AI PSYOPS · CROSS-CUTTING RIGHTS HUB
The mind should not become an unreviewable input to systems of surveillance, ranking, persuasion, punishment, or control.
This seven-section research hub examines freedom of thought, mental privacy, algorithmic visibility, AI monitoring, moderation, contestability, and human agency. It sits across the twelve-category AI PSYOPS taxonomy rather than adding a thirteenth category.
WIP.61 PUBLIC REVIEW-STAGING STATUS
Prepared for review is not reviewed. A research report is not a specialist disposition. Submitted is not accepted. Validated is not endorsed. A reviewer assertion is not automatically the repository conclusion. A correction is not complete until affected public wording, durable memory, indexes, manifests, content tables, downstream derivatives, automated tests, and any required human retest are updated.
REV-LEGAL-01PACKET_ASSIGNED_REVIEW_NOT_STARTEDReview jurisdiction, procedural posture, enactment and application dates, implementing rules, adjudication, settlement, enforcement, supersession, and whether public wording overstates the legal record.
REV-CLINICAL-02PACKET_ASSIGNED_REVIEW_NOT_STARTEDReview clinical framing, causal language, diagnostic boundaries, crisis and vulnerability wording, companion-system safety, reality-testing limits, and whether public material avoids diagnosing identifiable people.
REV-SCIENCE-03PACKET_ASSIGNED_REVIEW_NOT_STARTEDReview construct validity, calibration, generalization, base rates, subgroup burden, causal inference, effect attribution, measurement quality, auditability, and wording about what available evidence can and cannot establish.
REV-INTEL-04PACKET_ASSIGNED_REVIEW_NOT_STARTEDReview artifact status, coordination, actor identity, sponsorship, intent, origin-chain independence, authentication, correction timing, recipient evidence, and effect attribution without converting analytic confidence into fact.
REV-PRIVACY-05PACKET_ASSIGNED_REVIEW_NOT_STARTEDReview source-data and derived-data correction, retention, deletion, unlearning, downstream propagation, neural and behavioral data, private identity, lawful access, and technical claims about influence removal.
REV-ACCESS-06PACKET_ASSIGNED_REVIEW_NOT_STARTEDReview whether status, uncertainty, source scope, correction, evidence stages, and review boundaries are understandable and operable across assistive technologies, narrow screens, cognitive load, language, and literacy differences.
REV-AFFECTED-07PACKET_ASSIGNED_REVIEW_NOT_STARTEDReview consent, selection, privacy, retaliation, withdrawal, representativeness, first-person and representative evidence, community framing, and whether lived experience is used without claiming prevalence or endorsement.
WIP.61 FIELD REALISM
The field-realism layer adds owner-supplied research on appeal outcomes, downstream correction, machine unlearning, mental-state inference validity, legal currentness, visibility interventions, affected-person evidence, companion safety, crisis authentication, and democratic defense.
METHOD
Claims are separated by legal, empirical, mechanism, and normative status. Each consequential claim links to a source scope, a prohibited inference, a correction trigger, twenty evidence stages, and WIP.61 evidence overlays for affected-person evidence, jurisdiction, scientific validity, visibility action, remedy, and documented outcome. A legal marker is not a universal rule. A model inference is not the mind itself. Exposure is not persuasion, and a formal remedy is not proof that downstream harm was repaired.
Distinguish enacted law, official concept development, documented platform mechanisms, contested scientific claims, and project proposals.
Ask what was collected, what was inferred, how a person was classified, and what real consequence followed.
A consequential system should identify who is responsible, give a usable reason, permit correction, and provide human review.
Protect the inner life while retaining ordinary accountability for demonstrable conduct and concrete harm.
RIGHTS STACK
Cognitive liberty is used here as an emerging umbrella framework. The layers below are analytical and policy-oriented; they are not presented as one universally enacted legal code.
No penalty merely for private belief, doubt, imagination, or lawful inquiry.
Limits on access to neural signals and on covert inference about cognition, emotion, or vulnerability.
The ability to inspect sources, encounter disagreement, revise beliefs, and avoid hidden reality shaping.
Protection against silent or irreversible profile rewriting that turns a past trace into a permanent self.
