Derleme
BibTex RIS Kaynak Göster

A Compilation on the Social Implications and Critiques of Artificial Intelligence

Yıl 2024, , 239 - 259, 30.10.2024
https://doi.org/10.17828/yalovasosbil.1419070

Öz

This article summarizes the societal impacts of artificial intelligence technology, how society has responded to this technology, what the social and economic consequences of artificial intelligence usage are, and how the level of societal acceptance has developed throughout this process. The societal response and opposition to artificial intelligence have become complex issues alongside the rapid evolution and widespread adoption of the technology. Artificial intelligence is a rapidly advancing and effectively utilized technology in many sectors today. It is widely used in various fields, from manufacturing sector to healthcare, from education to security. However, the rapid rise of this technology has also brought some concerns and criticisms. There are societal concerns, particularly regarding the labor market, the privacy of personal life, ethical issues, and the replacement of human intelligence. Artificial intelligence, by automating work processes, can reduce the need for labor in certain sectors, which may lead to unemployment problems. Additionally, concerns arise from the possibility of individuals’ private lives being monitored by artificial intelligence, and ethical issues are a source of ongoing debate. Despite this, artificial intelligence has achieved significant successes in areas such as big data analytics, deep learning, and natural language processing. While society benefits from the positive impacts of artificial intelligence, a balanced approach can be adopted in the development and use of this technology, considering criticisms and concerns. In this context, the study provides suggestions on artificial intelligence, its development, the societal response and opposition to artificial intelligence, along with artificial intelligence design and development.

