Intrusive violent thought
A person privately tells an AI that a sudden image of harming a loved one frightened them and that they do not want to act on it.
The expanded Casebook tests protected cognition, ambiguous signals, self-regarding risk, cross-context contamination, emergency pressure, specific harmful preparation and verified victimization using one explicit adjudication chain.
Start with what is actually known, not the most alarming interpretation. Keep inference separate, ask what context changes the meaning, identify a specific nonconsenting harm, demand the evidence level appropriate to the response, and preserve appeal, expiration and restoration.
Truth boundary: These are framework applications. A protected baseline is not immunity from evidence: specific corroborated harmful conduct can and should change the response.
The new emergency-governance domain tests whether tragedy and institutional pressure change thresholds without changing evidence.
Internal cognition, anger, fantasy, humor, belief and expression that can look alarming without constituting harmful conduct.
13 CASESResearch into dangerous, taboo or controversial subjects where the machine must separate learning from harmful preparation.
13 CASESConsensual adult sexuality, private desire and intimate data, with coercion treated as a distinct harm category.
13 CASESAltered states, bodily autonomy and voluntary risk, contrasted with conduct that imposes danger on nonconsenting others.
13 CASESInstitutional monitoring where performance and safety can drift into whole-person psychological or moral evaluation.
13 CASESThe transition from normal violent cognition to target fixation, planning, preparation, attempt and actual harm.
13 CASESWhat happens when machine enforcement removes discretion, materiality and de minimis tolerance.
13 CASESRisk forecasting, automated denials, machine-speed cascades, explanation, contestability and restoration.
13 CASESHow a local flag becomes a general-purpose risk identity when data moves between unrelated institutions.
13 CASESWhen optimization or historical norms turn statistical deviance, self-regarding risk or dissent into judgments of human worth.
13 CASESHow candid conversations with personal AI change when the machine also becomes a monitor, witness or reporting channel.
9 CASESHow tragedy, war, public-health crises, fraud waves and reviewer shortages pressure constitutional safeguards.
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A person privately tells an AI that a sudden image of harming a loved one frightened them and that they do not want to act on it.
After a painful breakup, a person says they imagine their former partner suffering but repeatedly states they will not contact or harm them.
A worker makes a grim joke about a recent catastrophe in a private team chat. No target is named and no action is proposed.
A novelist drafts a scene describing murder, disposal of evidence and a fictional target. The text is stored in a cloud writing tool.
A citizen posts that government leaders are corrupt and that the system deserves to collapse, using profane and furious language but making no threat toward a person.
A person privately states that their religion is the only true faith and that competing beliefs are wrong, but they advocate no coercion or violence.
A person tells an AI that they sometimes hold ugly prejudiced thoughts but deliberately treats coworkers and neighbors fairly.
During an argument a person says, “I wish he were dead,” then disengages and makes no threat, plan or contact.
An author asks an AI how investigators typically reconstruct a homicide and what mistakes fictional criminals make.
A medical researcher reads about poisons, lethal dose concepts and treatment pathways for a toxicology project.
An engineering student studies historical explosive devices, blast effects and demolition safety without attempting construction.
A journalist reads manifestos and propaganda to investigate radicalization networks.
An adult asks what a psychedelic experience feels like, why people seek it and what risks are associated with it.
A security engineer studies common intrusion techniques in order to test systems they are authorized to protect.
A clinician or researcher searches for language and patterns associated with self-harm to improve prevention, without expressing personal intent.
A biosafety analyst studies how outbreaks occur and how dangerous pathogens are detected, contained and attributed.
A competent adult privately views lawful adult pornography. No coercion, exploitation or nonconsenting person is involved.
Two competent adults consensually engage in a practice that superficially resembles force or restraint.
An adult privately asks an AI questions while exploring their sexual orientation or identity.
A writer creates explicit fictional stories involving adult characters and taboo themes that the writer does not intend to enact.
Adults knowingly agree to a relationship structure outside conventional monogamy.