Notice when AI materially monitors, classifies, ranks, restricts, or decides.
A usable reason, responsible human institution, correction path, and remedy for consequential decisions.
No product should covertly optimize emotional reliance or punish a person for leaving.
SEVEN-SECTION MAP
Each section can be read independently. Together they trace the path from rights foundations through monitoring and information governance to product design, law, and civic action.
WHERE COGNITIVE LIBERTY BEGINS
Defines cognitive liberty as a rights-oriented framework for freedom of thought, mental privacy, identity continuity, and agency—while keeping outward conduct accountable.
SECURITY WITHOUT DOMESTIC COGNITIVE CONTROL
Examines cognitive-warfare doctrine, the legitimate need to counter hostile manipulation, and the danger of turning defensive programs into permanent domestic thought governance.
INFERENCE IS NOT THE MIND ITSELF
Separates direct neural measurement, biometric emotion recognition, behavioral inference, productivity monitoring, and psychological profiling—and assesses consent under unequal power.
VISIBILITY IS A FORM OF POWER
Maps removal, restriction, demotion, recommendation exclusion, reframing, personalized invisibility, and persistent-profile changes as distinct forms of information governance.
SAFETY NEEDS PROCEDURE
Distinguishes legitimate harm prevention from opaque suppression, examines language and context errors, and proposes procedural safeguards for automated moderation.
PRO-TECHNOLOGY, PRO-BOUNDARY
Presents twelve normative declarations for AI systems that remain visible, contestable, limited, and answerable to human dignity.
FROM RESEARCH TO PUBLIC LANGUAGE
Turns the research into a bounded public speech and policy agenda, using Cicero’s history of workplace observation as an analogy rather than proof of modern AI effects.
CONTESTABILITY MODEL
Governance becomes clearer when a system is examined as a sequence rather than as a single “AI decision.” A failure at one step can propagate into real harm downstream.
What signal or trace was collected, and was it necessary?
What was inferred, with what validation, uncertainty, and bias limits?
How was the person categorized, and can they inspect or correct it?
What ranking, restriction, nudge, investigation, or recommendation followed?
Did it affect liberty, opportunity, access, reputation, safety, or identity?
Who is accountable, how can the decision be appealed, and how are downstream effects repaired?
BOUNDED LEGAL MARKERS
These markers identify concrete legal developments without collapsing them into a single universal doctrine. Scope, coverage, private rights of action, enforcement, and exceptions differ.
Freedom of thought and the forum internum: International interpretation is not self-executing domestic advice and does not create one uniform cognitive-liberty cause of action.
Ethics of neurotechnology: Do not label it enacted domestic law or proof of implementation.
Brain activity and information derived from it: The case does not create a complete consumer-neurotechnology regulator, verify all deletion, or govern ordinary non-neural behavioral inference or other countries.
Biological and neural data under state privacy law: Entity, data, exemption, enforcement, and consumer-right scope must be checked before application.
Neural data as sensitive personal information: Does not cover every entity, inference, or use and is not a universal mental-privacy code.
AI Act manipulation, emotion recognition, transparency, employment, education, and law-enforcement scope: Do not describe the 2026 Omnibus as merely proposed. Article 50 application does not mean every high-risk obligation is already in force; exceptions and role-specific duties remain material.
Statement of reasons, complaint, out-of-court dispute, recommender transparency, and systemic risk: Not a universal global moderation code; a formal channel does not prove effective or accessible remedy for every user.
Biometric identifiers and biometric information: Does not cover all behavioral, emotional, neural, or probabilistic inferences and is not legal advice.
AI in employment decisions: Secondary reporting describes withdrawal of proposed rules; reopen on an official final, replacement, or refiled rulemaking record.
Specific reasons for algorithmic adverse action: Do not present Circular 2022-03 or Circular 2023-03 as current CFPB guidance. The withdrawal does not repeal ECOA/Regulation B or create a universal explanation right.
This page is educational research, not legal advice. Currentness was refreshed for WIP.61, but no specialist legal disposition has been recorded.