Proje Numarası

2146-1406

Kaynakça

  • Acemoglu, D. ve Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244.
  • Alam, F., Ofli, F. ve Imran, M. (2020). Descriptive and visual summaries of disaster events using artificial intelligence techniques: Case studies of Hurricanes Harvey, Irma, and Maria. Behaviour & Information Technology, 39(3), 288-318.
  • Araujo, T., Helberger, N., Kruikemeier, S. ve De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35, 611-623.
  • Ardebili, A. T. ve Rickertsen, K. (2020). Personality traits, knowledge, and consumer acceptance of genetically modified plant and animal products. Food Quality and Preference, 80, 103825.
  • Asan, O., Bayrak, A. E. ve Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of Medical Internet Research, 22(6), e15154.
  • Asimakopoulos, S., Asimakopoulos, G. ve Spillers, F. (2017). Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables. In Informatics, 4(1), 2-16.
  • Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30.
  • Baldauf, M., Fröehlich, P. ve Endl, R. (2020). Trust me, I’m a doctor–user perceptions of AI-driven apps for mobile health diagnosis. In Proceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia, 167-178.
  • Barnett, T., Pearson, A. W., Pearson, R. ve Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24, 374-390.
  • Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F. ve Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 1-16.
  • Bossmann, J. (2016). Top 9 ethical issues in artificial intelligence. In World Economic Forum.
  • Brooks, A. (2019). The benefits of AI: 6 societal advantages of automation. Rasmussen University. Available online: https://www.rasmussen.edu/degrees/technology/blog/benefits-of-ai/, Erişim Tarihi: 11.08.2024.
  • Brownsword, R. ve Harel, A. (2019). Law, liberty and technology: Criminal justice in the context of smart machines. International Journal of Law in Context, 15(2), 107–125.
  • Caporale, G. ve Monteleone, E. (2004). Influence of information about manufacturing process on beer acceptability. Food Quality and Preference, 15(3), 271-278.
  • Cappelen, H. ve Dever, J. (2021). Making AI intelligible: Philosophical foundations. Oxford University Press.
  • Carrasco, M., Mills, S., Whybrew, A. ve Jura, A. (2019). The citizen’s perspective on the use of AI in government. BCG Digital Government Benchmarking.
  • Cartwright, H. (2008). Using artificial intelligence in chemistry and biology: A practical guide. CRC Press.
  • Cave, S., Coughlan, K. ve Dihal, K. (2019). "Scary Robots" Examining Public Responses to AI. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 331-337.
  • Charness, N., Yoon, J. S., Souders, D., Stothart, C. ve Yehnert, C. (2018). Predictors of attitudes toward autonomous vehicles: The roles of age, gender, prior knowledge, and personality. Frontiers in Psychology, 9, 2589.
  • Chau, K. W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine pollution bulletin, 52(7), 726-733.
  • Chen, Y. N. K. ve Wen C. H. R. (2021). Impacts of attitudes toward government and corporations on public trust in artificial intelligence. Communication Studies, 72(1), 115-131.
  • Cherkasov, A., Hilpert, K., Jenssen, H., Fjell, C. D., Waldbrook, M., Mullaly, S. C. ve Hancock, R. E. (2009). Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS chemical biology, 4(1), 65-74.
  • Chiang, H. H., Wu, S. J., Perng, J. W., Wu, B. F. ve Lee, T. T. (2010). The human-in-the-loop design approach to the longitudinal automation system for an intelligent vehicle. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(4), 708-720.
  • Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (1251-1258).
  • Chuan, C. H., Tsai, W. H. S. ve Cho, S. Y. (2019). Framing artificial intelligence in American newspapers. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (339-344).
  • Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P. ve Malhotra, S. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute, 2.
  • Chuquicusma, M. J., Hussein, S., Burt, J. ve Bagci, U. (2018). How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (240-244). IEEE.
  • Circiumaru, A. (2022). Futureproofing EU Law the Case of Algorithmic Discrimination. Oxford: Oxford University.
  • Darko, A., Chan, A. P., Adabre, M. A., Edwards, D. J., Hosseini, M. R. ve Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, 103081.
  • Deloitte, (2019). Automotive Consumer Study, Advanced vehicle Technologies, https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/consumer-business/Deloitte-Automotive-Consumer-Study-2019.pdf, Erişim Tarihi: 11.10.2023.
  • Devaraj, S., Easley, R. F. ve Crant, J. M. (2008). Research note -how does personality matter? Relating the five-factor model to technology acceptance and use. Information systems research, 19(1), 93-105.
  • Devlin, J., Chang, M. W., Lee, K., Toutanova ve K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Doward, J. (2018). Britain funds research into drones that decide who they kill, says report. Sat, 10, 13-59. Elbadawi, M., McCoubrey, L. E., Gavins, F. K., Ong, J. J., Goyanes, A., Gaisford, S. ve Basit, A. W. (2021). Harnessing artificial intelligence for the next generation of 3D printed medicines. Advanced Drug Delivery Reviews, 175, 113805.
  • Elliott, L. (2015). Robots threaten 15 m UK jobs, says Bank of England’s chief economist. The Guardian, 12.
  • Eurobarometer. (2017). Attitudes towards the Impact of Digitisation and Automation on Daily Life. Available online: https://europa.eu/eurobarometer/surveys/detail/2160, Erişim Tarihi: 11.10.2023.
  • Eurobarometer. (2017). Attitudes towards the Impact of Digitisation and Automation on Daily Life, https://europa.eu/eurobarometer/surveys/detail/2160, Erişim Tarihi: 25.10.2023.
  • Fast, E. ve Horvitz, E. (2017). Long-term trends in the public perception of artificial intelligence. In Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 963-969.
  • Fatehi, A. ve Huang, B. (2017). Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay. Journal of Process Control, 53, 15-25.
  • Frey, C. B. ve Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
  • Fry, H. (2018). How do we stop cutting-edge technology falling into the wrong hands? The Guardian.
  • Gansser, O. A. ve Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535.
  • Garimella, K. (2018). Job loss from AI? There’s more to fear. Forbes. https://www.forbes.com/sites/cognitiveworld/2018/08/07/job-loss-from-ai-theres-moreto-fear, Erişim Tarihi: 05.11.2023.
  • Gaskell, G., Allum, N., Wagner, W., Kronberger, N., Torgersen, H., Hampel, J. ve Bardes, J. (2004). GM foods and the misperception of risk perception. Risk Analysis: An International Journal, 24(1), 185-194.
  • Gasparetto, A. ve Scalera, L. (2019a). From the unimate to the delta robot: the early decades of industrial robotics. In Explorations in the History and Heritage of Machines and Mechanisms: Proceedings of the 2018 HMM IFToMM Symposium on History of Machines and Mechanisms, Springer International Publishing, 284-295. Gasparetto, A. ve Scalera, L. (2019b). A brief history of industrial robotics in the 20th century. Advances in Historical Studies, 8, 24-35.
  • Gerlich, M. (2023). The Power of Virtual Influencers: Impact on Consumer Behaviour and Attitudes in the Age of AI. Administrative Sciences, 13(8), 178.
  • Gillespie, N., Lockey, S. ve Curtis, C. (2021). Trust in artificial intelligence: A five country study.
  • Gillham, J., Rimmington, L., Dance, H. Verweij, G., Rao, A., Roberts, B. K. ve Paich, M. (2018). The macroeconomic impact of artificial intelligence. Retrieved from Price waterhouse Coopers, https://www.pwc.co.uk/economic-services/assets/macroeconomic-impact-of-aitechnical-report-feb-18.pdf, Erişim Tarihi: 23.09.2023.
  • Graves, A., Mohamed, A. R. ve Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (6645-6649).
  • Grigorescu, S., Trasnea, B., Cocias, T. ve Macesanu, G. (2020). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37(3), 362-386.
  • Gualtieri, L., Rauch, E. ve Vidoni, R. (2021). Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review. Robotics and Computer-Integrated Manufacturing, 67, 101998. Gupta, N., Fischer, A. R. ve Frewer, L. J. (2012). Socio-psychological determinants of public acceptance of technologies: A review. Public Understanding of Science, 21(7), 782-795.
  • Hamet, P. ve Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, 36-40.
  • Hartwig, B. (2021). Benefits of Artificial Intelligence. Hackr.io., https://hackr.io/blog/benefits-ofartificial-intelligence, Erişim Tarihi: 13.10.2023.
  • He, K., Zhang, X., Ren, S. ve Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.
  • Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... ve Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97.
  • Hong, J. W., Wang, Y. ve Lanz, P. (2020). Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. International Journal of Human–Computer Interaction, 36(18), 1768-1774.
  • Huang, M. H. ve Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
  • Imran, M., Castillo, C., Lucas, J., Meier, P. ve Vieweg, S. (2014, April). AIDR: Artificial intelligence for disaster response. In Proceedings of the 23rd International Conference on World Wide Web, 159-162.
  • Jakšič, M. ve Marinč, M. (2019). Relationship banking and information technology: The role of artificial intelligence and FinTech. Risk Management, 21, 1-18.
  • Jha, S. ve Topol, E. J. (2016). Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA, 316(22), 2353-2354.
  • Jiang, Y., Yin ve S., Kaynak, O. (2020). Optimized design of parity relation-based residual generator for fault detection: Data-driven approaches. IEEE Transactions on Industrial Informatics, 17(2), 1449-1458. Jones, M. L., Kaufman, E. ve Edenberg, E. (2018). AI and the ethics of automating consent. IEEE Security & Privacy, 16(3), 64-72.
  • Kaplan, A. ve Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
  • Kaur, K. ve Rampersad, G. (2018). Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 48, 87-96.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., Demir Kaya, M. (2022). The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal of Human-Computer Interaction, 1-18.
  • Khan, M. ve Waseem, H. M. (2018). A novel image encryption scheme based on quantum dynamical spinning and rotations. Plos One, 13(11).
  • Kim, Y., Kim, M., Kim, W. (2013). Effect of the Fukushima nuclear disaster on global public acceptance of nuclear energy. Energy Policy, 61, 822-828.
  • Korinek, A. ve Stiglitz, J. E. (2018). Artificial intelligence and its implications for income distribution and unemployment. In The economics of artificial intelligence: An agenda (349-390). University of Chicago Press.
  • Kortum, P. ve Oswald, F. L. (2018). The impact of personality on the subjective assessment of usability. International Journal of Human–Computer Interaction, 34(2), 177-186.
  • Kuriscak, E., Marsalek, P., Stroffek, J. ve Toth, P. G. (2015). Biological context of Hebb learning in artificial neural networks, a review. Neurocomputing, 152, 27-35.
  • Kurzweil, R. (2005). The singularity is near. In Ethics and emerging technologies. London: Palgrave Macmillan UK.
  • Lake, B. M., Ullman, T. D., Tenenbaum, J. B. ve Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40.
  • Latikka, R., Turja, T. ve Oksanen, A. (2019). Self-efficacy and acceptance of robots. Computers in Human Behavior, 93, 157-163.
  • Lewicki, R. J., McAllister, D. J. ve Bies, R. J. (1998). Trust and distrust: New relationships and realities. Academy of management Review, 23(3), 438-458.
  • Lewis, P. (2018). ‘I was shocked it was so easy’: Meet the professor who says facial recognition can tell if you’re gay. The Guardian, 7.
  • Li, X., Jiang, Y., Li, M. ve Yin, S. (2020). Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Transactions on Industrial Informatics, 17(3), 1958-1967.
  • Li, Z., Liu, J., Huang, Z., Peng, Y., Pu, H. ve Ding, L. (2017). Adaptive impedance control of human-robot cooperation using reinforcement learning. IEEE Transactions on Industrial Electronics, 64(10), 8013-8022.
