Operational Translucency: A Third Mode of Algorithmic Visibility in Platform Governance
Keywords:
AI Act Algorithmic accountability Algorithmic transparency Digital Services Act Platform governance Researcher accessAbstract
This paper develops the concept of operational translucency: a third mode of algorithmic visibility distinct from both transparency and opacity in platform governance. Drawing on the post-transparency literature, the opacity tradition, and emerging European regulatory primary sources, the paper argues that what platforms and regulators are now jointly building is neither full transparency nor total opacity, but a stable third regime characterised by partial, structured, asymmetric visibility in which different stakeholders see different things at different resolutions, on schedules and through interfaces controlled by the platform. Four dimensions are constitutive: resolution (granularity of disclosed information), audience (stakeholder-stratification), temporality (cadence and platform-controlled scheduling), and mediation (the interface and infrastructure of disclosure). The construct is positioned against qualified transparency, platform observability, and earlier passing uses of translucence. Implications are drawn for regulatory enforcement, audit practice, and researcher access, and a four-line research agenda is proposed. Naming the regime is the prerequisite for governing it.
Downloads
References
[1] European Commission. (2025, October 24). Commission preliminarily finds TikTok and Meta in breach of their transparency obligations under the Digital Services Act (Press release IP/25/2503). https://ec.europa.eu/commission/presscorner/detail/en/ip_25_2503
[2] Heald, D. (2006). Varieties of transparency. In C. Hood & D. Heald (Eds.), Transparency: The key to better governance? (pp. 25–43). Oxford University Press. https://doi.org/10.5871/bacad/9780197263839.003.0002
[3] Hood, C. (2006). Transparency in historical perspective. In C. Hood & D. Heald (Eds.), Transparency: The key to better governance? (pp. 3–23). Oxford University Press. https://doi.org/10.5871/bacad/9780197263839.003.0001
[4] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. https://doi.org/10.4159/harvard.9780674736061
[5] Pasquale, F. (2010). Beyond innovation and competition: The need for qualified transparency in internet intermediaries. Northwestern University Law Review, 104(1), 105–173. https://doi.org/10.2139/ssrn.1686043
[6] Hansen, H. K., & Flyverbom, M. (2015). The politics of transparency and the calibration of knowledge in the digital age. Organization, 22(6), 872–889. https://doi.org/10.1177/1350508414522315
[7] Rieder, B., & Hofmann, J. (2020). Towards platform observability. Internet Policy Review, 9(4), 1–28. https://doi.org/10.14763/2020.4.1535
[8] Birchall, C. (2011). Introduction to “Secrecy and transparency”: The politics of opacity and openness. Theory, Culture & Society, 28(7–8), 7–25. https://doi.org/10.1177/0263276411427744
[9] Hood, C., & Heald, D. (Eds.). (2006). Transparency: The key to better governance? Oxford University Press. https://doi.org/10.5871/bacad/9780197263839.001.0001
[10] Gillespie, T. (2018). Custodians of the internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press. https://doi.org/10.12987/9780300235029
[11] Klonick, K. (2018). The new governors: The people, rules, and processes governing online speech. Harvard Law Review, 131(6), 1598–1670. https://harvardlawreview.org/print/vol-131/the-new-governors-the-people-rules-and-processes-governing-online-speech/
[12] Suzor, N. P. (2019). Lawless: The secret rules that govern our digital lives. Cambridge University Press. https://doi.org/10.1017/9781108666428
[13] Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
[14] Ananny, M. (2016). Toward an ethics of algorithms: Convening, observation, probability, and timeliness. Science, Technology, & Human Values, 41(1), 93–117. https://doi.org/10.1177/0162243915606523
[15] Fenster, M. (2006). The opacity of transparency. Iowa Law Review, 91(3), 885–949. https://scholarship.law.ufl.edu/facultypub/46/
[16] Fenster, M. (2017). The transparency fix: Secrets, leaks, and uncontrollable government information. Stanford University Press. https://doi.org/10.1515/9781503602670
[17] Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512
[18] Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
[19] Selbst, A. D., & Barocas, S. (2018). The intuitive appeal of explainable machines. Fordham Law Review, 87(3), 1085–1139. https://doi.org/10.2139/ssrn.3126971
[20] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
[21] Edwards, L., & Veale, M. (2017). Slave to the algorithm? Why a ‘right to an explanation’ is probably not the remedy you are looking for. Duke Law & Technology Review, 16, 18–84. https://doi.org/10.31228/osf.io/97upg
[22] Xu, S., Xie, K., & Burtch, G. (2025). An empirical study of strategic opacity in crowdsourced evaluations. MIS Quarterly, 49(3), 1205–1220. https://doi.org/10.25300/MISQ/2024/17441
[23] Wayner, P. (2002). Translucent databases: Confusion, misdirection, randomness, sharing, authentication, and steganography to defend privacy. Flyzone Press.
