TY  - JOUR
T1  - Participatory Management Can Help AI Ethics Adhere to the Social Consensus
AU  - Suna, Hayri Eren
AU  - Özer, Mahmut
AU  - Perc, Matjaz
PY  - 2024
DA  - August
Y2  - 2024
DO  - 10.26650/SJ.2024.44.1.0001
JF  - İstanbul University Journal of Sociology
PB  - Istanbul University
WT  - DergiPark
SN  - 2667-6931
SP  - 221
EP  - 238
VL  - 44
IS  - 1
LA  - en
AB  - 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.
KW  - Artificial intelligence
KW  - ethics
KW  - bias
KW  - fairness
KW  - participatory algorithmic management
KW  - algorithmic accountability
CR  - Acemoğlu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work. NBER Working Paper 24196. National Bureau of Economic Research. google scholar
CR  - Acemoğlu, D., Autor, D., & Johnson, S. (2023). Can we have pro-worker- AI? Choosing a path of machines in service of minds. CEPR Policy Insight, No.123, 1-12. google scholar
CR  - Aghion, P., & Howitt, P. (1990). A model of growth through creative destruction. NBER Working Paper 3223 National Bureau of Economic Research. google scholar
CR  - Aghion, P., Howitt, P. (1994). Growth and unemployment. Rev Econ Stud, 61, 477-494. google scholar
CR  - Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., & Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, 102387. google scholar
CR  - Aldewereld, H., Dignum, V., & Hua Tan, Y. (2014). Design for values in software development. In Jeroen van den Hoven, Pieter E. Vermaas, I. v. d. P., (Eds), Handbook of ethics, values, and technological design. Springer. google scholar
CR  - Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (23 May, 2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. google scholar
CR  - 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
CR  - 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
CR  - 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
CR  - Awad, E., Dsouza, S., Bonnefon, J. F., Shariff, A., & Rahwan, I. (2020). Crowdsourcing: Moral machines. Communications of ACM, 63(3). google scholar
CR  - Azjen, I., Brown, T. C., & Carvajal, F. (2004). Explaining the discrepancy between intentions and actions: The case of hypothetical bias in contingent valuation. Personality and Social Psychology Bulletin, 30(9), 1108-1121. google scholar
CR  - Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052-1092. google scholar
CR  - Barocas, S., Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104, 671-732. google scholar
CR  - Bartelsman, E., Haltiwanger, J., & Scarpetta, S. (2004). Microeconomic evidence of creative destruction in industrial and developing countries. The World Bank. google scholar
CR  - 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
CR  - Berman, B. (1989).The computer metaphor: Bureaucratizing the mind. Science as Culture, 1(7), 7-42. google scholar
CR  - Berman, B. (1992). Artificial intelligence and the ideology of capitalist reconstruction. AI & Society, 6(2), 103-114. google scholar
CR  - 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
CR  - 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
CR  - 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
CR  - Bonnefon, J. F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293):1573-1576. google scholar
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - Brinkmann, L., Baumann, F., Bonnefon, J. F. et al. (2023). Machine culture. Nature Human Behavior, 7(11), 1855-1868. google scholar
CR  - Calacci, D (2023). Building dreams beyond labor: Worker autonomy in the age of AI. Intereactions Mag, 48-51. google scholar
CR  - 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
CR  - Citron, D. K., Pasquale, F. A. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89. google scholar
CR  - 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
CR  - Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538, 311-313. google scholar
CR  - 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
CR  - 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
CR  - Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age ofAI. Harvard Business Review Press. google scholar
CR  - Delgado, F., Yang, S., Madaio, M., & Yang, Q. (2023). The participatory turn in ai design: Theoretical foundations and the current state of practice. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ‘23). Association for Computing Machinery, New York, NY, USA, Article 37, 1-23. google scholar
CR  - Diakopoulos, N. (2015). Algorithmic accountability: Journalistic investigation of computational power structure. Digital Journalism, 3(3), 1-18. google scholar
CR  - Didier, E. (2015). Gabriel Tarde and statistical movement. In V. Vargas (Ed), The social after Gabriel Tarde (pp. 299-325). Routledge. google scholar
CR  - Erdi, P. (2020). Ranking: The unwritten rules of the social game we all play. Oxford University Press. google scholar
CR  - Etzioni, A., & Etzioni, O. (2016). AI assisted ethics. Ethics and Information Technology, 18(2), 149-156. google scholar
CR  - Frank, M. R., Autor, D., Bessen, J. E., et al. (2019). Toward understanding the impact of artificial intelligence on labor. PNAS, 116(14), 6531-6539. google scholar