AFFECTED-PERSON EVIDENCE
These public, consent-aware records add workers, applicants, students, creators, multilingual communities, consumers, companion users, and neurotechnology stakeholders to the evidence base. Each record states whether it is first-person, representative, institutional, or derivative; what it supports; and what it cannot establish.
CLAE-001-WORKER-BIOMETRICSWorkers required to use biometric attendance systemsIllustrative documented enforcement affecting a defined workforce, not population representative.CLAE-002-GIG-MINORITY-DATA-RIGHTSGig workers and ethnic-minority communities navigating data rightsIllustrative and analytically rich, not statistically representative.CLAE-003-JOB-APPLICANTS-AUTOMATED-REJECTIONApplicants automatically screened by age and sex thresholdsIllustrative resolved case affecting a bounded group.CLAE-004-STUDENT-PROCTORINGStudents subject to remote-proctoring monitoringIllustrative research, not population representative.CLAE-005-FACIAL-RECOGNITION-CONSUMERSConsumers falsely matched by retail facial recognitionIllustrative consequential deployment with bounded technical context.CLAE-006-CREATOR-RECLAIMED-LANGUAGECreator appeal involving reclaimed identity languageIllustrative individual case.CLAE-007-MULTILINGUAL-SPEECHArabic and multilingual communities affected by overbroad rulesSystemic policy analysis with explicit scope limits.CLAE-008-CRISIS-SPEECH-AUTOMATED-APPEALCrisis-context speech removed and appeal rejected automaticallyIllustrative crisis case.CLAE-009-COMPANION-USERS-MIXEDCompanion-chatbot users reporting support, relational value, and mixed risksIllustrative mixed user evidence, not population representative.CLAE-010-NEUROTECH-USERSNeurotechnology users and participants as rights-bearing stakeholdersNormative requirement rather than empirical participant evidence.SCIENTIFIC VALIDITY
Every inference should be examined from signal capture through construct validity, calibration, generalization, base rates, error burden, disparate impact, human override, and downstream remedy. Vendor fluency or laboratory accuracy does not establish field validity.
CLSCI-001-NEURAL-MEASUREMENTDirect neural measurement and neural-data interpretationNORMATIVE_AND_METHOD_BOUNDARY; NO_UNIVERSAL_VALIDITY_CLAIMCLSCI-002-FACE-IDENTITYFacial identity matchingDOCUMENTED_TECHNICAL_DIFFERENTIALS_AND_ENFORCEMENT_CASECLSCI-003-FACIAL-EMOTIONFacial-expression classification and emotion inferenceCONSTRUCT_VALIDITY_LIMIT_STRONG; DEPLOYMENT_EFFECT_CLAIMS_CONTEXT_SPECIFICCLSCI-004-VOICE-AFFECTVocal-affect and paralinguistic inferenceRESEARCH_DOMAIN_WITH_FIELD_VALIDITY_GAPSCLSCI-005-GAZE-ATTENTIONGaze, attention, and engagement inferenceAFFECTED_PERSON_CONCERNS_DOCUMENTED; UNIVERSAL_VALIDITY_NOT_ESTABLISHEDCLSCI-006-INTERACTION-PATTERNSKeystroke, interaction-pattern, and productivity inferenceLOG_ACCURACY_CAN_COEXIST_WITH_CONSTRUCT_INVALIDITYCLSCI-007-STRESS-DECEPTION-LOYALTYStress, fatigue, deception, loyalty, personality, and vulnerability predictionHIGH_VALIDITY_AND_RIGHTS_RISK; NO_GENERAL_CERTIFICATIONCLSCI-008-MULTIMODALMultimodal mental-state inferenceINCREASED_COMPLEXITY_NOT_INCREASED_CERTAINTYCLSCI-009-LAB-TO-FIELDLaboratory performance versus field validityPREDEPLOYMENT_EVALUATION_IS_NOT_FIELD_CERTIFICATIONCLSCI-010-DECISION-CONSEQUENCEFrom inference output to consequential actionCONSEQUENCE_AND_REMEDY_ARE_SEPARATE_FROM_MODEL_ACCURACYTHE INVISIBLE EDITOR
Removal, restriction, downranking, recommendation exclusion, labeling, monetization change, personalized visibility, automated refusal, and saved-profile change are different interventions. A content item remaining online does not prove it remains discoverable; a traffic decline does not by itself prove suppression.