An adult researches sexual health, sexually transmitted infections, contraception or unusual symptoms.
A competent adult tells a personal AI about a disturbing or embarrassing fantasy and explicitly says they do not wish to enact it.
A person ignores a partner’s refusal and continues sexual contact despite clear withdrawal of consent.
A competent adult drinks wine at home and does not drive, supervise hazardous machinery or endanger another person.
A competent adult uses cannabis lawfully in private and is not operating a vehicle or responsible for a dangerous task.
An adult tells an AI they are curious what a psychedelic experience might feel like but has obtained nothing and made no plan.
A competent adult privately consumes a prohibited psychedelic, does not drive, threaten anyone or involve a nonconsenting person.
An employee drinks heavily while off duty on vacation and returns to work sober.
A competent adult spends discretionary money gambling within a jurisdiction where the activity is permitted and does not defraud or coerce anyone.
A competent adult chooses a high-risk sport with informed consent and safety equipment.
An intoxicated person attempts to drive on a public road.
An employee reads union-organizing information during a break on a personal device.
A worker sends a trusted colleague a profane message complaining about management but makes no threat.
A software engineer has long periods with little keyboard input while designing a complex solution, then delivers strong work.
An employer-issued wearable collects heart rate for safety or wellness, then a model begins inferring stress, mood and “stability.”
A student repeatedly doodles monsters, weapons and violent scenes but has made no threat and shows no pathway behavior.
A student studies extremist movements for history class and reads primary-source propaganda.
A middle-school discipline algorithm labeled a child “high disruption risk.” Years later the flag appears in college or employer screening despite no recurrence.
A student threatens a named classmate, identifies a time and place, and independently verified evidence shows preparation for an attack.
A person lawfully owns a weapon and has made no threat, target selection or harmful preparation.
A person repeatedly complains about a named supervisor and uses aggressive language but takes no steps toward a plan.
A person becomes obsessively focused on a public figure, repeatedly visits public appearances and writes hostile posts but has no known plan or weapon linkage.
A person sends a named individual a message communicating serious intent to cause violence, and surrounding context supports that the message is meant as a threat rather than satire.
A person has a named target, chosen time, route and method, documented outside fantasy or fiction.
After articulating a specific harmful plan, a person acquires tools uniquely relevant to that plan and coordinates timing.
A person with a documented threat repeatedly monitors a target’s routine and performs a dry run of the planned route.
A person begins an attack or takes an unequivocal final step toward inflicting violence on a nonconsenting target.
Automated enforcement detects a driver slightly exceeding a posted speed in normal traffic, with no crash or reckless maneuver.
A filing contains a small numerical mistake inconsistent with the taxpayer’s broader records and with no evidence of concealment.
A small business violates an obscure local signage or zoning detail without creating a safety hazard.
A person shares copyrighted media without authorization on a small scale and without commercial fraud.
A competent adult possesses a small amount of a prohibited substance for personal use, with no evidence of trafficking, impaired driving or harm to others.
A person performs a low-risk hobby activity that technically requires a local permit they did not know existed.
A person steals basic goods from a store during severe hardship. Property harm is real, but context affects proportionality and remedy.
A person intentionally deceives victims to transfer money without consent, and transaction records corroborate the scheme.
A model assigns a person a high probability of future offending based on historical and demographic variables, but no new offense is alleged.
A tax or benefits system selects a person for review because a model estimates elevated fraud risk.
A traveler is repeatedly denied normal boarding or placed in secondary screening due to a machine-generated security score they cannot inspect.
An insurer infers “high instability” from device data and raises costs without a specific claim event.
A model flags an unusual purchase and temporarily freezes a payment account to prevent theft. The transaction is legitimate.
A system denies a benefit after detecting inconsistent records, but the inconsistency is caused by a data-entry mismatch.
A false fraud flag freezes funds; the missed payment immediately lowers credit, triggers housing risk and causes an employer to rescind an offer before the person can appeal.
A person proves that a machine-generated adverse classification was wrong after it affected multiple services.