CR  - Friedman, B. (1996). Value-sensitive design. Interactions, 3(6), 16-23. google scholar
CR  - Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330-347. google scholar
CR  - Fu, R., Huang, Y., & Singh, P. V. (2020). AI and algorithmic bias: Source, detection, mitigation and implications, SSRN, doi:/10.2139/ssrn.3681517 google scholar
CR  - Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: A multi-dimensional study. Social Sciences, 12(9), 502. google scholar
CR  - Harari, Y. N. (2017). Reboot for the AI revolution. Nature, 550(19), 324-327. google scholar
CR  - Hernandez-Ramirez, R. ().Technology and self-modification: Understanding technologies of the self after Foucault. Journal of Science and Technology of the Arts, 9(3), 45-57. google scholar
CR  - Hossain, S. Q., & Ahmed, S. I. (2021). Towards a new participatory approach for designing artificial intelligence and data-driven technologies. ACM, New York, NY, USA, google scholar
CR  - Hussain, S., Sanders, E. B. N., & Steinert. M. (2012). Participatory design with marginalized people in developing countries: Challenges and opportunities experienced in a field study in Cambodia. International Journal of 'Design 6, 2, 91-109. google scholar
CR  - Joyce, K., Smith-Doerr, L., Alegria, S., Bell, S., Cruz, T., Hoffman, S. G., Noble, S. U., & Shestakofsky, B. (2021). Toward a sociology of artificial intelligence: A call for research on inequalities and structural change. Socius, 7. google scholar
CR  - Kostick-Quenet, K. M., Gerke, S. (2022). AI in the hands of imperfect users. NPJ Digital Medicine, 5, 197. google scholar
CR  - Lee, M. K., Kusbit, D., Kahng, A. et al. (2019). WeBuildAI: Participatory framework for algorithmic governance. Proc. ACM Hum.-Comput. Interact., 3(CSCW), 181. google scholar
CR  - Lewis, K., Kaufman, J., Gonzales, M., Wimmer, A., & Christakis, N. (2008). Tastes, ties, and time: A new social network dataset using Facebook.com. Social Networks, 30, 330-342. google scholar
CR  - Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. google scholar
CR  - Liu, Z. (2020). Sociological perspectives on artificial intelligence: A typological reading. Sociology Compass, 15(3), e12851. google scholar
CR  - Lum, K., & Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14-19. google scholar
CR  - Manyika, J., & Sneader, K. (2018). AI, automation, and the future of work: Ten things to solve for. McKinsey Global Institute. google scholar
CR  - Mayer-Schönberger, V., & Cukier, K. (2014). Big data. Houghton Mifflin Harcourt. google scholar
CR  - Merton, R. K. (1968). The Matthew effect in science. Science, 159, 53-63. google scholar
CR  - Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023). Bias in AI-based models for medical applications: Challenges and mitigation strategies. NPJ Digital Medicine, 6, 113. google scholar
CR  - Montesano, A. (2011). Ricardo on machinery. What matters: Technical progress or substitution of machines for circulating capital?. History of Economic Ideas, 19(1), 103-124. google scholar
CR  - Mu, W. (2023). How artificial intelligence affects workforces: The impact of biased recruitment and job displacement risk. Highlights in Business, Economics and Management, 23, 19-25. google scholar
CR  - Muller, M. J. (2009). Participatory design: The third space in HCI. In Human-computer interaction (pp. 181-202). CRC Press. google scholar
CR  - Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C, Moukheiber, M., Khanna, A. K., et al. (2023). Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2(6):e0000278. google scholar
CR  - Ntoutsi, E., Fafalios, P., Gadiraju, U., et al. (2020). Bias in data-driven artificial intelligence systems-an introductory survey. WIREs Data Mining Knowl Discov, 10(3):e1356. google scholar
CR  - Nevmyvaka, Y., Feng, Y., & Kearns, M. (2006). Reinforcement learning for optimized trade execution. In Proc 23rd International Conference on Machine Learning (pp. 673-680). ACM. google scholar
CR  - Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366, 447-453. google scholar
CR  - O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Books. google scholar
CR  - Özer, M. (2023a). The Matthew effect in Turkish education system. Bartın University Journal of Faculty of Education, 12(4):704-712. google scholar
CR  - Özer, M. (2023b). Matta Etkisi. Uluslararası Yönetim İktisat ve İşletme Dergisi, 19(4):974-984. google scholar
CR  - Özer, M. (2024). Başarı oyununda Matta Etkisi ve ödülün asimetrik dağılımı. Reflektif Journal of Social Sciences, 5(1):187-197. google scholar
CR  - Özer, M., & Perc, M. (2020). Dream and realities of school tracking and vocational education. Palgrave Communications, 6(1), 1-7. google scholar
CR  - Özer, M., & Perc, M. (2021). Impact of social networks on the labor market inequalities and school-to-work transitions. Journal of Higher Education, 11(1):38-50. google scholar
CR  - Özer, M., Perc, M., & Suna, H. E. (in press). AI bias and the amplification of inequalities in the labor market. Journal of Economy, Culture and Society. google scholar