[24] Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79(1), 119–157. https://digitalcommons.law.uw.edu/wlr/vol79/iss1/6/
[25] Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press. https://doi.org/10.1515/9780804772891
[26] Goffman, E. (1959). The presentation of self in everyday life. Anchor Books.
[27] boyd, d. (2008). Taken out of context: American teen sociality in networked publics [Doctoral dissertation, University of California, Berkeley]. http://www.danah.org/papers/TakenOutOfContext.pdf
[28] Calma, J. (2023, February 2). Twitter just closed the book on academic research. The Verge. https://www.theverge.com/2023/2/2/23582615/twitter-removing-free-api-academic-research-misinformation
[29] Meta. (2024, August 14). CrowdTangle. Meta Transparency Center. https://transparency.meta.com/researchtools/other-data-catalogue/crowdtangle/
[30] Kaushal, R., van de Kerkhof, J., Goanta, C., Spanakis, G., & Iamnitchi, A. (2024). Automated transparency: A legal and empirical analysis of the Digital Services Act transparency database. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24) (pp. 1121–1132). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658960
[31] Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014, May). Auditing algorithms: Research methods for detecting discrimination on internet platforms. Paper presented at the Data and Discrimination preconference of the 64th annual meeting of the International Communication Association, Seattle, WA, United States. https://social.cs.uiuc.edu/papers/pdfs/ICA2014-Sandvig.pdf
[32] Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT ’20)* (pp. 33–44). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873
[33] Costanza-Chock, S., Raji, I. D., & Buolamwini, J. (2022). Who audits the auditors? Recommendations from a field scan of the algorithmic auditing ecosystem. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22) (pp. 1571–1583). Association for Computing Machinery. https://doi.org/10.1145/3531146.3533213
[34] King, G., & Persily, N. (2020). A new model for industry–academic partnerships. PS: Political Science & Politics, 53(4), 703–709. https://doi.org/10.1017/S1049096519001021
[35] Santa Clara Principles. (2021). The Santa Clara Principles on transparency and accountability in content moderation (Version 2.0). https://santaclaraprinciples.org/
[36] Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500. https://doi.org/10.2307/1879431
[37] Pandey, J. K., & Kumar, R. (2025). Governing the algorithmic agent: Confronting overt and covert challenges to justice and the future of work. International Journal of Law Management & Humanities, 8(4), 1974–1984. https://doij.org/10.10000/IJLMH.1110648
[38] Lalitha Kumari, P., & Pandey, J. K., et al. (2021). Impact of artificial intelligence on employees’ performance in information technology sector. AIP Conference Proceedings, 2587(1). https://doi.org/10.1063/5.0150403
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Digital Justice and Legal Innovation
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0): anyone may copy, redistribute, remix, transform, and build upon the material in any medium or format for any purpose, including commercially, provided appropriate credit is given to the original authors and a link to the licence is provided.
