Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, , 221 - 238, 26.08.2024
https://doi.org/10.26650/SJ.2024.44.1.0001

Öz

Kaynakça

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  • Brauner, P., Hick, A., Philipsen, R., & Ziefle, M. (2023). What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI. Frontiers in Computer Science, 5, 1113903. google scholar
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Participatory Management Can Help AI Ethics Adhere to the Social Consensus

Yıl 2024, , 221 - 238, 26.08.2024
https://doi.org/10.26650/SJ.2024.44.1.0001

Öz

Artificial Intelligence (AI) is increasingly pervasive, significantly altering social structures, cultural dynamics, and labor markets. The rapid growth of this ecosystem has sparked worldwide debates about AI’s challenges, including its role in reinforcing biases and social inequalities, ignoring societal values, and impacting diverse sectors like genetics, drug production, defense, and democratic processes. This study examines AI ethics through the social consensus framework, proposing participatory management as a crucial approach to address these challenges. The methodology spans the entire AI lifecycle, advocating for inclusive practices from the design stage to implementation, monitoring, and control. The participatory management model is structured in three phases: Stakeholder Engagement, which involves active participation from diverse stakeholders in developing AI systems, ensuring a range of perspectives in design, modeling, and implementation; Monitoring and Alignment, which focuses on the continuous observation of AI systems’ interaction with their environments, and Macro-level Impact Analysis, which looks at the broader societal impacts of the AI ecosystem, assessing its influence on various sectors like education, culture, health, and safety. This study underscores the importance of a collaborative, inclusive approach in AI development and management, emphasizing the need to align AI advancements with ethical principles and societal well-being.

Kaynakça

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  • Aquino, Y. S. J. (2023). Making decisions: Bias in artificial intelligence and data-driven diagnostic tools. Australian Journal of General Practice, 52(7), 439-442. google scholar
  • Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis. OECD Social, Employment and Migration Working Paper 189. google scholar
  • Arseniev-Koehler, A., & Foster, J. G. (2022). Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat. Sociological Methods & Research, 51(4), 1484-1539. google scholar
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  • Barocas, S., Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104, 671-732. google scholar
  • Bartelsman, E., Haltiwanger, J., & Scarpetta, S. (2004). Microeconomic evidence of creative destruction in industrial and developing countries. The World Bank. google scholar
  • Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. google scholar
  • Berman, B. (1989).The computer metaphor: Bureaucratizing the mind. Science as Culture, 1(7), 7-42. google scholar
  • Berman, B. (1992). Artificial intelligence and the ideology of capitalist reconstruction. AI & Society, 6(2), 103-114. google scholar
  • Biggio, B. et al. (2013). Evasion attacks against machine learning at test time. In Proc Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 387-402). google scholar
  • Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed. S. (2022). Power to the people? Opportunities and challenges for participatory AI. In Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ‘22). Association for Computing Machinery, New York, NY, USA, Article 6, 1-8. google scholar
  • Bondi, E., Xu, L., Acosta-Navas, D., & Killian., J. A. (2021). Envisioning communities: A participatory approach towards AI for social good. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES ‘21). Association for Computing Machinery, New York, NY, USA, 425-436. google scholar
  • Bonnefon, J. F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293):1573-1576. google scholar
  • Bornmann, L., Haunschild, R., & Mutz, R. (2021).Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanities and Social Sciences Communications 8, 224. google scholar
  • Boutyline, A., Arseniev-Koehler, A., & Cornell, D. J. (2023). School, studying, and smarts: Gender stereotypes and education across 80 years of American print media, 1930-2009. Social Forces, 102(1), 263-286. google scholar
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679. google scholar
  • Bratteteig, T., & Verne, G. (2018). Does AI make PD obsolete? Exploring challenges from artificial ıntelligence to participatory design. In Proceedings of the 15th Participatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial, 2, 8, 1-5. google scholar
  • Brauner, P., Hick, A., Philipsen, R., & Ziefle, M. (2023). What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI. Frontiers in Computer Science, 5, 1113903. google scholar
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  • Bozkurt, V., & Gürsoy, D. (2023). The artificial intelligence paradox: Opportunity or threat for humanity?. International Journal of Human-Computer Interaction, doi: 10.1080/10447318.2023.2297114. google scholar
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  • Calacci, D (2023). Building dreams beyond labor: Worker autonomy in the age of AI. Intereactions Mag, 48-51. google scholar
  • Capraro, V., Lentsch, A., Acemoğlu, D., et al. (2023). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. arXiv preprint. arXiv:2401.05377. google scholar
  • Citron, D. K., Pasquale, F. A. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89. google scholar
  • Conitzer, V., Brill, M., & Freeman, R. (2015). Crowdsourcing societal tradeoffs. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, (pp. 12131217). International Foundation for Autonomous Agents and Multiagent Systems. google scholar
  • Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538, 311-313. google scholar
  • de Laat, P. B. (2018). Algorithmic decision-making based on machine learning from big data: can transparency restore accountability? Philos Technol, 31, 525-541. google scholar
  • da Silva, J. A. T. (2021). The Matthew effect impacts science and academic publishing by preferentially amplifying citations, metrics and status. Scientometrics, 126, 5373-5377. google scholar
  • Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age ofAI. Harvard Business Review Press. google scholar
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Toplam 111 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyoloji (Diğer)
Bölüm ARAŞTIRMA MAKALELERİ
Yazarlar

