Online ranking system effects on perceived fairness
Gender, income and education
Keywords:
Online Workplace Platforms, Gig Economy, Social InequalitiesAbstract
The gig economy, which is also referred to as the sharing or on-demand economy, involves the use of online platforms to offer and find short-term work, goods, and services on a flexible basis. These platforms, which allow freelancers and independent contractors to connect with clients in need of their services, have gained widespread popularity in recent years. However, the gig economy has been the subject of much controversy, particularly regarding the fairness of platform rating systems and their impact on workers' income and job security. This article presents an analysis of the distribution of fairness and perceived satisfaction with ranking systems in these work markets, and discusses the ways in which these systems may lead to unfair outcomes for workers. It also examines the effects of these systems on workers' income and job security, and investigates the potential influence of factors such as gender, age, and employment status on the fairness of these rating systems. The article suggests directions for further research on this topic and considers the implications of these findings for policymakers and practitioners.
References
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6 (1), 3–5.
Busemeyer, J. R. (1985). Decision making under uncertainty: A comparison of simple scalability, fixed-sample, and sequential-sampling models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11 (3), 538.
Chase, R. (2015). Peers inc: How people and platforms are inventing the collaborative economy and reinventing capitalism. PublicAffairs.
Codagnone, C., Abadie, F., & Biagi, F. (2016). The future of work in the ‘sharing economy’. Market efficiency and equitable opportunities or unfair precarisation.
Cooper, M. A. H. J. (2003). The sage handbook of social psychology. Sage.
Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management science, 49 (10), 1407–1424.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 214–226.
Elbassuoni, S., Amer-Yahia, S., & Ghizzawi, A. (2020a). Fairness of scoring in online job marketplaces. ACM Transactions on Data Science, 1 (4), 1–30.
Elbassuoni, S., Amer-Yahia, S., & Ghizzawi, A. (2020b). Fairness of scoring in online job marketplaces. ACM Transactions on Data Science, 1 (4), 1–30.
Ess, C., & Sudweeks, F. (2005). Culture and computer-mediated communication: Toward new understandings. Journal of computer-mediated communication, 11 (1), 9.
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of mechanical turk samples. Journal of Behavioral Decision Making, 26 (3), 213–224.
Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in neural information processing systems, 29.
Hilbert, M., & López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332 (6025), 60–65.
Hsee, C. K. (1996). The evaluability hypothesis: An explanation for preference reversals between joint and separate evaluations of alternatives. Organizational Behavior and Human Decision Processes, 67 (3), 247–257.
Kim, H. S., & Hodgins, D. C. (2017). Reliability and validity of data obtained from alcohol, cannabis, and gambling populations on amazon’s mechanical turk. Psychology of addictive behaviors, 31 (1), 85.
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2002). Inequality and instability: A study of the world income distribution. Handbook of economic growth (pp. 1563–1641). Elsevier.
Konstan, J. A., Riedl, J., & Terveen, L. (2002). Recommender systems for the internet: An introduction. Recommender systems handbook (pp. 1–20). Springer, Boston, MA.
Li, V. O., Lam, J. C., & Cui, J. (2021). Ai for social good: Ai and big data approaches for environmental decision-making.
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5 (5), 411–419.
Patro, G. K., Porcaro, L., Mitchell, L., Zhang, Q., Zehlike, M., & Garg, N. (2022). Fair ranking: A critical review, challenges, and future directions. arXiv preprint arXiv:2201.12662.
Schmitt, D. P., Allik, J., McCrae, R. R., & Benet-Martınez, V. (2007). The geographic distribution of big five personality traits: Patterns and profiles of human self-description across 56 nations. Journal of cross-cultural psychology, 38 (2), 173–212.
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