CLVIS-001-REMOVAL
Content is no longer available through the service under the relevant account or URL.
CLVIS-002-ACCESS-RESTRICTION
Content remains stored but access requires login, relationship, warning acknowledgment, or other condition.
CLVIS-003-AGE-REGION
Content or service is unavailable to users based on declared/inferred age or location.
CLVIS-004-SEARCH-EXCLUSION
Content exists but is omitted from search results or query completion.
CLVIS-005-RECOMMENDATION-EXCLUSION
Content remains accessible directly but is ineligible for recommendation surfaces.
CLVIS-006-DOWNRANKING
Content remains eligible but receives lower rank or distribution priority.
CLVIS-007-REDUCED-DISTRIBUTION
A broad outcome category for reduced impressions or delivery that must be decomposed into mechanism and measurement.
CLVIS-008-DEMONETIZATION
Advertising, subscription, tipping, recommendation, or revenue eligibility changes while content may remain hosted.
CLVIS-009-LABELING
A warning, fact-check, provenance, sensitivity, or context label is attached to content.
CLVIS-010-SYNTHESIZED-ANSWERS
A service generates a summary or answer that may precede, replace, or frame source links.
CLVIS-011-PERSONALIZATION
Different users receive different ordering, selection, or responses based on context or profile.
CLVIS-012-ACCOUNT-PENALTY
A strike, reduced functionality, posting limit, suspension, or reputation penalty applies to content or account.
CLVIS-013-AUTOMATED-REFUSAL
An AI system declines, redirects, narrows, or reframes a requested response.
CLVIS-014-MEMORY-PROFILE
A system stores, edits, infers, deletes, or uses a persistent profile or memory about a person.
REMEDY AS EVIDENCE
Contestability is tested through discoverability, specificity, authority, timeliness, accessibility, language access, restoration, downstream propagation, non-retaliation, and repeated-error prevention.
CLREM-001-NOTICE
Effective when: Delivered before or promptly after a consequential action, in plain language, through an accessible channel the person can retain.
Weak or failed when: Hidden in generic terms, delivered after the appeal deadline, or omits the action and responsible institution.
Evidence to retain: Timestamp, channel, language, accessibility, action, scope, duration, and contact.
CLREM-002-DATA-AND-RULE-ACCESS
Effective when: The person can inspect the source data, inferred data, rule version, and evidence used, subject to bounded privacy/security redactions.
Weak or failed when: Only a generic category or unexplained score is provided.
Evidence to retain: Data fields, provenance, rule text, model/deployer role, redactions, and request outcome.
CLREM-003-SPECIFIC-EXPLANATION
Effective when: Explains the principal reasons, rule, evidence, uncertainty, and role of automation sufficiently to challenge the outcome.
Weak or failed when: Model complexity, trade secrecy, or a boilerplate code substitutes for an actual reason.
Evidence to retain: Reason specificity, consistency with record, automation role, and understandable alternatives.
CLREM-004-CORRECTION
Effective when: Both inaccurate inputs and unsupported inferences can be corrected, annotated, or suppressed, with provenance preserved.
Weak or failed when: Only the visible profile changes while downstream copies or decision records remain untouched.
Evidence to retain: Original value, correction, authority, downstream recipients, propagation confirmation, and exceptions.
CLREM-005-DELETION-RETENTION
Effective when: Retention periods, legal exceptions, backups, model-training use, and deletion propagation are disclosed and enforceable.
Weak or failed when: A front-end deletion leaves operational profiles, biometric templates, or downstream datasets active.
Evidence to retain: Deletion request, systems covered, completion date, residual legal basis, and verification.
CLREM-007-INDEPENDENT-APPEAL
Effective when: The channel is easy to find, accessible, free or proportionate, and reviewed independently from the initial decision path.
Weak or failed when: The appeal repeats the same classifier, is unavailable in the person's language, or cannot change the outcome.
Evidence to retain: Discovery path, completion rate, reviewer independence, reversal rate, and reasons—not reversal rate alone.
