Technological Transparency in the Workplace : Black Box Algorithmic Culture in the Warehousing Industry

• Autorzy: Miłosz Miszczyński , Jordan Klimek
• Języki publikacji: EN

Abstrakty

EN

Algorithms in information technology influence changes in contemporary organizations. They monitor business processes, support decision-making, and help to increase efficiency. The literature has described extensively the applications of algorithmic technology in new models of organizations but few studies have addressed the relationship between algorithms and organizational culture. This paper fills that niche by concentrating on black boxing to address the technological transparency in the algorithmic workplace. This paper uses the case study of the Amazon POZ 1 warehouse near Poznań, Poland. The findings show that algorithmic culture has a profound effect on how employees interact, how they see themselves at work, and how they perform their job responsibilities. As we show, algorithmic transparency influences not only employees' worklife but also the general positionality of the workforce in the wider political economy. We conclude by arguing in favor of greater algorithmic regulation. (original abstract)

Słowa kluczowe

EN

Labour; Working condition; Warehouse management; Algorithms

PL

Praca; Warunki pracy; Gospodarka magazynowa; Algorytmy

• Czasopismo: Polish Sociological Review
• Rocznik: 2023
• Numer: nr 4 (224)
• Strony: 483--498

Twórcy

autor

Miłosz Miszczyński

• Kozminski University

autor

Jordan Klimek

• University of Szczecin, Poland

Bibliografia

• Acciarini, C., Cappa, F., Boccardelli, P., & Oriani, R. 2023. How can organizations leverage big data to innovate their business models? A systematic literature review, Technovation 123, article 102713. DOI: 10.1016/j.technovation.2023.102713.
• Adensamer, A., Gsenger, R., & Klausner, L. D. 2021. "Computer says no": Algorithmic decision support and organisational responsibility, Journal of Responsible Technology 7-8: article 100014. DOI: 10.1016/j.jrt.2021.100014.
• Angwin, J., Larson, J., Mattu, S., & Kirchner, L. 2016. Machine bias: There's software used across the country to predict future criminals. And it's biased against Blacks, ProPublica.
• Anteby, M., & Chan, C. K. 2018. A self-fulfilling cycle of coercive surveillance: Workers' invisibility practices and managerial justification, Organization Science 29(2): 247-263.
• Bader, V., & Kaiser, S. 2019. Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence, Organization 26(5): 655-672. DOI: 10.1177/1350508419855714.
• Barocas, S., & Selbst, A. D. 2016. Big Data's disparate impact, California Law Review 104(3): 671-732.
• Baym, N. K. 2018. Playing to the Crowd: Musicians, Audiences, and the Intimate Work of Connection. New York: New York University Press.
• Beane, M. 2019. Shadow learning: Building robotic surgical skill when approved means fail, Administrative Science Quarterly 64(1): 87-123.
• Beunza, D. 2019. Taking the Floor: Models, Morals, and Management in a Wall Street Trading Room. Princeton: Princeton University Press.
• Bolin, G., & Schwarz, A. J. 2015. Heuristics of the algorithm: Big Data, user interpretation and institutional translation, Big Data & Society 2(2), DOI: 10.1177/2053951715608406.
• Brayne, S. 2017. Big data surveillance: The case of policing, American Sociological Review 82(5): 977-1008.
• Browne, S. 2015. Dark Matters: On the Surveillance of Blackness. Durham NC: Duke University.
• Bucher, T. 2017. The algorithmic imaginary: Exploring the ordinary effects of the Facebook algorithms. Information, Communication & Society 20(1): 30-44.
• Bujold, A., Parent-Rocheleau, X., & Gaudet, M-C. 2022. Opacity behind the wheel: The relationship between transparency of algorithmic management, justice perception, and intention to quit among truck drivers, Computers in Human Behavior Reports 8: article 100245. DOI: 10.1016/j.chbr.2022.100245.
• Burrell, J. 2016. How the machine 'thinks': Understanding opacity in machine learning algorithms, Big Data & Society 3(1): 1-12. DOI: 10.1177/2053951715622512.
• Christin, A. 2020. The ethnographer and the algorithm: Beyond the black box, Theory and Society 49: 897-918. DOI: 10.1007/s11186-020-09411-3.
• Clarysse, B., Fang He, V., & Tucci, C. L. 2022. How the Internet of Things reshapes the organization of innovation and entrepreneurship, Technovation 118: article 102644. DOI: 10.1016/j.technovation.2022.102644.
• Coffey, A., & Atkinson, P. 1996. Making Sense of Qualitative Data: Complementary Research Strategies. London: Sage Publications, Inc.
• Cunha, J., & Carugati, A. 2018. Transfiguration work and the system of transfiguration: How employees represent and misrepresent their work, MIS Quarterly 42(3): 873-894.