  • Lichtenthaler, U. (2020). Extremes of acceptance: Employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39-45.
  • Liu, P. ve Liu, J. (2021). Selfish or utilitarian automated vehicles? Deontological evaluation and public acceptance. International Journal of Human-Computer Interaction, 37(13), 1231-1242.
  • Liu, S., Li, L., Tang, J., Wu, S. ve Gaudiot, J. L. (2018). Creating autonomous vehicle systems. San Rafael, California: Morgan & Claypool.
  • Liu, S., Li, X., Jiang, Y., Luo, H., Gao, Y. ve Yin, S. (2021). Integrated learning approach based on fused segmentation information for skeletal fluorosis diagnosis and severity grading. IEEE Transactions on Industrial Informatics, 17(11), 7554-7563.
  • Liu, X., Deng, R. H., Choo, K. K. R. ve Yang, Y. (2018). Privacy-preserving outsourced support vector machine design for secure drug discovery. IEEE Transactions on Cloud Computing, 8(2), 610-622.
  • Logg, J. M., Minson, J. A. ve Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103.
  • Longoni, C., Bonezzi, A. ve Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650.
  • Lu, R. ve Hong, S. H. (2019). Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy, 236, 937-949.
  • Ma, Y. ve Siau, K. (2018). Artificial intelligence impacts on higher education. MWAIS 2018 Proceedings. Presented at the 13th Annual Conference of the Midwest AIS, St. Louis, MO.
  • Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60.
  • Mayer, R. C., Davis, J. H. ve Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734.
  • McCarthy, J., Minsky, M. L., Rochester, N. ve Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12-12.
  • McClure, P. K. (2018). “You’re fired” says the robot: The rise of automation in the workplace, technophobes, and fears of unemployment. Social Science Computer Review, 36(2), 139-156.
  • Meidan, Y., Lerner, B., Rabinowitz, G. ve Hassoun, M. (2011). Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining. IEEE Transactions on Semiconductor Manufacturing, 24(2), 237-248.
  • Menouar, H., Guvenc, I., Akkaya, K., Uluagac, A. S., Kadri, A. ve Tuncer, A. (2017). UAV-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Communications Magazine, 55(3), 22-28.
  • Misra, S. K., Das, S., Gupta, S. ve Sharma, S. K. (2020). Public policy and regulatory challenges of artificial intelligence (AI). In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18-19, 2020, Proceedings, Part I (100-111). Springer International Publishing.
  • Mun, Y. Y. ve Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449.
  • Munir, S., Stankovic, J. A., Liang, C. J. M. ve Lin, S. (2013). Reducing energy waste for computers by human-in-the-loop control. IEEE Transactions on Emerging Topics in Computing, 2(4), 448-460.
  • Nair, A. M., Fanta, A., Haugen, F. A. ve Ratnaweera, H. (2019). Implementing an Extended Kalman Filter for estimating nutrient composition in a sequential batch MBBR pilot plant. Water Science And Technology, 80(2), 317-328.
  • Neudert, L. M., Knuutila, A. ve Howard, P. N. (2020). Global attitudes towards AI, machine learning & automated decision making. Google Scholar Google Scholar Reference.
  • Nilsson, N. J. (2010). The quest for artificial intelligence. Cambridge University Press.
  • Ning, H. ve Liu, H. (2015). Cyber-physical-social-thinking space based science and technology framework for the Internet of Things. Sci. China Inf. Sci., 58(3), 1-19.
  • OECD, (2019). Recommendation of the Council on Artificial Intelligence. https://www.oecd.org/digital/artificial-intelligence/, Erişim Tarihi: 08.11.2023.
  • OECD. 2019. Artificial Intelligence in Society. Organisation for Economic Co-Operation and Development Publishing. Available online: https://www.oecd-ilibrary.org/sites/eedfee77-en/index.html?itemId=/content/publication/eedfee77-en, Erişim Tarihi: 08.11.2023.
  • Olhede, S. C. ve Wolfe, P. J. (2018). The growing ubiquity of algorithms in society: Implications, impacts and innovations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170364.
  • Oxford Insights, (2020). IDRC, Government AI Readiness Index 2020. https://ec.europa.eu/newsroom/rtd/items/700847#: ~: text=According%20to%20Government%20AI%20Readiness,%2C%20Finland%2C%20Germany%20and%20Sweden.&text=On%20September%202020%2C%20Oxford%20Insights,the%20Government%20AI%20Readiness%20Index, Erişim Tarihi: 11.10.2023.
  • Palagi, S. ve Fischer, P. (2018). Bioinspired microrobots. Nature Reviews Materials, 3(6), 113-124.
  • Park, Jonghyuk ve Sang Eun Woo. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. The Journal of Psychology 156, 68-94.
  • Randhawa, G. K. ve Jackson, M. (2020). The role of artificial intelligence in learning and professional development for healthcare professionals. In Healthcare management forum. Sage CA: Los Angeles, CA: SAGE Publications.Vol. 33, No. 1, 19-24.
  • Raza, M. Q. ve Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352-1372.
  • Rheu, M., Shin, J. Y., Peng, W. ve Huh-Yoo, J. (2021). Systematic review: Trust-building factors and implications for conversational agent design. International Journal of Human–Computer Interaction, 37(1), 81-96.
  • Royal Society Working Group. (2017). Machine learning: The power and promise of computers that learn by example (Technical report). https://royalsociety.org/topics-policy/projects/machine-learning/, Erişim Tarihi: 11.10.2023.
  • Russell, S. J. ve Norvig, P. (2010). Artificial intelligence a modern approach. London.
  • Sartoretti, G., Kerr, J., Shi, Y., Wagner, G., Kumar, T. S., Koenig, S. veChoset, H. (2019). Primal: Pathfinding via reinforcement and imitation multi-agent learning. IEEE Robotics and Automation Letters, 4(3), 2378-2385.
  • Savela, N., Turja, T. ve Oksanen, A. (2018). Social acceptance of robots in different occupational fields: a systematic literature review. International Journal of Social Robotics, 10(4), 493-502.
  • Schepman, A. ve Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust and general trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741.
  • Scheufele, D. A. ve Lewenstein, B. V. (2005). The public and nanotechnology: How citizens make sense of emerging technologies. Journal of Nanoparticle Research, 7, 659-667.
  • Schmidt, P., Biessmann, F. ve Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260-278.
  • Sharma, S. K., Dwivedi, Y. K., Metri, B., ve Rana, N. P. (Eds.). (2020). Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6. International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18–19, 2020, Proceedings, Part II (618). Springer Nature.
  • Shropshire, J., Warkentin, M. ve Sharma, S. (2015). Personality, attitudes, and intentions: Predicting initial adoption of information security behavior. Computers & Security, 49, 177-191.
  • Siau, K. L. ve Yang, Y. (2017). Impact of artificial intelligence, robotics, and machine learning on sales and marketing, https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1047&context=mwais2017, Erişim Tarihi: 11.10.2023.
  • Siau, K. ve Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47-53.
  • Statista, (2020). Artificial Intelligence Dossier. https://www.statista.com/study/38609/artificial-intelligence-ai-statista-dossier/, Erişim Tarihi: 11.10.2023.
  • Stephanidis, C., Salvendy, G., Antona, M., Chen, J. Y., Dong, J., Duffy, V. G., ... ve Zhou, J. (2019). Seven HCI grand challenges. International Journal of Human–Computer Interaction, 35(14), 1229-1269.
  • Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., ... ve Teller, A. (2022). Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence. arXiv preprint arXiv: 2211.06318. Strong, A. I. (2016). Applications of artificial intelligence & associated technologies. Science [ETEBMS-2016], 5(6), 64-67.
  • Su, G. (2018). Unemployment in the AI Age. AI Matters, 3(4), 35-43.
  • Sutskever, I., Vinyals, O. ve Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural İnformation Processing Systems, 27, 1-9.
  • Svendsen, G. B., Johnsen, J. A. K., Almås-Sørensen, L. ve Vittersø, J. (2013). Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour & Information Technology, 32(4), 323-334.
  • TBD. (2020). Türkiye’de Yapay Zekânın Gelişimi İçin Görüş ve Öneriler, Kavramsal Rapor 2020. https://www.tbd.org.tr/pdf/yapay-zeka-raporu.pdf, Erişim Tarihi: 11.10.2023.
  • Thatte, N., Duan, H. ve Geyer, H. (2017). A sample-efficient black-box optimizer to train policies for human-in-the-loop systems with user preferences. IEEE Robotics and Automation Letters, 2(2), 993-1000.
  • Tortoise Media, The Global AI Index, 2019, https://www.tortoisemedia.com/intelligence/global-ai/, Erişim Tarihi: 11.10.2023.
  • Triberti, S., Durosini, I., Lin, J., La Torre, D. ve Ruiz Galán, M. (2021). On the “human” in human-artificial intelligence interaction. Frontiers in Psychology, 12, 808995.
  • Turja, T. ve Oksanen, A. (2019). Robot acceptance at work: a multilevel analysis based on 27 EU countries. International Journal of Social Robotics, 11(4), 679-689.
  • Ulusal Yapay Zekâ Stratejisi 2021-2025, 2021, https://cbddo.gov.tr/SharedFolderServer/Genel/File/TR-UlusalYZStratejisi2021-2025.pdf, Erişim Tarihi: 11.10.2023.
  • Vasquez, Z. (2018). The truth about killer robots: The year’s most terrifying documentary. The Guardian.
  • Venkatesh, V. ve Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
  • Vesnic-Alujevic, L., Nascimento, S. ve Polvora, A. (2020). Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks. Telecommunications Policy, 44(6), 47-61.
  • WEF, (2018), A Framework for Developing a National Artificial Intelligence Strategy, https://www.weforum.org/publications/a-framework-for-developing-a-national-artificial-intelligence-strategy/, Erişim Tarihi: 11.10.2023.
  • WEF, (2020). Data Free Flow with Trust (DFFT): Paths towards Free and Trusted Data Flows, https://www.weforum.org/publications/data-free-flow-with-trust-dfft-paths-towards-free-and-trusted-data-flows/, Erişim Tarihi: 11.10.2023.
  • Wenger, E. (2014). Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann.
  • Xiang, W. ve Lee, H. P. (2008). Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence, 21(1), 73-85.
  • Yin, S., Rodriguez-Andina, J. J. ve Jiang, Y. (2019). Real-time monitoring and control of industrial cyberphysical systems: With integrated plant-wide monitoring and control framework. IEEE Industrial Electronics Magazine, 13(4), 38-47.
  • Yokoi, R. ve Nakayachi, K. (2021). The effect of value similarity on trust in the automation systems: A case of transportation and medical care. International Journal of Human–Computer Interaction, 37(13), 1269-1282.
  • Yokoi, R., Eguchi, Y., Fujita, T. ve Nakayachi, K. (2021). Artificial intelligence is trusted less than a doctor in medical treatment decisions: Influence of perceived care and value similarity. International Journal of Human-Computer Interaction, 37(10), 981-990.
  • Yoo, W., Yu, E. ve Jung, J. (2018). Drone delivery: Factors affecting the public’s attitude and intention to adopt. Telematics and Informatics, 35(6), 1687-1700.
  • Yu, K. H., Beam, A. L. ve Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature biomedical engineering, 2(10), 719-731.
  • Zamalloa, I., Kojcev, R., Hernández, A., Muguruza, I., Usategui, L., Bilbao, A. ve Mayoral, V. (2017). Dissecting robotics-historical overview and future perspectives. arXiv preprint arXiv: 1704.08617.
  • Zang, Y., Zhang, F., Di, C. A. ve Zhu, D. (2015). Advances of flexible pressure sensors toward artificial intelligence and health care applications. Materials Horizons, 2(2), 140-156.
  • Zhang, B. ve Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874.
  • Zhang, J., Jiang, Y., Li, X., Huo, M., Luo, H. ve Yin, S. (2022). An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty. Reliability Engineering & System Safety, 222, 108357.
  • Zhang, L. ve Zhang, B. (1999). A geometrical representation of McCulloch-Pitts neural model and its applications. IEEE Transactions on Neural Networks, 10(4), 925-929.
  • Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X. ve Lin, J. (2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care, 42(2), 194-206.

