Cognitive Liberties
Thought diversity · Revised detailed companion investigations

Corrected supplemental research: Making search and AI omissions observable, explainable and contestable

Research edition · October 11, 2026 · 834 source words

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Rewritten replacement for uploaded document: Search Monopolies And Information Discovery.md. This document does not claim to preserve the language or total length of the original upload; it preserves the topic and relevant independently supported findings while removing the incorrect privacy-first thesis and unsupported assertions. Original uploaded text remains available separately in the prior expanded archive.

Editorial position and scope

Proposed mission: Cognitive liberty means that people can think and inquire freely, seek and receive information, voluntarily share their views and evidence, encounter disagreement, and challenge opaque decisions that prevent lawful information from being found. Access does not equal endorsement: freedom to inspect a claim is compatible with labeling its evidence weak, correcting falsehoods, and applying proportionate lawful restrictions. Individual privacy remains an option and a safeguard, not the central objective or a demand that people stop sharing with knowledge systems.

Status of this document: Independent desk research as of October 11, 2026. It uses public sources and the existing public website. It is not an internal site audit, a measured search-ranking experiment, an inspection of proprietary AI training datasets, or legal advice. Numbered [S##] citations resolve to SOURCES.md.

Corrections to the uploaded study

The uploaded market-concentration manuscript contained point estimates of market and AI referral share without complete methodological framing. Such metrics change with geography, denominator and methodology; the revised edition avoids using the old figures as timeless truths.

Newly verified public evidence

Revised interpretation centered on expression and discovery

Accountability requires diagnosing pipeline stages, repeatable visibility audits, publisher tools and context-specific appeals. European DSA rules create concrete transparency duties for covered services, but they do not promise all viewpoints equal exposure and do not apply identically worldwide.

A useful audit records discovery pipeline transitions: URL accessible → crawled → indexed → retrieved for test query → ranked → cited → accurately described → clicked. Loss of visibility at one stage has a different remedy from failure at another.

Compare pairs of ideas with similar source quality and wording, across query paraphrases, locations, devices and repeated dates. Pre-register the reference set and allow independent reviewers to challenge the benchmark’s own ideological framing.

When a platform provides no appeal, a third-party reporting system can still publish transparent observations with screenshots, request dates, technical diagnostics, confidence intervals and documented corrections. Do not infer motives from outcomes alone.

Public accountability should aim for notice, explanations, auditing access, due process where appropriate, and meaningful exit via independent links. Disclosure should not require revealing detailed anti-spam rules useful to attackers.

Competition continuity

Assess competition as a combination of distribution defaults, independent indexes, publisher relationships, AI interface selection and alternative access routes. Do not reuse volatile percentage shares without a matching 2026 date, geography and measurement denominator. The EU DSA provides defined transparency duties for certain services; it is not a blanket rule mandating ideological balance in search. The public-interest goal is credible user choice, evidence of differential treatment and procedural accountability, not a predetermined ruling about an actor’s motives. [S13][S14]

Practical application and build instructions

  1. Develop a public “Why is this source missing?” troubleshooting page.
  2. Publish a downloadable visibility audit dataset with neutral topic coding and replicable scripts.
  3. Draft a corrections and challenge process for errors in the project’s own directory.

Research questions and ways the thesis could fail

How can users and publishers test whether lawful content is excluded, demoted or misrepresented by search and AI systems, without presuming that every low ranking is censorship?

Unresolved: Ordinary relevance ranking, lawful moderation and technical crawling defects must be distinguished from discriminatory exclusion. Many model internals are not public.

Sources

Continue independent inquiry

Follow the cited primary sources, compare their evidence, and draw your own conclusions. Disagreement is part of discovery—not evidence of wrongdoing.

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