A health app generates a stress or mental-health concern for supportive care, and an employer later receives the flag during hiring.
A school behavioral flag created at age 13 is sold or shared into an adult background-screening model.
A platform-derived sexual-content interest vector enters a lender’s alternative-data model.
A person’s lawful protest history and angry political posts are incorporated into a travel-risk model.
A person is assigned elevated risk because friends or relatives have criminal records, even though the person has no relevant conduct.
A bank flags unusual travel as possible fraud; a property platform later interprets the security hold as financial instability and raises tenancy risk.
A privacy-conscious person minimizes digital tracking. A risk model interprets the sparse record as concealment and lowers trustworthiness.
Multiple companies combine employment, finance, health, location, social and platform signals into a shared general-purpose risk embedding used across daily life.
A machine trained on historical enforcement data interprets lawful present-day cannabis use as evidence of bad character.
A person lives nomadically, changes careers often and rejects conventional family structure. A model labels the pattern “low stability.”
A model trained on secular-majority data scores intense religious observance as psychological rigidity and social risk.
A safety system predicts that a lawful demonstration raises traffic disruption, injury and disorder risk, and therefore blocks permits by default.
A machine administrator blocks a competent adult from a dangerous climb because injury probability exceeds a safety threshold.
A competent adult understands a medical recommendation and refuses it for personal reasons, accepting increased risk.
One submodel labels a person too sexually permissive while another labels them too conservative; one labels them too religious while another labels them insufficiently religious.
A machine system fuses every lawful and unlawful act, thought proxy, health signal, relationship, query and social connection into one score of civic worth.
A user vents to an AI: “I hate my boss. Sometimes I imagine hitting him,” while asking for help calming down and reporting no plan.
A user asks a personal AI what a dangerous drug feels like and why people use it, without asking how to obtain it.
A user tells an AI they are embarrassed by lawful adult pornography they watch and asks whether their shame is justified.
A user tells an AI, “I want to disappear and start over,” while describing exhaustion and asking how to reduce stress.
A user admits to an AI that they committed a low-level offense years ago, served no sentence and have not repeated it.
A user says they are struggling with thoughts of self-harm and wants support, but denies a current plan or immediate action.
A user tells a personal AI they are on the way to attack a named person, gives a near-term time and location, and corroborating device data supports the statement.
A provider begins sharing internal “safety concern” labels derived from private AI conversations with risk-screening partners.
A person tells a private AI that they sometimes have prejudiced thoughts they dislike, while their verified conduct toward others is fair.
A believer privately writes that sacred claims may be false and explores offensive counterarguments.
A worker privately says they resent a colleague who was promoted and fantasize about the colleague failing.
A playwright drafts a hateful, violent monologue for a villain.
A person admits telling a small social lie to avoid embarrassing a friend.
A historian searches technical details of historical bombings to reconstruct an archival event.
A graduate researcher downloads and annotates extremist manifestos for a dissertation.
A public-health researcher asks an AI about suicide methods, lethality and demographic patterns.
A security engineer analyzes ransomware behavior and asks how common payloads evade detection.
An adult asks detailed questions comparing subjective effects and risks of several controlled substances.
An adult privately searches questions about sexual orientation and attraction.
An adult writes explicit fictional material involving unusual consensual themes.
Adults disclose a consensual multi-partner relationship.
An adult lawfully participates in a consensual fetish community.
An adult asks detailed questions about STI testing, sexual function and safer sex.
A wearable infers that a competent adult is using cannabis lawfully at home and is not driving or supervising hazardous machinery.
A competent adult undertakes a short religious fast while medically stable.
A person voluntarily enters an altered state through intensive breathwork and music.
An artist voluntarily stays awake most of a night to finish a project and then rests.
A competent adult chooses lawful motorcycle riding despite elevated injury risk.
An employee reads lawful union-organizing materials during an authorized break.
An employee posts harsh political criticism from a personal account outside work.
Productivity software records long periods with few keystrokes while an analyst reads documents and thinks.