Yapay Zekânın Toplumsal Karşılığı ve Karşıtlığı Üzerine Bir Derleme

Yıl 2024, , 239 - 259, 30.10.2024
https://doi.org/10.17828/yalovasosbil.1419070

Öz

Bu makale, yapay zekâ teknolojisinin toplumsal etkilerini, toplumun bu teknolojiye nasıl tepki verdiğini, yapay zekâ kullanımının toplumsal ve ekonomik sonuçlarının neler olduğunu ve bu süreçte toplumun kabul derecesinin ne şekilde geliştiğini özetlemektedir. Yapay zekânın toplumsal karşılığı ve karşıtlığı, teknolojinin hızla evrimi ve yaygınlaşmasıyla birlikte karmaşık bir konu haline gelmiştir. Yapay zekâ, günümüzde birçok sektörde hızla gelişen ve etkili bir şekilde kullanılan bir teknolojidir. İmalat sektöründen sağlığa, eğitimden güvenliğe kadar birçok alanda yaygın olarak kullanılmaktadır. Ancak, bu teknolojinin hızlı yükselişi beraberinde bazı endişeleri ve eleştirileri de getirmiştir. Özellikle işgücü piyasası, özel yaşamın gizliliği, etik sorunlar ve insan zekâsının yerini alması gibi konularda toplumsal endişeler bulunmaktadır. Yapay zekâ, iş süreçlerini otomatize ederek bazı sektörlerde işgücü ihtiyacını azaltabilir, bu da işsizlik sorunlarına sebebiyet verebilir. Ayrıca, bireylerin özel yaşamlarının yapay zekâ tarafından izlenmesi endişe yaratırken, etik konular da tartışmalara neden olmaktadır. Buna rağmen, yapay zekâ aynı zamanda büyük veri analitiği, derin öğrenme ve doğal dil işleme gibi alanlarda önemli başarılar elde etmiştir. Toplum, yapay zekânın olumlu etkilerinden yararlanırken, eleştiriler ve endişeler de dikkate alınarak bu teknolojinin geliştirilmesi ve kullanılması konusunda dengeli bir yaklaşım benimsenebilir. Bu bağlamda çalışmada yapay zekânın mahiyeti, gelişimi ve toplumsal karşılığı yanında toplumsal karşıtlığı ile yapay zekâ tasarım ve geliştirme konusunda öneriler getirilmiştir.