Mahmut Özer 0000-0001-8722-8670

Matjaz Perc 0000-0002-3087-541X

Hayri Eren Suna 0000-0002-6874-7472

Yayımlanma Tarihi 26 Ağustos 2024
Gönderilme Tarihi 5 Ocak 2024
Kabul Tarihi 25 Mart 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Özer, M., Perc, M., & Suna, H. E. (2024). Participatory Management Can Help AI Ethics Adhere to the Social Consensus. İstanbul Üniversitesi Sosyoloji Dergisi, 44(1), 221-238. https://doi.org/10.26650/SJ.2024.44.1.0001
AMA Özer M, Perc M, Suna HE. Participatory Management Can Help AI Ethics Adhere to the Social Consensus. İstanbul Üniversitesi Sosyoloji Dergisi. Ağustos 2024;44(1):221-238. doi:10.26650/SJ.2024.44.1.0001
Chicago Özer, Mahmut, Matjaz Perc, ve Hayri Eren Suna. “Participatory Management Can Help AI Ethics Adhere to the Social Consensus”. İstanbul Üniversitesi Sosyoloji Dergisi 44, sy. 1 (Ağustos 2024): 221-38. https://doi.org/10.26650/SJ.2024.44.1.0001.
EndNote Özer M, Perc M, Suna HE (01 Ağustos 2024) Participatory Management Can Help AI Ethics Adhere to the Social Consensus. İstanbul Üniversitesi Sosyoloji Dergisi 44 1 221–238.
IEEE M. Özer, M. Perc, ve H. E. Suna, “Participatory Management Can Help AI Ethics Adhere to the Social Consensus”, İstanbul Üniversitesi Sosyoloji Dergisi, c. 44, sy. 1, ss. 221–238, 2024, doi: 10.26650/SJ.2024.44.1.0001.
ISNAD Özer, Mahmut vd. “Participatory Management Can Help AI Ethics Adhere to the Social Consensus”. İstanbul Üniversitesi Sosyoloji Dergisi 44/1 (Ağustos 2024), 221-238. https://doi.org/10.26650/SJ.2024.44.1.0001.
JAMA Özer M, Perc M, Suna HE. Participatory Management Can Help AI Ethics Adhere to the Social Consensus. İstanbul Üniversitesi Sosyoloji Dergisi. 2024;44:221–238.
MLA Özer, Mahmut vd. “Participatory Management Can Help AI Ethics Adhere to the Social Consensus”. İstanbul Üniversitesi Sosyoloji Dergisi, c. 44, sy. 1, 2024, ss. 221-38, doi:10.26650/SJ.2024.44.1.0001.
Vancouver Özer M, Perc M, Suna HE. Participatory Management Can Help AI Ethics Adhere to the Social Consensus. İstanbul Üniversitesi Sosyoloji Dergisi. 2024;44(1):221-38.