• Danaher, J. 2016. The threat of algocracy: Reality, resistance and accommodation, Philosophy & Technology 29(3); 245-268.
• Davidson, A. 2016. Planet Money, in J. Goldstein (Ed.), The Future of Work Looks like a UPS truck. Available at https://www.npr.org/sections/money/2014/05/02/308640135/episode-536-the-future-of-work-looks-like-a-ups-truck.
• Davis, G. F. 2015. What might replace the modern corporation: Uberization and the web page enterprise, Seattle University Law Review 39(2): 501-516.
• Davis, G. F. 2016. Can an economy survive without corporations? Technology and robust organizational alternatives, Academy of Management Perspectives 30(2): 129-140.
• de Assis Vilela, F., Cesário Times, V., de Campos Bernardi, A. C., de Paula Freitas, A., & Rodrigues Ciferri, R. 2023. A non-intrusive and reactive architecture to support real-time ETL processes in data warehousing environments, Heliyon 9(5): article e15728. DOI: 10.1016/j.heliyon.2023.e15728.
• Ekbia, H. R., & Nardi, B. A. 2017. Heteromation and Other Stories of Computing and Capitalism. Cambridge, Ma: MIT Press.
• Eubanks, V. 2018. Automating Inequality: How High-tech Tools Profile, Police, and Punish the Poor. New York: St. Martin's Press.
• Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. 2017. Using deep learning and Google street view to estimate the demographic makeup of neighborhoods across the United States, Proceedings of the National Academy of Sciences 114(50): 13108-13113.
• Graham, M., Hjorth, I., & Lehdonvirta, V. 2017. Digital labour and development: Impacts of global digital labour platforms and the gig economy on worker livelihoods, Transfer: European Review of Labour and Research 23(2): 135-162.
• Gray, M. L., Suri, S., Ali, S. S., & Kulkarni, D. 2016. The Crowd is a Collaborative Network. [Conference presentation]. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing.
• Griesbach, K., Reich, A., Elliott-Negri, L., & Milkman, R. 2019. Algorithmic control in platform food delivery work, Socius 5: 1-15. DOI: 10.1177/2378023119870041.
• Gupta, S., Ghardallou, W., Pandey, D. K., & Sahu, G. P. 2022. Artificial intelligence adoption in the insurance industry: Evidence using the technology-organization-environment framework, Research in International Business and Finance 63: article 101757. DOI: 10.1016/j.ribaf.2022.101757.
• Halavais, A. 2018. Search Engine Society. Cambridge: Polity.
• Hall, J. V., Horton, J. J., & Knoepfle, D. T. 2019. Pricing efficiently in designed markets: The case of ridesharing. Available at john-joseph-horton.com.
• Hatton, E. 2017. Mechanisms of invisibility: Rethinking the concept of invisible work, Work, Employment and Society 31(2): 336-351.
• Jhaver, S., Karpfen, Y., & Antin, J. 2018. Algorithmic anxiety and coping strategies of Airbnb hosts. [Conference presentation]. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.
• Langer, M., & König, C. J. 2023. Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management, Human Resource Management Review 33(1): article 100881. DOI: 10.1016/j.hrmr.2021.100881.
• Langer, M., & Landers, R. N. 2021. The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers, Computers in Human Behavior 123: article 106878. DOI: 10.1016/j.chb.2021.106878.
• Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. 2019. Doubting the Diagnosis: How Artificial Intelligence Increases Ambiguity during Professional Decision Making. New York: New York University.
• Leonardi, P., & Contractor, N. 2018. Better people analytics measure who they know, not just who they are, Harvard Business Review 96(6): 70-81.
• Levy, K. E. 2015. The contexts of control: Information, power, and truck-driving work, The Information Society 31(2); 160-174.
• Levy, K., & Barocas, S. 2018. Privacy at the margins| refractive surveillance: Monitoring customers to manage workers, International Journal of Communication 12: 1166-1188.
• Liu, M., Brynjolfsson, E., & Dowlatabadi, J. 2018. Do digital platforms reduce moral hazard? The case of Uber and taxis. National Bureau of Economic Research.
• Lix, K., Goldberg, A., Srivastava, S., & Valentine, M. 2019. Expressly different: Interpretive diversity and team performance, Working Paper. Stanford University.
• Lorente, A. 2023. Setting the goals for ethical, unbiased, and fair AI, in: F. A. Batarseh, & L. J. Freeman (Eds.), AI Assurance. Cambridge, Ma.: Academic Press, pp. 13-54. DOI: 10.1016/B978-0-32-391919-7.00014-7.
• Mahmud, H., Najmul Islam, A.K.M., Ahmed, S. I., & Smolander, K. 2022. What influences algorithmic decision-making? A systematic literature review on algorithm aversion, Technological Forecasting and Social Change 175: article 121390. DOI: 10.1016/j.techfore.2021.121390.