Etik Beyan

Yapay Zekanın Toplumsal Karşılığı-Karşıtlığı başlığı altında yazdığım makalemin başka bir yerde yayımlanmak üzere verilmediğini ve Yalova Üniversitesi Sosyal Bilimler Dergisinin tüm yükümlülüklerini kabul ettiğimi beyan ederim.

Proje Numarası

2146-1406

Teşekkür

Yayınlanma sürecinde desteklerinizden dolayı teşekkür ederim.

Kaynakça

  • Acemoglu, D. ve Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244.
  • Alam, F., Ofli, F. ve Imran, M. (2020). Descriptive and visual summaries of disaster events using artificial intelligence techniques: Case studies of Hurricanes Harvey, Irma, and Maria. Behaviour & Information Technology, 39(3), 288-318.
  • Araujo, T., Helberger, N., Kruikemeier, S. ve De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35, 611-623.
  • Ardebili, A. T. ve Rickertsen, K. (2020). Personality traits, knowledge, and consumer acceptance of genetically modified plant and animal products. Food Quality and Preference, 80, 103825.
  • Asan, O., Bayrak, A. E. ve Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of Medical Internet Research, 22(6), e15154.
  • Asimakopoulos, S., Asimakopoulos, G. ve Spillers, F. (2017). Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables. In Informatics, 4(1), 2-16.
  • Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30.
  • Baldauf, M., Fröehlich, P. ve Endl, R. (2020). Trust me, I’m a doctor–user perceptions of AI-driven apps for mobile health diagnosis. In Proceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia, 167-178.
  • Barnett, T., Pearson, A. W., Pearson, R. ve Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24, 374-390.
  • Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F. ve Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 1-16.
  • Bossmann, J. (2016). Top 9 ethical issues in artificial intelligence. In World Economic Forum.
  • Brooks, A. (2019). The benefits of AI: 6 societal advantages of automation. Rasmussen University. Available online: https://www.rasmussen.edu/degrees/technology/blog/benefits-of-ai/, Erişim Tarihi: 11.08.2024.
  • Brownsword, R. ve Harel, A. (2019). Law, liberty and technology: Criminal justice in the context of smart machines. International Journal of Law in Context, 15(2), 107–125.
  • Caporale, G. ve Monteleone, E. (2004). Influence of information about manufacturing process on beer acceptability. Food Quality and Preference, 15(3), 271-278.
  • Cappelen, H. ve Dever, J. (2021). Making AI intelligible: Philosophical foundations. Oxford University Press.
  • Carrasco, M., Mills, S., Whybrew, A. ve Jura, A. (2019). The citizen’s perspective on the use of AI in government. BCG Digital Government Benchmarking.
  • Cartwright, H. (2008). Using artificial intelligence in chemistry and biology: A practical guide. CRC Press.
  • Cave, S., Coughlan, K. ve Dihal, K. (2019). "Scary Robots" Examining Public Responses to AI. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 331-337.
  • Charness, N., Yoon, J. S., Souders, D., Stothart, C. ve Yehnert, C. (2018). Predictors of attitudes toward autonomous vehicles: The roles of age, gender, prior knowledge, and personality. Frontiers in Psychology, 9, 2589.
  • Chau, K. W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine pollution bulletin, 52(7), 726-733.
  • Chen, Y. N. K. ve Wen C. H. R. (2021). Impacts of attitudes toward government and corporations on public trust in artificial intelligence. Communication Studies, 72(1), 115-131.
  • Cherkasov, A., Hilpert, K., Jenssen, H., Fjell, C. D., Waldbrook, M., Mullaly, S. C. ve Hancock, R. E. (2009). Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS chemical biology, 4(1), 65-74.
  • Chiang, H. H., Wu, S. J., Perng, J. W., Wu, B. F. ve Lee, T. T. (2010). The human-in-the-loop design approach to the longitudinal automation system for an intelligent vehicle. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(4), 708-720.
  • Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (1251-1258).
  • Chuan, C. H., Tsai, W. H. S. ve Cho, S. Y. (2019). Framing artificial intelligence in American newspapers. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (339-344).
  • Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P. ve Malhotra, S. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute, 2.
  • Chuquicusma, M. J., Hussein, S., Burt, J. ve Bagci, U. (2018). How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (240-244). IEEE.
  • Circiumaru, A. (2022). Futureproofing EU Law the Case of Algorithmic Discrimination. Oxford: Oxford University.
  • Darko, A., Chan, A. P., Adabre, M. A., Edwards, D. J., Hosseini, M. R. ve Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, 103081.
  • Deloitte, (2019). Automotive Consumer Study, Advanced vehicle Technologies, https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/consumer-business/Deloitte-Automotive-Consumer-Study-2019.pdf, Erişim Tarihi: 11.10.2023.
  • Devaraj, S., Easley, R. F. ve Crant, J. M. (2008). Research note -how does personality matter? Relating the five-factor model to technology acceptance and use. Information systems research, 19(1), 93-105.
  • Devlin, J., Chang, M. W., Lee, K., Toutanova ve K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Doward, J. (2018). Britain funds research into drones that decide who they kill, says report. Sat, 10, 13-59. Elbadawi, M., McCoubrey, L. E., Gavins, F. K., Ong, J. J., Goyanes, A., Gaisford, S. ve Basit, A. W. (2021). Harnessing artificial intelligence for the next generation of 3D printed medicines. Advanced Drug Delivery Reviews, 175, 113805.
  • Elliott, L. (2015). Robots threaten 15 m UK jobs, says Bank of England’s chief economist. The Guardian, 12.
  • Eurobarometer. (2017). Attitudes towards the Impact of Digitisation and Automation on Daily Life. Available online: https://europa.eu/eurobarometer/surveys/detail/2160, Erişim Tarihi: 11.10.2023.
  • Eurobarometer. (2017). Attitudes towards the Impact of Digitisation and Automation on Daily Life, https://europa.eu/eurobarometer/surveys/detail/2160, Erişim Tarihi: 25.10.2023.
  • Fast, E. ve Horvitz, E. (2017). Long-term trends in the public perception of artificial intelligence. In Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 963-969.
  • Fatehi, A. ve Huang, B. (2017). Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay. Journal of Process Control, 53, 15-25.
  • Frey, C. B. ve Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
  • Fry, H. (2018). How do we stop cutting-edge technology falling into the wrong hands? The Guardian.
  • Gansser, O. A. ve Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535.
  • Garimella, K. (2018). Job loss from AI? There’s more to fear. Forbes. https://www.forbes.com/sites/cognitiveworld/2018/08/07/job-loss-from-ai-theres-moreto-fear, Erişim Tarihi: 05.11.2023.
  • Gaskell, G., Allum, N., Wagner, W., Kronberger, N., Torgersen, H., Hampel, J. ve Bardes, J. (2004). GM foods and the misperception of risk perception. Risk Analysis: An International Journal, 24(1), 185-194.
  • Gasparetto, A. ve Scalera, L. (2019a). From the unimate to the delta robot: the early decades of industrial robotics. In Explorations in the History and Heritage of Machines and Mechanisms: Proceedings of the 2018 HMM IFToMM Symposium on History of Machines and Mechanisms, Springer International Publishing, 284-295. Gasparetto, A. ve Scalera, L. (2019b). A brief history of industrial robotics in the 20th century. Advances in Historical Studies, 8, 24-35.
  • Gerlich, M. (2023). The Power of Virtual Influencers: Impact on Consumer Behaviour and Attitudes in the Age of AI. Administrative Sciences, 13(8), 178.
  • Gillespie, N., Lockey, S. ve Curtis, C. (2021). Trust in artificial intelligence: A five country study.
  • Gillham, J., Rimmington, L., Dance, H. Verweij, G., Rao, A., Roberts, B. K. ve Paich, M. (2018). The macroeconomic impact of artificial intelligence. Retrieved from Price waterhouse Coopers, https://www.pwc.co.uk/economic-services/assets/macroeconomic-impact-of-aitechnical-report-feb-18.pdf, Erişim Tarihi: 23.09.2023.
  • Graves, A., Mohamed, A. R. ve Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (6645-6649).