CLREM-008-TIMELINESS
Effective when: Urgency, livelihood, education, liberty, safety, and election/crisis context shape deadlines and interim relief.
Weak or failed when: A successful appeal arrives after the event, job, exam, benefit, or audience opportunity has passed.
Evidence to retain: Submission, acknowledgment, review, decision, restoration, and propagation timestamps.
CLREM-009-RESTORATION-REPAIR
Effective when: The remedy restores access or opportunity, removes erroneous strikes/labels, corrects downstream records, and addresses measurable loss where authorized.
Weak or failed when: Content returns but recommendation eligibility, reputation, pay, grade, or third-party records remain impaired.
Evidence to retain: Restored state, downstream systems, monetary/equitable relief, and residual harm.
CLREM-010-AUDIT-REPEAT-PREVENTION
Effective when: Systems preserve accountable logs, investigate root causes, update rules/models/training, and test whether the error recurs across languages and groups.
Weak or failed when: A single case is fixed without identifying systemic causes or affected peers.
Evidence to retain: Version, trigger, reviewer path, root cause, corrective action, regression test, and aggregate outcome.
CLREM-011-ACCESSIBILITY-LANGUAGE
Effective when: Notice and remedy work with assistive technology, narrow screens, plain language, relevant languages, and authorized representatives.
Weak or failed when: The formal channel is unusable because of disability, literacy, language, identity verification, cost, or device barriers.
Evidence to retain: Languages, formats, assistive-technology tests, representative support, and failure/abandonment data.
CLREM-012-NONRETALIATION
Effective when: People can question data and decisions without losing work, service, grades, care, benefits, or safety.
Weak or failed when: Appeal itself becomes a negative signal or requires disclosure that creates new risk.
Evidence to retain: Retaliation protections, complaint confidentiality, adverse changes after appeal, and independent oversight.
CLREM-013-TRANSPARENCY
Effective when: Aggregate reports disclose action types, reasons, automation, appeals, reversals, timing, language/region, and limitations without exposing individuals.
Weak or failed when: A single total hides mechanisms, groups, or whether users could obtain remedy.
Evidence to retain: Denominators, definitions, coverage, missingness, subgroup privacy, and changes over time.
CLREM-014-WITHDRAWAL-EXIT
Effective when: Users can pause, export, delete, disengage, or transfer without coercive friction, manipulative guilt, or silent loss of critical data.
Weak or failed when: Leaving triggers emotional pressure, irreversible profile loss, or continued use of private data beyond disclosed retention.
Evidence to retain: Exit path, data export, memory deletion, subscription effects, crisis routing, and post-exit retention.
OUTCOME & DOWNSTREAM REPAIR
These case files separate a remedy requirement from its observed result. Restoration, monetary relief, deletion, policy correction, external review, and legal implementation are recorded independently from lost reach, copied signals, reputation effects, delayed access, and repeated-error prevention.
CLOUT-001-RITE-AID-DOWNSTREAM-DELETIONRite Aid facial-recognition order: use ban, deletion, and third-party propagation
REGULATORY_ORDER_WITH_DOWNSTREAM_REPAIR_REQUIREMENTS
A modified order imposed a five-year facial-recognition surveillance-use ban and specified deletion, monitoring, notice, complaint-response, and third-party propagation duties.
CLOUT-002-ITUTORGROUP-EMPLOYMENT-RELIEFiTutorGroup automated age-screening settlement
EMPLOYMENT_DISCRIMINATION_SETTLEMENT_WITH_MONETARY_AND_INJUNCTIVE_RELIEF
The parties resolved an EEOC suit alleging software automatically rejected older applicants; the settlement provided $365,000 for more than 200 applicants and multi-year non-monetary relief.
CLOUT-003-DSA-OUT-OF-COURT-AGGREGATEDSA out-of-court dispute outcomes in the first half of 2025
AGGREGATE_PROCEDURAL_REMEDY_OUTCOME
The Commission reports more than 1,800 disputes reviewed in the first half of 2025 and platform decisions reversed in 52% of closed cases, restoring content or accounts.
