Strongest objection
What pushes against this view?
Full transparency can expose abuse-detection logic, enable gaming, leak sensitive signals, or be impractical for complex models.
QAEDA-ARG-005 · working thesis
Consequential algorithmic inference needs recourse
When automated inference materially restricts access, opportunity, visibility, or inquiry, the affected person should receive intelligible reasons, a path to correction, and review with authority to reverse error.
Current response
Why QAEDA still holds it.
Due process does not require publishing every model weight. It requires enough notice, reason categories, evidence access, correction mechanisms, and independent or human review to make consequential error contestable.
Revision rule
What would change the argument?
Revise if empirical evaluation identifies alternative procedural mechanisms that correct consequential errors more reliably while revealing less exploitable detail.
Open question
What remains unresolved?
What minimum explanation is sufficient for meaningful appeal without making abuse systems trivial to evade?
Argument graph
How this thesis connects.
operationalizesQAEDA-ARG-001 · Lawful inquiry begins with non-suspicionWhen systems do move from query context to consequential person-level inference, due process makes that transition contestable.related via operationalizesQAEDA-ARG-012 · Audit behavior, not only policy promisesBehavioral auditing provides evidence about whether transparency, appeal, and correction mechanisms work in practice.related via supportsQAEDA-ARG-013 · Visibility control can restrict access without deletionVisibility controls make notice and appeal important even when content is not formally removed.
Apply the argument
Decision tests that operationalize this reasoning.
These tests turn the argument into review questions. They do not create a score or change claim confidence.
QAEDA-TST-006Require notice, reasons, and recourseIf the system materially restricts a person, can they understand what happened, contest the relevant evidence, and obtain meaningful human review?QAEDA-TST-008Measure efficacy and make rollback realHow will we know the intervention reduced the stated harm, what adverse effects will be measured, and what mechanism actually reverses or narrows the system if it fails?
Trace the basis
Linked public records
These links do different jobs. Claim status owns evidence confidence; source IDs own preserved-file identity; annotations own passage context; positions own current commitments.
Claims
QAEDA-CLM-020editorial_inferenceAlgorithmic due processQAEDA-CLM-011supportedSearch-query sensitivityPositions
algorithmic-due-processConsequential algorithmic decisions need recourseworking positionPassage annotations
QAEDA-ANN-004Open evidence notePassage-level contextQAEDA-ANN-014Open evidence notePassage-level contextPreserved sources
QAEDA-SRC-019The Algorithmic Judgment of Intent: How AI Search Systems Profile the User Behind the QueryProtected dossier fingerprintQAEDA-SRC-020Algorithmic Gatekeeping and Cognitive Liberty: The Architecture of Search Engine Ranking and AI-Mediated DiscoveryProtected dossier fingerprintQAEDA-SRC-022The Cognitive Liberty Audit Framework: Standardizing the Evaluation of AI Systems and Digital PlatformsProtected dossier fingerprintEssays
QAEDA-ART-003The algorithmic judgment of intentEditorial synthesisQAEDA-ART-009Audit the gatekeeperEditorial synthesisReading paths
QAEDA-COL-002Cognitive liberty in the AI eraCurated navigationQAEDA-COL-003Safety without a permanent ratchetCurated navigationChallenge the reasoning
The graph is a map of current arguments, not a demand for agreement.
Point to a missing premise, stronger objection, counterexample, source, or revision condition. Stable IDs make disagreement specific.