  • Grigorescu, S., Trasnea, B., Cocias, T. ve Macesanu, G. (2020). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37(3), 362-386.
  • Gualtieri, L., Rauch, E. ve Vidoni, R. (2021). Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review. Robotics and Computer-Integrated Manufacturing, 67, 101998. Gupta, N., Fischer, A. R. ve Frewer, L. J. (2012). Socio-psychological determinants of public acceptance of technologies: A review. Public Understanding of Science, 21(7), 782-795.
  • Hamet, P. ve Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, 36-40.
  • Hartwig, B. (2021). Benefits of Artificial Intelligence. Hackr.io., https://hackr.io/blog/benefits-ofartificial-intelligence, Erişim Tarihi: 13.10.2023.
  • He, K., Zhang, X., Ren, S. ve Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.
  • Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... ve Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97.
  • Hong, J. W., Wang, Y. ve Lanz, P. (2020). Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. International Journal of Human–Computer Interaction, 36(18), 1768-1774.
  • Huang, M. H. ve Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
  • Imran, M., Castillo, C., Lucas, J., Meier, P. ve Vieweg, S. (2014, April). AIDR: Artificial intelligence for disaster response. In Proceedings of the 23rd International Conference on World Wide Web, 159-162.
  • Jakšič, M. ve Marinč, M. (2019). Relationship banking and information technology: The role of artificial intelligence and FinTech. Risk Management, 21, 1-18.
  • Jha, S. ve Topol, E. J. (2016). Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA, 316(22), 2353-2354.
  • Jiang, Y., Yin ve S., Kaynak, O. (2020). Optimized design of parity relation-based residual generator for fault detection: Data-driven approaches. IEEE Transactions on Industrial Informatics, 17(2), 1449-1458. Jones, M. L., Kaufman, E. ve Edenberg, E. (2018). AI and the ethics of automating consent. IEEE Security & Privacy, 16(3), 64-72.
  • Kaplan, A. ve Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
  • Kaur, K. ve Rampersad, G. (2018). Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 48, 87-96.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., Demir Kaya, M. (2022). The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal of Human-Computer Interaction, 1-18.
  • Khan, M. ve Waseem, H. M. (2018). A novel image encryption scheme based on quantum dynamical spinning and rotations. Plos One, 13(11).
  • Kim, Y., Kim, M., Kim, W. (2013). Effect of the Fukushima nuclear disaster on global public acceptance of nuclear energy. Energy Policy, 61, 822-828.
  • Korinek, A. ve Stiglitz, J. E. (2018). Artificial intelligence and its implications for income distribution and unemployment. In The economics of artificial intelligence: An agenda (349-390). University of Chicago Press.
  • Kortum, P. ve Oswald, F. L. (2018). The impact of personality on the subjective assessment of usability. International Journal of Human–Computer Interaction, 34(2), 177-186.
  • Kuriscak, E., Marsalek, P., Stroffek, J. ve Toth, P. G. (2015). Biological context of Hebb learning in artificial neural networks, a review. Neurocomputing, 152, 27-35.
  • Kurzweil, R. (2005). The singularity is near. In Ethics and emerging technologies. London: Palgrave Macmillan UK.
  • Lake, B. M., Ullman, T. D., Tenenbaum, J. B. ve Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40.
  • Latikka, R., Turja, T. ve Oksanen, A. (2019). Self-efficacy and acceptance of robots. Computers in Human Behavior, 93, 157-163.
  • Lewicki, R. J., McAllister, D. J. ve Bies, R. J. (1998). Trust and distrust: New relationships and realities. Academy of management Review, 23(3), 438-458.
  • Lewis, P. (2018). ‘I was shocked it was so easy’: Meet the professor who says facial recognition can tell if you’re gay. The Guardian, 7.
  • Li, X., Jiang, Y., Li, M. ve Yin, S. (2020). Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Transactions on Industrial Informatics, 17(3), 1958-1967.
  • Li, Z., Liu, J., Huang, Z., Peng, Y., Pu, H. ve Ding, L. (2017). Adaptive impedance control of human-robot cooperation using reinforcement learning. IEEE Transactions on Industrial Electronics, 64(10), 8013-8022.
  • Lichtenthaler, U. (2020). Extremes of acceptance: Employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39-45.
  • Liu, P. ve Liu, J. (2021). Selfish or utilitarian automated vehicles? Deontological evaluation and public acceptance. International Journal of Human-Computer Interaction, 37(13), 1231-1242.
  • Liu, S., Li, L., Tang, J., Wu, S. ve Gaudiot, J. L. (2018). Creating autonomous vehicle systems. San Rafael, California: Morgan & Claypool.
  • Liu, S., Li, X., Jiang, Y., Luo, H., Gao, Y. ve Yin, S. (2021). Integrated learning approach based on fused segmentation information for skeletal fluorosis diagnosis and severity grading. IEEE Transactions on Industrial Informatics, 17(11), 7554-7563.
  • Liu, X., Deng, R. H., Choo, K. K. R. ve Yang, Y. (2018). Privacy-preserving outsourced support vector machine design for secure drug discovery. IEEE Transactions on Cloud Computing, 8(2), 610-622.
  • Logg, J. M., Minson, J. A. ve Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103.
  • Longoni, C., Bonezzi, A. ve Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650.
  • Lu, R. ve Hong, S. H. (2019). Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy, 236, 937-949.
  • Ma, Y. ve Siau, K. (2018). Artificial intelligence impacts on higher education. MWAIS 2018 Proceedings. Presented at the 13th Annual Conference of the Midwest AIS, St. Louis, MO.
  • Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60.
  • Mayer, R. C., Davis, J. H. ve Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734.
  • McCarthy, J., Minsky, M. L., Rochester, N. ve Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12-12.
  • McClure, P. K. (2018). “You’re fired” says the robot: The rise of automation in the workplace, technophobes, and fears of unemployment. Social Science Computer Review, 36(2), 139-156.
  • Meidan, Y., Lerner, B., Rabinowitz, G. ve Hassoun, M. (2011). Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining. IEEE Transactions on Semiconductor Manufacturing, 24(2), 237-248.
  • Menouar, H., Guvenc, I., Akkaya, K., Uluagac, A. S., Kadri, A. ve Tuncer, A. (2017). UAV-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Communications Magazine, 55(3), 22-28.
  • Misra, S. K., Das, S., Gupta, S. ve Sharma, S. K. (2020). Public policy and regulatory challenges of artificial intelligence (AI). In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18-19, 2020, Proceedings, Part I (100-111). Springer International Publishing.
  • Mun, Y. Y. ve Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449.
  • Munir, S., Stankovic, J. A., Liang, C. J. M. ve Lin, S. (2013). Reducing energy waste for computers by human-in-the-loop control. IEEE Transactions on Emerging Topics in Computing, 2(4), 448-460.
  • Nair, A. M., Fanta, A., Haugen, F. A. ve Ratnaweera, H. (2019). Implementing an Extended Kalman Filter for estimating nutrient composition in a sequential batch MBBR pilot plant. Water Science And Technology, 80(2), 317-328.
  • Neudert, L. M., Knuutila, A. ve Howard, P. N. (2020). Global attitudes towards AI, machine learning & automated decision making. Google Scholar Google Scholar Reference.
  • Nilsson, N. J. (2010). The quest for artificial intelligence. Cambridge University Press.
  • Ning, H. ve Liu, H. (2015). Cyber-physical-social-thinking space based science and technology framework for the Internet of Things. Sci. China Inf. Sci., 58(3), 1-19.
  • OECD, (2019). Recommendation of the Council on Artificial Intelligence. https://www.oecd.org/digital/artificial-intelligence/, Erişim Tarihi: 08.11.2023.
  • OECD. 2019. Artificial Intelligence in Society. Organisation for Economic Co-Operation and Development Publishing. Available online: https://www.oecd-ilibrary.org/sites/eedfee77-en/index.html?itemId=/content/publication/eedfee77-en, Erişim Tarihi: 08.11.2023.
  • Olhede, S. C. ve Wolfe, P. J. (2018). The growing ubiquity of algorithms in society: Implications, impacts and innovations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170364.