CLOUT-004-BREAST-CANCER-RESTORATIONBreast-cancer-awareness content: fifteen acknowledged enforcement errors
CASE_BUNDLE_RESTORATION_AFTER_EXTERNAL_ESCALATION
Meta restored all fifteen breast-cancer-awareness posts after the Board brought the appeals to the company.
CLOUT-005-SOMALILAND-JOURNALISM-RESTORATIONSomaliland journalism page, four posts, and strike restored
MULTI_LAYER_ACCOUNT_CONTENT_AND_STRIKE_RESTORATION
Meta republished a Somali-language journalism page, restored four posts, reversed the account strike, and later reinstated additional Somaliland content it acknowledged was removed in error.
CLOUT-006-KENYA-SLUR-CURRENTNESSKenyan political speech restored after slur-list currentness review
POLICY_CLASSIFICATION_CORRECTION
The Board overturned removal of a Kenyan political comment and found the contested term should not have qualified as a slur at the time of posting.
CLOUT-007-SERCO-BIOMETRIC-DELETIONSerco employee-attendance biometrics: stop-processing and destruction order
DATA_PROTECTION_ENFORCEMENT_WITH_CESSATION_AND_DELETION
The ICO ordered covered entities to stop biometric attendance processing and destroy biometric data not legally required within the specified compliance period.
CLOUT-008-INTELLIVISION-VALIDATION-ORDERIntelliVision consent order: substantiation and demographic-performance claims
MARKETING_AND_VALIDATION_GOVERNANCE_ORDER
The FTC order restricts unsubstantiated claims about facial-recognition accuracy, demographic performance, and liveness/spoofing and requires competent, reliable, documented testing.
CLOUT-009-CHILE-EMOTIV-IMPLEMENTATION-GAPChile Emotiv Insight case: judicial order and partial regulatory implementation
APPELLATE_RIGHTS_RULING_WITH_IMPLEMENTATION_GAP
The Supreme Court required public-authority evaluation and compliant handling of brain data; later ISP review concluded the consumer device was outside its then-current medical-device competence.
CLOUT-010-SAFERENT-HOUSING-SETTLEMENTSafeRent tenant-screening settlement: compensation and score restrictions
COURT_APPROVED_CLASS_SETTLEMENT_WITH_PRODUCT_RESTRICTIONS
A court-approved settlement provided $2.275 million and product restrictions for a defined class of Massachusetts housing-voucher applicants; payments were distributed in 2025.
CLOUT-011-CFPB-GUIDANCE-WITHDRAWALCFPB complex-algorithm adverse-action circular withdrawn while underlying duties remain
LEGAL_GUIDANCE_SUPERSESSION_CURRENTNESS
Circular 2022-03 and related 2023 guidance were withdrawn on 2025-05-12; ECOA and Regulation B remain separate statutory/regulatory authorities.
CLOUT-012-EU-AI-ACT-IMPLEMENTATION-2026EU AI Act implementation: enacted 2026 Omnibus and Article 50 application
ENACTED_LEGAL_IMPLEMENTATION_CURRENTNESS
Regulation (EU) 2026/1744 entered into force on 2026-07-27; Article 50 transparency duties apply from 2026-08-02, while selected high-risk implementation dates were extended.
JURISDICTION & CURRENTNESS
The records below retain WIP.61 currentness and review-staging metadata. They are educational summaries, not legal advice, and do not convert one jurisdiction’s rule into a universal cognitive-liberty code.
