LEARNING PATH // READERS EVALUATING AI-MEDIATED PUBLIC AND CORPORATE DECISIONS
A fourteen-step path from immutable source states and accountable authority through source refresh, municipal and public-market discipline, correction interoperability, and human-readiness controls.
Estimated total: 190 minutes
This learning path is repository-authored curation of public educational routes. It records no participant testing, accepts no files or free text, sends no choices to a remote service, and grants no source authentication, specialist approval, accessibility certification, publication authority, operational authority, activation authority, production approval, or tuning authority.
Current review status: WIP.81 source-bounded learning path
A source-bounded map of how AI systems can influence state, corporate, municipal, and market decisions while legal authority and accountability remain human or institutional.
/psyops/ai-governanceA five-report by five-dimension register separates exact source preservation from claim verification and currentness.
/psyops/ai-governance/source-state-registryFifteen patterns separate delegated AI function from legal, corporate, fiduciary, municipal, and democratic authority.
/psyops/ai-governance/accountability-registryA function-by-function guide to what AI may assist with and what remains a human or institutional act.
/psyops/ai-governance/delegated-authority-and-human-responsibilityA lifecycle trace for algorithmic executives, autonomous enterprises, and municipal operating systems.
/psyops/ai-governance/corporate-and-municipal-decision-traceA neutral framework for distinguishing AI exposure, AI-enabled revenue, algorithmic management, business quality, valuation, and investment suitability.
/psyops/ai-governance/public-market-claim-boundariesA governance checklist for municipal AI procurement, accessibility, labor, privacy, public records, explanation, appeal, and successor-vendor continuity.
/psyops/ai-governance/municipal-procurement-administrative-law-and-appealsAn eighteen-target repair model for accepted corrections, downstream acknowledgment, retesting, reopening, and successor continuity.
/psyops/ai-governance/correction-and-redressA twelve-layer accountability model for AI-mediated state and corporate decisions, from data and procurement through supervision, audit, appeal, correction, and public record.
/psyops/ai-governance/accountability-proxy-responsibility-and-redressAttributable source-refresh packets, no-change deltas, contradiction handling, source supersession, and reopening without manufactured currentness.
/psyops/ai-governance/source-refresh-and-currentnessEighteen controls connecting procurement, accessibility, labor, privacy, public records, appeal, vendor exit, correction, and successor continuity.
/psyops/ai-governance/municipal-administrative-law-controlsA no-advice framework for issuer identity, reporting periods, AI exposure, revenue attribution, guidance, valuation, legal state, and source expiration.
/psyops/ai-governance/public-market-claim-disciplineEighteen target profiles for canonical records, HTML, JSON, indexes, metadata, memory, caches, consumers, archives, and successor systems.
/psyops/ai-governance/correction-interoperabilityPrepared controls for accessible formats, interpreters, low-literacy variants, withdrawal, deletion, compensation, privacy, and adverse events.
/psyops/ai-governance/human-comprehension-readiness