• Marabelli, M., Newell, S., & Page, X. 2018. Algorithmic Decision-Making in the US Healthcare Industry [Conference presentation, December 11-12]. IFIP 8.2 Working Conference, San Francisco, CA. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract id=3262379.
• McDaid, E., Andon, P., & Free, C. 2023. Algorithmic management and the politics of demand: Control and resistance at Uber, Accounting, Organizations and Society, article 101465. DOI: 10.1016/j.aos.2023.101465.
• Meisner, C., Duffy, B. E., & Ziewitz, M. 2022. The labor of search engine evaluation: Making algorithms more human or humans more algorithmic? New Media & Society. DOI: 10.1177/14614448211063860.
• Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. 2019. Model cards for model reporting [Conference presentation]. Proceedings of the Conference on Fairness, Accountability, and Transparency.
• Mols, B. 2017. In black box algorithms we trust (or do we?) [Conference presentation, March 16]. Communications of the ACM. Retrieved from: https://cacm.acm.org/news/214618-in-black-box-algorithms-wetrust-or-do-we/fulltext.
• Noble, S. U. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
• O'Neil, C. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Broadway Books.
• Polzer, J. T. 2022. The rise of people analytics and the future of organizational research, Research in Organizational Behavior 42: article 100181. DOI: 10.1016/j.riob.2023.100181.
• Puranam, P. 2018. The Microstructure of Organizations. Oxford: Oxford University Press.
• Rahman, H. 2019. From iron cages to invisible cages: Algorithmic evaluations in online labor markets. Working Paper. Stanford University.
• Rosenblat, A. 2018. Uberland: How Algorithms are Rewriting the Rules of Work. Los Angeles: University of California Press.
• Rosenblat, A., Levy, K. E., Barocas, S., & Hwang, T. 2017. Discriminating tastes: Uber's customer ratings as vehicles for workplace discrimination, Policy & Internet 9(3): 256-279.
• Rosenblat, A., & Stark, L. 2016. Algorithmic labor and information asymmetries: A case study of Uber's drivers, International Journal of Communication 10: 3758-3784.
• Sachs, S. E. 2019. The algorithm at work? Explanation and repair in the enactment of similarity in art data, Information, Communication & Society 23(11): 1-17.
• Scholz, T. 2013. Digital Labor: The Internet as Playground and Factory. London: Routledge.
• Scholz, T. 2016. Platform Cooperativism. Challenging the Corporate Sharing Economy. New York: Rosa Luxemburg Foundation.
• Schwartz, D. 2018. Embedded in the crowd: Creative freelancers, crowdsourced work, and occupational community, Work and Occupations 45(3): 247-282.
• Shestakofsky, B. 2017. Working algorithms: Software automation and the future of work, Work and Occupations 44(4): 376-423. DOI: 10.1177/0730888417726119.
• Shin, D., & Park, Y. J. 2019), Role of fairness, accountability, and transparency in algorithmic affordance, Computers in Human Behavior 98: 277-284. DOI: 10.1016/j.chb.2019.04.019.
• Teddlie, C., & Yu, F. 2007. Mixed methods sampling: A Typology with examples, Journal of Mixed Methods Research 1(1): 77-100. DOI: 10.1177/1558689806292430.
• Terranova, T. 2000. Free labor: Producing culture for the digital economy, Social Text 18(2): 33-58.
• Ticona, J., & Mateescu, A. 2018. Trusted strangers: Cultural entrepreneurship on domestic work platforms in the on-demand economy, New Media & Society 20(11): 4384-4404.
• Tseng, F.-M., Liang, C.-W., & Nguyen, N. B. 2023. Blockchain technology adoption and business performance in large enterprises: A comparison of the United States and China Technology in Society 73: article 102230. DOI: 10.1016/j.techsoc.2023.102230.
• Weld, D. S., & Bansal, G. 2018. The Challenge of Crafting Intelligible Intelligence. Available at arXiv.org/abs/1803.04263.
• Willems, T., & Hafermalz, E. 2021. Distributed seeing: Algorithms and the reconfiguration of the workplace: A case of 'automated' trading, Information and Organization 31(4): article 100376. DOI: 10.1016/j.infoandorg.2021.100376.
• Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. 2019. Good gig, bad gig: Autonomy and algorithmic control in the global gig economy, Work, Employment and Society 33(1): 56-75.
• Zanoni, P., & Miszczyński, M. 2023. Post-Diversity, precarious work for all: Unmaking borders to govern labor in the Amazon warehouse, Organization Studies. DOI: 10.1177/01708406231191336.
• Zhou, S., Valentine, M., & Bernstein, M. S. 2018. In search of the dream team: Temporally constrained multiarmed bandits for identifying effective team structures [Conference presentation]. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems: 1-13. Montreal QC, Canada: ACM.
• ---

Identyfikatory

DOI

https://doi.org/10.26412/psr224.04