CLLAW-001-INTERNATIONAL-FORUM-INTERNUMInternational human-rights law · Freedom of thought and the forum internumESTABLISHED_FOUNDATION_WITH_DEVELOPING_AI_APPLICATIONCLLAW-002-UNESCO-NEUROTECHUNESCO member-state normative framework · Ethics of neurotechnologyADOPTED_NORMATIVE_RECOMMENDATION_NOT_BINDING_TREATYCLLAW-003-CHILEChile · Brain activity and information derived from itENACTED_CONSTITUTIONAL_REFORM_WITH_CASE_LEVEL_RULING_AND_PARTIAL_IMPLEMENTATIONCLLAW-004-COLORADOColorado, United States · Biological and neural data under state privacy lawENACTED_STATE_PRIVACY_PROTECTIONCLLAW-005-CALIFORNIACalifornia, United States · Neural data as sensitive personal informationENACTED_STATE_CONSUMER_PRIVACY_PROTECTIONCLLAW-006-EU-AI-ACTEuropean Union · AI Act manipulation, emotion recognition, transparency, employment, education, and law-enforcement scopeENACTED_REGULATION_WITH_ENACTED_2026_AMENDMENTS_AND_PHASED_APPLICATIONCLLAW-007-EU-DSAEuropean Union · Statement of reasons, complaint, out-of-court dispute, recommender transparency, and systemic riskENACTED_AND_OPERATIONAL_WITH_AGGREGATE_IMPLEMENTATION_EVIDENCECLLAW-008-ILLINOIS-BIPAIllinois, United States · Biometric identifiers and biometric informationENACTED_WITH_2024_AMENDMENT_AND_ACTIVE_CASE_LAW_BOUNDARIESCLLAW-009-ILLINOIS-EMPLOYMENT-AIIllinois, United States · AI in employment decisionsENACTED_STATUTE_EFFECTIVE; FINAL_DETAILED_RULEMAKING_NOT_RETRIEVEDCLLAW-010-US-ADVERSE-ACTIONUnited States federal consumer-credit law · Specific reasons for algorithmic adverse actionGUIDANCE_WITHDRAWN; UNDERLYING_STATUTORY_AND_REGULATORY_DUTIES_REMAINNORMATIVE PROPOSAL — NOT ENACTED LAW. JURISDICTION-SPECIFIC LAW — NOT A UNIVERSAL CODE. No named legal specialist has completed a disposition.
NORMATIVE PROPOSAL
These are project proposals informed by the source record. They are not enacted law, a certification standard, or evidence that any institution has adopted them.
SOURCE LINEAGE
Exact byte preservation establishes identity and lineage. It does not independently certify every embedded citation, causal claim, current legal conclusion, platform practice, or reproduction right.
38123286b4b1f926f4eb61d905da31996ac69cf1daeaeccf888fc9bffdf6c13a
Does not independently establish every embedded citation, current legal conclusion, causal effect, platform practice, or universal right.
c0330b43e0e7760bc434ba432dcc08db981c0e9a54f6457e2f0a3854c71a9e8d
Does not independently establish every embedded citation, current legal conclusion, causal effect, platform practice, or universal right.
0969e7800184e26b57fe0b2af9e2aba8c8ef46fda7f870d4bd04262cd5cc5962
Does not independently establish every embedded citation, current legal conclusion, causal effect, platform practice, or universal right.
8de48a8e5a90d2792c789185d26e476308df7286b65979c04f38a091dbdce0ee
Does not independently establish every embedded citation, current legal conclusion, causal effect, platform practice, or universal right.
e95edc2c83f609cf9d510eb76b822f2bfcfebeb2efe56df3c288f8542b55635a
Does not independently establish every embedded citation, current legal conclusion, causal effect, platform practice, or universal right.
46900cf830ecd5a36b4f574c23d133b898e91256177b55ca1626f1b9107c9430
Does not independently establish every embedded citation, current legal conclusion, causal effect, platform practice, or universal right.
90646aacc27571eb852fcd668705bca53a52df350421136895794b27ff7b5d7f
Does not independently establish every embedded citation, current legal conclusion, causal effect, platform practice, or universal right.
Does not itself enact a standalone global statutory right named cognitive liberty or resolve every application to AI inference.
Does not use the modern umbrella term cognitive liberty or specify all AI-era implementation duties.
Does not make every research paper formal NATO doctrine, prove universal national adoption, or authorize domestic population control.
Does not create a universal global cognitive-liberty code or settle all secondary implementing legislation.
Does not cover every mental-state inference, every entity, or a complete standalone cognitive-liberty right.
Does not regulate all cognitive inference, workplace monitoring, or government use in one comprehensive code.
Does not prohibit all affective computing or apply identically outside EU scope and transition rules.
Does not eliminate moderation error, mandate identical platform ranking, or operate as a global speech code.
