial intelligence. In MATEC Web of Conferences (Vol. 121, p. 04007). EDP Sciences.
• Pavaloiu, A., Köse, U., 2017. Ethical Artificial Intelligence-An Open Question. Journal of Multidisciplinary Developments, 2(2), 15-27.
• Piano, S.L., 2020. Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanities and Social Sciences Communications, 7(1), 1-7.
• Pompa, C., 2015. Jobs for the Future. Report-Shaping Policy for Development. Overseas Development Institute.
• Ransbotham, S., Kiron, D., Gerbert, P., Reeves, M., 2017. Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).
• Ruiz-Real, J.L., Uribe-Toril, J., Torres, J.A., De Pablo, J., 2020. Artificial intelligence in business and economics research: trends and future. Journal of Business Economics and Management, 1-20.
• Russell, S.J., Norvig, P., 2016. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
• Sankaran, S., Zhang, C., Gutierrez Lopez, M., Väänänen, K., 2020. Respecting Human Autonomy through Human-Centered AI. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (pp. 1-3).
• Scholkopf, B., Smola, A.J., 2001. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press.
• Shanahan, M., 2015. The technological singularity. MIT Press.
• Sharifzadeh, R., 2020. Does artifacts have morality? Bruno Latour and ethics of technology. Philosophy of Science, 9(18), 75-94.
• Silahtaroğlu, G., 2008. Veri madenciliği. Papatya Yayıncılık.
• Soni, N., Sharma, E.K., Singh, N., Kapoor, A., 2019. Impact of artificial intelligence on businesses: from research, innovation, market deployment to future shifts in business models. arXiv preprint arXiv:1905.02092.
• Stoitsis, J., Valavanis, I., Mougiakakou, S.G., Golemati, S., Nikita, A., Nikita, K.S., 2006. Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 569(2), 591-595.
• Turing, A., 1948:2004. Intelligent machinery (1948). B. Jack Copeland, 395.
• Turing, A.M., 1950:1995. Computing machinery and intelligence. Brian Physiology and Psychology, 213.
• UNCTAD (United Nations Conference on Trade and Development)., 2020. Coronavirus reveals need to bridge the digital divide. UNCTAD.org. Çevrimiçi: https://unctad.org/news/coronavirus-reveals-need-bridge-digital-divide (Erişim 06 Aralık 2020).
• Uzun, M.M., 2020. Yapay Zeka: Fırsatlar ve Tehditler. ULISA12. 2, 34-44.
• Ünal, A., 2019. İşletmelerde yapay zekaların icra kurulu başkanı olabilirliği üzerine bir araştırma. Doktora Tezi, Düzce Üniversitesi Sosyal Bilimler Enstitüsü, İşletme ABD.
• Üstündağ, A., Çevikcan, E., 2017. Industry 4.0: managing the digital transformation. Springer.
• Verbeek, P.P., 2009. Cultivating humanity: Towards a non-humanist ethics of technology. In New waves in philosophy of technology (pp. 241-263). Palgrave Macmillan, London.
• Watkins, C.J., Dayan, P., 1992. Q-learning. Machine learning, 8(3-4), 279-292.
• Webster, C., Ivanov, S., 2020. Robotics, artificial intelligence, and the evolving nature of work. In Digital Transformation in Business and Society (pp. 127-143). Palgrave Macmillan, Cham.
• Yang, X.S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M. (Eds.), 2013. Swarm intelligence and bio-inspired computation: theory and applications. Newnes.
• Zhu, X., Goldberg, A.B., 2009. Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning, 3(1), 1-130.