Governance Through Uncertainty

What Chinese Algorithmic Systems Reveal About the Limits of Fairness, Accountability, and Transparency

Jason C. Lau · ACM Conference on Fairness, Accountability, and Transparency (FAccT ’26), Montreal, June 2026 · pp. 4061–4077

DOI (open access at the ACM Digital Library) · PDF · The argument in public form

Abstract

Frameworks for algorithmic governance assume that opacity is a problem to be solved through transparency. This paper argues that algorithmic systems may govern not despite their opacity but through it. Drawing on ethnographic research in China (2013–2020), I examine how social credit systems, content moderation, and platform infrastructure operate through “governance through uncertainty”: a mode of control where indeterminacy about rules, boundaries, and consequences functions as the governing mechanism itself. I develop the concept of the “microworld” to theorize how platforms construct simplified, quantified environments that users must navigate according to logics they cannot see. This produces distinctive social effects: involution (intensified striving without advancement) and lying flat (withdrawal from the game) — responses to what I call compulsory striving under conditions of epistemic deprivation. Rather than treating China as an authoritarian outlier, I argue that governance through uncertainty is an emergent property of algorithmic systems — one that arises under specifiable conditions and that the Chinese case makes unusually visible. The paper concludes by examining implications for FAccT. If opacity is integral to algorithmic governance, transparency frameworks require reconsideration. But fairness frameworks need rethinking too: governance through uncertainty produces stratification not through biased outputs but through asymmetric capacity to navigate opacity. Those with resources, connections, and political standing can bear uncertainty; those without cannot. The question is not only how to make algorithms legible, but who can afford illegibility — and who pays for it.

@inproceedings{lau2026governance,
  author    = {Lau, Jason C.},
  title     = {Governance Through Uncertainty: What Chinese Algorithmic
               Systems Reveal About the Limits of Fairness, Accountability,
               and Transparency},
  booktitle = {Proceedings of the ACM Conference on Fairness,
               Accountability, and Transparency (FAccT '26)},
  year      = {2026},
  pages     = {4061--4077},
  publisher = {ACM},
  address   = {Montreal, QC, Canada},
  doi       = {10.1145/3805689.3806495}
}