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Th.48G Attention

Information Sovereignty in the Age of AI Theorem

AI mediation poses major risks to information sovereignty.

Cycle path

Art.8→Art.1→Art.78→Art.102→Art.8

As artificial intelligence systems increasingly mediate information flows at massive scale, they pose both unprecedented opportunities and severe risks to information sovereignty — the capacity of individuals and communities to exercise meaningful control over their information environments.

AI systems can enhance sovereignty by helping people navigate overwhelming information landscapes, filter noise, and access relevant knowledge. However, when these systems are controlled by concentrated powers (corporations, states, or unaccountable algorithms), they can also become instruments of unprecedented manipulation, surveillance, and the shaping of reality itself. The concentration of power over information mediation in AI represents one of the most significant challenges to autonomy in the digital age.

Consequences that now become visible: • Many current concerns about AI (bias, manipulation, surveillance, filter bubbles) can be understood as concerns about the erosion of information sovereignty. • The governance of AI systems that mediate information is therefore not only a technical issue but a fundamental question of power, autonomy, and democratic self-determination. • Advanced AI systems that can generate highly personalized and persuasive content raise new ethical and political questions about manipulation and the integrity of individual and collective decision-making. • Information sovereignty may become one of the central battlegrounds of the 21st century, alongside traditional concerns about territorial and economic sovereignty. • The development of decentralized, transparent, and accountable AI systems for information mediation could help protect and enhance information sovereignty rather than undermine it.

Outside instrument

Crux

Stake a prior. Name the disagreement. Not a theorem.

Open full

Idea Series

Article-base cycle tokens and Atlas-named hubs. Art.n is not Th.n.

Applications

Write a withdrawal condition

Outside the corpus

Journals, repositories, counters

attention · human–AI hybrid systems. A paper can support a concept. It cannot support a necessity claim. Th.48 remains unproved by every card below.

Support · living source

Tight papers that bear on concepts inside Th.48. A DOI is not a join.

  • Bakshy, E., Messing, S. & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science 348, 1130.

    Individual choice and ranking both reduce cross-cutting exposure.

    Does not support the necessity claim

    Open originalNamed on Art.36, 72, 79

  • Huszár, F. et al. (2022). Algorithmic amplification of politics on Twitter. PNAS 119, e2025334119.

    The ranking layer is not neutral; it is a fixed-point of attention.

    Does not support the necessity claim

    Open original

  • Iyengar, S. & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. Am. J. Polit. Sci. 59, 690.

    Affective polarization as a measured identity split, not a media-only artifact.

    Does not support the necessity claim

    Open original

  • Weber, E. U. (2006). Experience-based and description-based perceptions of long-term risk. Climatic Change 77, 103.

    Brain timescales undervalue planetary risk that is described, not lived.

    Does not support the necessity claim

    Open original

  • Hadfield-Menell, D., Dragan, A., Abbeel, P. & Russell, S. (2016). Cooperative inverse reinforcement learning. NeurIPS 2016.

    Alignment as a cooperative game, not a one-way value load.

    Does not support the necessity claim

    Open originalNamed on Art.1, 71

  • Dafoe, A. et al. (2020). Open problems in cooperative AI. arXiv:2012.08630.

    Names multi-agent closure, institutions, and commitment as the open set.

    Does not support the necessity claim

    Open originalNamed on Art.1, 71

Counter-arguments · refusing pole

One domain counter per cluster, not a repeating method quartet. The mirror reads these as enough to refuse necessity.

  • Flaxman, S., Goel, S. & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opin. Q. 80, 298.

    Counter: online news can increase exposure to opposing views even inside partisan sites.

    Does not support the necessity claim

    Open originalNamed on Art.72

  • Boxell, L., Gentzkow, M. & Shapiro, J. M. (2017). Greater Internet use is not associated with faster growth in political polarization. PNAS 114, 10612.

    Counter: the oldest, least-online cohorts polarized fastest — feeds are not the whole cause.

    Does not support the necessity claim

    Open originalNamed on Art.36, 79

  • Bostrom, N. (2012). The superintelligent will: Motivation and instrumental rationality in advanced artificial agents. Minds Mach. 22, 71.

    Counter: orthogonality — intelligence and final goals can come apart; co-evolution is not necessary.

    Does not support the necessity claim

    Open originalNamed on Art.1, 71

  • Hubinger, E. et al. (2019). Risks from learned optimization in advanced machine learning systems. arXiv:1906.01820.

    Counter: inner objectives can come apart from outer ones even with cooperative training.

    Does not support the necessity claim

    Open original