Abstract
AbstractAutomated algorithms are silently making crucial decisions about our lives, but most of the time we have little understanding of how they work. To counter this hidden influence, there have been increasing calls for algorithmic transparency. Much ink has been spilled over the informational account of algorithmic transparency—about how much information should be revealed about the inner workings of an algorithm. But few studies question the power structure beneath the informational disclosure of the algorithm. As a result, the information disclosure itself can be a means of manipulation used by a group of people to advance their own interests. Instead of concentrating on information disclosure, this paper examines algorithmic transparency from the perspective of power, explaining how algorithmic transparency under a disciplinary power structure can be a technique of normalizing people’s behavior. The informational disclosure of an algorithm can not only set up some de facto norms, but also build a scientific narrative of its algorithm to justify those norms. In doing so, people would be internally motivated to follow those norms with less critical analysis. This article suggests that we should not simply open the black box of an algorithm without challenging the existing power relations.
Funder
China Scholarship Council
Publisher
Springer Science and Business Media LLC
Subject
History and Philosophy of Science,Philosophy
Reference99 articles.
1. Ahmed, S. (2018). Credit cities and the limits of the social credit system. In AI, China, Russia, and the Global Order. Wright, N.D. (editor) http://nsiteam.com/social/wp-content/uploads/2019/01/AI-China-Russia-Global-WP_FINAL_forcopying_Edited-EDITED.pdf#page=63
2. Albu, O. B., & Flyverbom, M. (2016). Organizational transparency: Conceptualizations, conditions, and consequences. Business and Society, 58(2), 268–297.
3. Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media and Society, 20(3), 973–989.
4. Baum, K., Mantel, S., Schmidt, E., et al. (2022). From responsibility to reason-giving explainable artificial intelligence. Philosophy and Technology, 35(1), 1–30. https://doi.org/10.1007/s13347-022-00510-w
5. Beer, D. (2017). The social power of algorithms. Information Communication and Society, 20(1), 1–13.
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