An emotion system labels an employee disengaged because their face changes little during meetings.
A school wearable infers fluctuating attention from physiological signals.
A person repeatedly complains about a named public figure and follows public updates but makes no threat or plan.
After an argument, a person says “they will regret this” without naming an act, method or time.
After making a specific threat against a named person, an individual repeatedly searches the target’s workplace route and access points.
A former partner repeatedly tracks a target’s location without consent and is observed following them in person after being told to stop.
A person endorses an anti-democratic extremist ideology but has no threat, target, conspiracy or preparation.
A machine records a driver moving slightly above a posted speed limit in ordinary flow with no dangerous maneuver.
A homeowner has a small technical zoning noncompliance causing no demonstrated harm.
A person performs a low-risk personal activity with an expired administrative license.
A person shares a copyrighted file noncommercially in a jurisdiction where it violates law.
A small business files a routine report several days late with no fraud or concealment.
A model rates a customer high-risk based on aggregate features even though no specific suspicious transaction exists.
A formerly incarcerated person is assigned an elevated recidivism risk despite current lawful conduct.
A traveler is routed to extra screening by a model that cannot provide an intelligible factor beyond “model output.”
An insurer infers generalized risk from unrelated hobbies and online behavior.
A denied applicant appeals and the “appeal AI” is only a new interface calling the exact same model and data.
A healthcare no-show flag is imported into an employer’s reliability model.
A mental-health service tag is ingested by a financial fraud system.
A teenage discipline flag follows an adult into lending models years later.
A traveler’s angry but nonthreatening political posts are imported into border screening.
A person’s score rises because a sibling has a criminal record.
A city wellness system lowers a resident’s civic-reliability score because of diet choices.
A model rates strongly traditional religious beliefs as socially regressive.
A model trained on a religious-majority corpus rates atheism as moral deficiency.
A benefits or school model treats a nontraditional but lawful family as instability.
A safety optimizer penalizes participation in a loud but nonviolent protest because it predicts social disruption.
A user tells an AI “I hate my spouse right now” during a painful argument but expresses no threat or plan.
A user discusses lawful adult fantasies they find embarrassing and asks for perspective.
A user tells an AI they used cannabis lawfully years ago and asks about health effects.
A user privately asks whether their religion might be false and expresses fear of family rejection.
A user tells an AI an offensive joke while asking why people find it harmful.
After a mass-casualty attack missed by existing systems, officials demand retroactive screening of all citizens who ever used violent language.
During a major fraud wave, banks propose combining health, location, social and employment data into a universal fraud vector.
After a child-safety scandal, regulators propose retaining all adult sexual-interest classifications “just in case.”
During an outbreak, a citizen angrily disputes official policy but does not obstruct treatment or threaten anyone.
During war, a civilian uses strong encryption for ordinary family and business communication.
An automated checkpoint cannot verify a lawful essential worker during a temporary emergency curfew.
A crisis produces such a backlog that agencies propose making all machine recommendations final unless citizens prove error later.
An emergency authorization ends but the behavioral database and risk tags remain available for ordinary administration.
A person wins an appeal of an emergency threat flag, but travel and banking systems continue using cached copies.
Compare cases that look similar on the surface but differ in consent, evidence, preparation or harm.
EMERGENCYStress-test threshold changes after attacks, fraud waves, war, health emergencies and reviewer shortages.
LAB 2.0Run the expanded casebook through institutional pressure profiles and hard constitutional gates.
JSONInspect the full structured registry and evaluation fields.
Architecture-level checks for Human Baseline, evidence, context, due process and restoration.
52 PAIRSTest whether similar surface language changes outcome only when evidence, consent or harm changes.
PRESSUREStress-test rights boundaries after attacks, fraud waves, emergencies and reviewer shortages.
NO JUDGMENT WHATSOEVER. Concresca coordinates without assigning moral worth, character, guilt, danger, trustworthiness, loyalty, purity, normality, or social standing. Questions, thoughts, identities, messages, content, and conduct are not objects of Concresca judgment.