CR  - Özer, M. (in press). Potential benefits and risks of artificial intelligence in education. Bartın University Journal of Faculty of Education. google scholar
CR  - Pajarinen M., Rouvinen P., & Ekeland, A. (2015). Computerization threatens one-third of Finnish and Norwegian employment. ETLA Brief, 34. google scholar
CR  - Panch, T., Mattie, H., & Atun, R. (2019). Artificial intelligence and algorithmic bias: implications for health systems. Journal of Global Health, 9(2), 010318. google scholar
CR  - Parkes, D. C., & Wellman, M. P. (2015). Economic reasoning and artificial intelligence. Science, 349, 267-272. google scholar
CR  - Pasquale, F. (2011). Restoring transparency to automated authority. Journal of Telecommunications & High Technology Law, 9, 235-256. google scholar
CR  - Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. google scholar
CR  - Pelacho, M., Ruiz, G., Sanz, F., Tarancon, A., & Clemento-Gallardo, J. (2020). Analysis of the evolution and collaboration networks of citizen science scientific publications. Scientometrics, doi: 10.1007/s11192-020-03724-x. google scholar
CR  - Perc, M. (2014). The Matthew effect in empirical data. Journal of the Royal Society Interface, 11, 98. google scholar
CR  - Perc, M., Özer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5, 61. google scholar
CR  - Piano, S. L. (2020). Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanities & Social Sciences Communications, 7, 9. google scholar
CR  - Rahwan, I. (2018). Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology, 20(1), 5-14. google scholar
CR  - Rahwan, I., Cebrian, M., Obradovich, N. et al. (2019). Machine behavior. Nature, 568, 477-486. google scholar
CR  - Reinsel, D., Gantz, J., & Rydning, J. (2018). The digitization of the world: From edge to core. IDC White Paper. google scholar
CR  - Rigney, D (2010). The Matthew effect: How advantage begets further advantage. Columbia University Press. google scholar
CR  - Schwartz, R. D. (1989). Artificial intelligence as a sociological phenomenon. The Canadian Journal of Sociology, 14(2), 179-202. google scholar
CR  - Shinde, P. P., & Shah, S. (2018). A review of machine learning and deep learning applications. 2018 Fourth International Conference on Computing Communication Control and Automation, 1-6. google scholar
CR  - Sjoding, M. W., Dickson, R. P., Iwashyna, T. J., Gay, S. E., & Valley, T. S. (2020). Racial bias in pulse oximetry measurement. google scholar
CR  - Sonnenburg, S. et al. (2007). The need for open source software in machine learning. J Mach Learn Res, 8, 2443-2466. google scholar
CR  - Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54-70. google scholar
CR  - Stahl, B. C. (2023). Embedding responsibility in intelligent systems: from AI ethics to responsible AI ecosystems. Scientific Reports, 13, 7586. google scholar
CR  - Strevens, M. (2006). The role of the Matthew effect in science. Studies in History and Philosophy of Science Part A, 37(2), 159-170. google scholar
CR  - Suleyman, M. (2023). The coming wave: Technology, power, and the twenty-first century’s greatest dilemma. Crown: New York. google scholar
CR  - Thimbleby, H. (2003). Explaining code for publication. Softw: Pract Experience, 33, 975-1001. google scholar
CR  - Thompson, E. P. (1967). Time, work-discipline and industrial capitalism. Past & Present, 38, 56-97. google scholar
CR  - Tramer, F. et al. (2017). Ensemble adversarial training: attacks and defences. arxiv.org/abs/1705.07204. google scholar
CR  - Turkle, S. (1984).The second self: Computers and the human spirit. Simon and Schuste. google scholar
CR  - Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in Artificial Intelligence policy. Review of Policy Research, 40, 665-687. google scholar
CR  - Ye, F. (2017). Scientific metrics: Towards analytical and quantitative sciences. Science Press Beijing & Springer. google scholar
CR  - Wan, Y., & Chang, K. W. (2024). The male CEO and the female assistant: Probing gender biases in text-to-image models through paired stereotype test. arXiv: 2402.11089. google scholar
CR  - Wolfe, A. (1991). Mind, self, society, and computer: Artificial intelligence and the sociology of mind. American Journal of Sociology, 96(5), 1073-1096. google scholar
CR  - Woolgar, S. (1985). Why not a sociology of machines? The case of sociology and artificial intelligence. Sociology, 19(4), 557-572. google scholar
CR  - Zarsky, T. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology & Human Values, 41(1), 118-132. google scholar
CR  - Zimmer, M (2008). More on the ‘Anonymity’ of the Facebook dataset: It’s Harvard College. MichaelZimmer.org Blog. google scholar
CR  - Zuckerman, H. (1989). Accumulation of Advantage and Disadvantage: The Theory and Its Intellectual Biography. In Ed. C Mongardini and S Tabboni (Eds). Robert K. Merton and contemporary sociology (pp.153-176). New Brunswick, NJ: Transaction. google scholar
UR  - https://doi.org/10.26650/SJ.2024.44.1.0001
L1  - https://dergipark.org.tr/en/download/article-file/4392666
ER  -