  • Oxford Insights, (2020). IDRC, Government AI Readiness Index 2020. https://ec.europa.eu/newsroom/rtd/items/700847#: ~: text=According%20to%20Government%20AI%20Readiness,%2C%20Finland%2C%20Germany%20and%20Sweden.&text=On%20September%202020%2C%20Oxford%20Insights,the%20Government%20AI%20Readiness%20Index, Erişim Tarihi: 11.10.2023.
  • Palagi, S. ve Fischer, P. (2018). Bioinspired microrobots. Nature Reviews Materials, 3(6), 113-124.
  • Park, Jonghyuk ve Sang Eun Woo. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. The Journal of Psychology 156, 68-94.
  • Randhawa, G. K. ve Jackson, M. (2020). The role of artificial intelligence in learning and professional development for healthcare professionals. In Healthcare management forum. Sage CA: Los Angeles, CA: SAGE Publications.Vol. 33, No. 1, 19-24.
  • Raza, M. Q. ve Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352-1372.
  • Rheu, M., Shin, J. Y., Peng, W. ve Huh-Yoo, J. (2021). Systematic review: Trust-building factors and implications for conversational agent design. International Journal of Human–Computer Interaction, 37(1), 81-96.
  • Royal Society Working Group. (2017). Machine learning: The power and promise of computers that learn by example (Technical report). https://royalsociety.org/topics-policy/projects/machine-learning/, Erişim Tarihi: 11.10.2023.
  • Russell, S. J. ve Norvig, P. (2010). Artificial intelligence a modern approach. London.
  • Sartoretti, G., Kerr, J., Shi, Y., Wagner, G., Kumar, T. S., Koenig, S. veChoset, H. (2019). Primal: Pathfinding via reinforcement and imitation multi-agent learning. IEEE Robotics and Automation Letters, 4(3), 2378-2385.
  • Savela, N., Turja, T. ve Oksanen, A. (2018). Social acceptance of robots in different occupational fields: a systematic literature review. International Journal of Social Robotics, 10(4), 493-502.
  • Schepman, A. ve Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust and general trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741.
  • Scheufele, D. A. ve Lewenstein, B. V. (2005). The public and nanotechnology: How citizens make sense of emerging technologies. Journal of Nanoparticle Research, 7, 659-667.
  • Schmidt, P., Biessmann, F. ve Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260-278.
  • Sharma, S. K., Dwivedi, Y. K., Metri, B., ve Rana, N. P. (Eds.). (2020). Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6. International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18–19, 2020, Proceedings, Part II (618). Springer Nature.
  • Shropshire, J., Warkentin, M. ve Sharma, S. (2015). Personality, attitudes, and intentions: Predicting initial adoption of information security behavior. Computers & Security, 49, 177-191.
  • Siau, K. L. ve Yang, Y. (2017). Impact of artificial intelligence, robotics, and machine learning on sales and marketing, https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1047&context=mwais2017, Erişim Tarihi: 11.10.2023.
  • Siau, K. ve Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47-53.
  • Statista, (2020). Artificial Intelligence Dossier. https://www.statista.com/study/38609/artificial-intelligence-ai-statista-dossier/, Erişim Tarihi: 11.10.2023.
  • Stephanidis, C., Salvendy, G., Antona, M., Chen, J. Y., Dong, J., Duffy, V. G., ... ve Zhou, J. (2019). Seven HCI grand challenges. International Journal of Human–Computer Interaction, 35(14), 1229-1269.
  • Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., ... ve Teller, A. (2022). Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence. arXiv preprint arXiv: 2211.06318. Strong, A. I. (2016). Applications of artificial intelligence & associated technologies. Science [ETEBMS-2016], 5(6), 64-67.
  • Su, G. (2018). Unemployment in the AI Age. AI Matters, 3(4), 35-43.
  • Sutskever, I., Vinyals, O. ve Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural İnformation Processing Systems, 27, 1-9.
  • Svendsen, G. B., Johnsen, J. A. K., Almås-Sørensen, L. ve Vittersø, J. (2013). Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour & Information Technology, 32(4), 323-334.
  • TBD. (2020). Türkiye’de Yapay Zekânın Gelişimi İçin Görüş ve Öneriler, Kavramsal Rapor 2020. https://www.tbd.org.tr/pdf/yapay-zeka-raporu.pdf, Erişim Tarihi: 11.10.2023.
  • Thatte, N., Duan, H. ve Geyer, H. (2017). A sample-efficient black-box optimizer to train policies for human-in-the-loop systems with user preferences. IEEE Robotics and Automation Letters, 2(2), 993-1000.
  • Tortoise Media, The Global AI Index, 2019, https://www.tortoisemedia.com/intelligence/global-ai/, Erişim Tarihi: 11.10.2023.
  • Triberti, S., Durosini, I., Lin, J., La Torre, D. ve Ruiz Galán, M. (2021). On the “human” in human-artificial intelligence interaction. Frontiers in Psychology, 12, 808995.
  • Turja, T. ve Oksanen, A. (2019). Robot acceptance at work: a multilevel analysis based on 27 EU countries. International Journal of Social Robotics, 11(4), 679-689.
  • Ulusal Yapay Zekâ Stratejisi 2021-2025, 2021, https://cbddo.gov.tr/SharedFolderServer/Genel/File/TR-UlusalYZStratejisi2021-2025.pdf, Erişim Tarihi: 11.10.2023.
  • Vasquez, Z. (2018). The truth about killer robots: The year’s most terrifying documentary. The Guardian.
  • Venkatesh, V. ve Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
  • Vesnic-Alujevic, L., Nascimento, S. ve Polvora, A. (2020). Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks. Telecommunications Policy, 44(6), 47-61.
  • WEF, (2018), A Framework for Developing a National Artificial Intelligence Strategy, https://www.weforum.org/publications/a-framework-for-developing-a-national-artificial-intelligence-strategy/, Erişim Tarihi: 11.10.2023.
  • WEF, (2020). Data Free Flow with Trust (DFFT): Paths towards Free and Trusted Data Flows, https://www.weforum.org/publications/data-free-flow-with-trust-dfft-paths-towards-free-and-trusted-data-flows/, Erişim Tarihi: 11.10.2023.
  • Wenger, E. (2014). Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann.
  • Xiang, W. ve Lee, H. P. (2008). Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence, 21(1), 73-85.
  • Yin, S., Rodriguez-Andina, J. J. ve Jiang, Y. (2019). Real-time monitoring and control of industrial cyberphysical systems: With integrated plant-wide monitoring and control framework. IEEE Industrial Electronics Magazine, 13(4), 38-47.
  • Yokoi, R. ve Nakayachi, K. (2021). The effect of value similarity on trust in the automation systems: A case of transportation and medical care. International Journal of Human–Computer Interaction, 37(13), 1269-1282.
  • Yokoi, R., Eguchi, Y., Fujita, T. ve Nakayachi, K. (2021). Artificial intelligence is trusted less than a doctor in medical treatment decisions: Influence of perceived care and value similarity. International Journal of Human-Computer Interaction, 37(10), 981-990.
  • Yoo, W., Yu, E. ve Jung, J. (2018). Drone delivery: Factors affecting the public’s attitude and intention to adopt. Telematics and Informatics, 35(6), 1687-1700.
  • Yu, K. H., Beam, A. L. ve Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature biomedical engineering, 2(10), 719-731.
  • Zamalloa, I., Kojcev, R., Hernández, A., Muguruza, I., Usategui, L., Bilbao, A. ve Mayoral, V. (2017). Dissecting robotics-historical overview and future perspectives. arXiv preprint arXiv: 1704.08617.
  • Zang, Y., Zhang, F., Di, C. A. ve Zhu, D. (2015). Advances of flexible pressure sensors toward artificial intelligence and health care applications. Materials Horizons, 2(2), 140-156.
  • Zhang, B. ve Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874.
  • Zhang, J., Jiang, Y., Li, X., Huo, M., Luo, H. ve Yin, S. (2022). An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty. Reliability Engineering & System Safety, 222, 108357.
  • Zhang, L. ve Zhang, B. (1999). A geometrical representation of McCulloch-Pitts neural model and its applications. IEEE Transactions on Neural Networks, 10(4), 925-929.
  • Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X. ve Lin, J. (2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care, 42(2), 194-206.
Toplam 147 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Sosyal Psikoloji
Bölüm Makaleler
Yazarlar