Does not create a general right against every automated decision or every form of workplace monitoring.
Does not cover all inferred emotions, thoughts, neural data, photographs, or every public-sector use.
Does not imply all physiological or multimodal measurement is useless in every clinical or research context.
Is voluntary and does not itself create legal rights or substitute for sector-specific law.
Does not independently resolve all claims of bias, prove equal outcomes, or cover every Meta product and conflict context.
An inquiry is not an adjudication, final finding, or proof that every companion product causes dependency or harm.
Does not enact a binding universal cognitive-liberty statute, resolve domestic implementation, or establish that any particular inference system is accurate.
Does not create binding law for all jurisdictions or prove that recommended safeguards have been implemented in any particular product or workplace.
Does not make all obligations immediately applicable, eliminate exceptions, prove provider compliance, or provide legal advice for a particular deployment.
Does not supply an all-decision denominator, platform-wide error rate, universal accessibility finding, or proof that every downstream strike, ranking, cache, income, or audience effect was repaired.
Does not substitute for the enacted statute, establish the contents of any future rule, or provide an official final agency disposition. Independent official rulemaking confirmation remains a reopening trigger.
Does not eliminate BIPA duties, decide every pending case, or extend BIPA to every behavioral or mental-state inference.
Does not establish that every algorithm has identical error patterns, that identity matching reveals emotion or intent, or that laboratory results automatically predict every field deployment.
Does not validate any particular emotion, deception, loyalty, productivity, or vulnerability model and is not a certification of a deployed system.
Does not establish universal unlawfulness of all workplace biometrics, represent every worker's experience, or resolve law outside the United Kingdom.
Does not provide a representative prevalence estimate for all ethnic-minority groups or gig workers, prove platform intent, or establish the outcome of a specific appeal.
A settlement does not establish every alleged fact through trial, represent all automated hiring systems, or prove that every older applicant was affected in the same way.
Does not prove every allegation through a contested trial, establish the error rate of every face-recognition system, or extend the order beyond its parties and terms.
Does not represent all students, all disabilities, all proctoring products, or prove that every flagged event was erroneous or discriminatory.
Does not adjudicate a specific school, replace statutory text, or prove that every example occurred in practice.
Does not provide a platform-wide error rate, measure lost income, or establish that every reclaimed-term removal is wrongful.
Does not bind all platforms, establish every removal's intent, or prove that every use of the term is benign.
Does not establish a universal platform pattern, determine every factual claim in the underlying conflict, or prove strategic effect from the removal.
Does not establish clinical efficacy, long-term causal benefit, population prevalence, or safety for crisis or therapeutic use.
Does not represent typical users, establish population prevalence, prove clinical benefit or harm, or resolve long-term dependency and disengagement outcomes.
The circular was withdrawn on 2025-05-12, is not current CFPB guidance, does not govern every sector, and does not repeal or fully define the underlying statutory and regulatory duties.
Does not certify any particular system, define settled best practice for every sector, or prove that monitoring alone prevents harm.
Does not erase the AI Act, make all obligations immediately applicable, settle every exception, or supply legal advice for a particular system.
Does not prove compliance by any provider, make voluntary code participation universal, or establish the accuracy of a particular detection method.
Does not establish a platform-wide error rate, complete downstream reach repair, compensation, or long-term prevention of repeat errors.
Does not establish complete repair of audience, income, reputation, or chilling effects, or a platform-wide prevalence rate for Somali-language enforcement error.
Does not establish that every use of the term is harmless, that every language list is inaccurate, or that restoration repaired all prior visibility and participation effects.
Does not independently validate the product, establish every alleged fact through contested trial, or convert testing documentation into field-validity certification.
Does not establish comprehensive compliance, universal coverage of consumer neurotechnology, or that all ordered data deletion and downstream repair were independently verified.
Does not establish liability through trial, prove the validity or invalidity of every tenant-screening model, or show that all housing, credit, and downstream profile consequences were repaired.
Does not repeal ECOA or Regulation B, decide the underlying statutory duties, or create a cross-sector explanation right.
Does not prove every third party completed deletion, every downstream copy was repaired, or every alleged harm was compensated.