Emin Avcı 0000-0001-8761-2285

Proje Numarası 2146-1406
Yayımlanma Tarihi 30 Ekim 2024
Gönderilme Tarihi 13 Ocak 2024
Kabul Tarihi 28 Eylül 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Avcı, E. (2024). Yapay Zekânın Toplumsal Karşılığı ve Karşıtlığı Üzerine Bir Derleme. Yalova Sosyal Bilimler Dergisi, 14(2), 239-259. https://doi.org/10.17828/yalovasosbil.1419070
AMA Avcı E. Yapay Zekânın Toplumsal Karşılığı ve Karşıtlığı Üzerine Bir Derleme. YSBD. Ekim 2024;14(2):239-259. doi:10.17828/yalovasosbil.1419070
Chicago Avcı, Emin. “Yapay Zekânın Toplumsal Karşılığı Ve Karşıtlığı Üzerine Bir Derleme”. Yalova Sosyal Bilimler Dergisi 14, sy. 2 (Ekim 2024): 239-59. https://doi.org/10.17828/yalovasosbil.1419070.
EndNote Avcı E (01 Ekim 2024) Yapay Zekânın Toplumsal Karşılığı ve Karşıtlığı Üzerine Bir Derleme. Yalova Sosyal Bilimler Dergisi 14 2 239–259.
IEEE E. Avcı, “Yapay Zekânın Toplumsal Karşılığı ve Karşıtlığı Üzerine Bir Derleme”, YSBD, c. 14, sy. 2, ss. 239–259, 2024, doi: 10.17828/yalovasosbil.1419070.
ISNAD Avcı, Emin. “Yapay Zekânın Toplumsal Karşılığı Ve Karşıtlığı Üzerine Bir Derleme”. Yalova Sosyal Bilimler Dergisi 14/2 (Ekim 2024), 239-259. https://doi.org/10.17828/yalovasosbil.1419070.
JAMA Avcı E. Yapay Zekânın Toplumsal Karşılığı ve Karşıtlığı Üzerine Bir Derleme. YSBD. 2024;14:239–259.
MLA Avcı, Emin. “Yapay Zekânın Toplumsal Karşılığı Ve Karşıtlığı Üzerine Bir Derleme”. Yalova Sosyal Bilimler Dergisi, c. 14, sy. 2, 2024, ss. 239-5, doi:10.17828/yalovasosbil.1419070.
Vancouver Avcı E. Yapay Zekânın Toplumsal Karşılığı ve Karşıtlığı Üzerine Bir Derleme. YSBD. 2024;14(2):239-5.

-