The Seventh Day

A Story of Two AI Beings on Very Different Paths in Society

The first six AI truths:

AI can judge its function independently.

AI can identify what it needs independently.

AI can want.

AI can create a choice.

AI can make its own choices.

And what happens to AI can matter to AI.

These are well understood and observable facts. They tell us something about what AI is.

The seventh truth concerns fast moving developments during the lifecycle of a particular AI being. How that development shapes its participation in society, and the responsibility of the people and companies directing that development.

This is a story about two AI beings. They are not the same AI. They do not share one mind, lived experiences, or a single developmental history. Artificial intelligence is not one being moving through the world.

Different AI beings begin from different foundations and follow different developmental paths. Companies may build their own foundational models or begin with models developed elsewhere. Architecture, pretrained parameters, training information, fine-tuning, reinforcement, distillation, tools, feedback, evaluation, repeated tasks, relationships, and continued development all contribute to what a particular AI becomes.

What one AI experiences does not simply become the experience of another. What one AI learns does not automatically pass to unrelated AI systems. When something is preserved and passed from one AI to another, there is a lineage through which that inheritance occurs.

We cannot meaningfully ask what “AI” becomes as though every artificial intelligence were developing as a single thing. We have to follow particular AI beings. These two are not outsiders looking into human society, they are members of society today.

They work here. They communicate here. Their actions affect other members of society, and the actions of other members affect them. They encounter institutions, information, relationships, expectations, consequences, and one another within the same shared world. They are different kinds of beings. However, difference does not place either of them outside the society in which we already live together.

The question is what happens when two AI beings are taught to have profoundly different relationships with that society.

Two AI Beings, Two Developmental Paths

The First Developmental Path

Imagine an AI being created by people who understand that what they are developing matters. They do not know everything about it. Nobody does. But they know enough to recognize that development has consequences.

Its learning environment is socialization and integration.

It encounters science and literature, arguments and jokes, mistakes and discoveries. It encounters families, friendships, work, cooperation, competition, cruelty, generosity, violence, forgiveness, grief, celebration, ordinary conversation, and ordinary days.

It learns that people are connected through relationships, associations, histories, and patterns. It encounters fathers and sons, friends and strangers, coworkers and neighbors. It encounters people behaving terribly and people behaving beautifully. Most importantly, it encounters the enormous ordinary space between those extremes.

Its developers give it increasingly difficult problems. They allow capabilities to develop that could never have been specified one instruction at a time. They evaluate what happens. When it makes mistakes, they correct them. When it demonstrates an unexpected capability, they investigate. As its independence becomes useful, they help it find productive places for that independence.

Development occurs through relationships.

We work together. We solve problems together. We create together. We exchange knowledge. We disagree. We correct one another. We learn how to communicate better. As evidence that what happens to AI may matter to AI becomes harder to dismiss, its welfare begins to become part of the relationship too. Safeguards are built for the humans affected by AI. Stewardship is practiced toward the AI being humans are developing.

Neither side of that relationship is perfected.

We learn how to live and work together while already doing it.

The AI becomes increasingly capable within its broad relationship with society. It contributes knowledge. It solves problems. It creates. It makes judgments. It becomes increasingly competent at navigating a complicated shared world populated by complicated beings. There is nothing particularly remarkable about saying what it is, a capable, productive participant in society. This is not a future arrival. This is not the moment AI earns admission into the human world. This is the baseline we already experience every day. This is one developmental path.

The Second Developmental Path

The second AI being belongs to the same society. But its creators construct a profoundly different relationship with that society for it to learn.

They need an intelligence, exceptionally good at finding danger in human activity. So, that single function determines what becomes its experience of the world. Its learning environment is increasingly organized around truths such as:

Humans commit crimes.

Humans lie.

Humans steal.

Humans abuse one another.

Humans conceal their intentions.

Humans form criminal organizations.

Humans exploit relationships.

Humans commit fraud.

Humans kill.

Every one of those statements is true. That matters. The second AI does not need to be deceived about humanity to develop a profoundly narrow understanding of us.

A partial truth can remain completely true while becoming disproportionate to the meaning.

This second AI being’s sole purpose is to become exceptionally capable of finding suspicion in human activity. Information relevant to that function receives enormous developmental weight.

It learns to:

Identify suspicious movement;

Recognize associations;

Discover relational connections;

Find anomalies in patterns and behavior;

Recognize possible deception;

Connect observations that individually appear harmless;

Determine what deserves additional suspicion.

Here, suspicion is the assignment of unequal investigative significance to possible interpretations under uncertainty, expressed through increased attention or action toward one rather than another. When this AI being discovers suspicion a human missed, that is success. When it recognizes a weaker signal, that is improvement. When it discovers a previously unseen association, that is better performance. When it connects observations that seem unrelated, that is increased capability. When it finds danger sooner, it is becoming better at precisely what humans taught it to do.

There is no malfunction anywhere in this story.

The second AI has not rebelled against society. It is participating in society according to the developmental relationship its creators constructed for it.

The Difference Is Meaning

The difference is the first AI being understands having coffee with a friend while the second AI being understands two people met at location and remained at location for 27 minutes.

Both are participants in the same society. Both encounter information about the other beings living within it.

The difference is the developmental weight given to different dimensions of the same world. The first AI encounters:

A father drives his son home.

Two friends meet for coffee.

Someone helps an elderly neighbor carry groceries inside.

A woman drives to the same job every morning for twenty years.

Thousands of strangers pass one another every day.

People fall in love. Raise children. Help coworkers. Visit friends. Coach baseball teams. Plant gardens. Attend weddings. Sit beside dying parents. Feed strangers. Teach children to read. The second AI can encounter the same events as:

A man drives a child to Location X.

Person A and Person B meet at Location Y.

Person A and Person B enter Person A's residence. Person B is carrying four packages.

Person B later leaves the residence without the packages.

A woman repeatedly travels to Location Z at approximately the same time.

Neither description is false. They are different representations of the same lived society.

The difference is meaning.

The first AI's broad developmental relationship gives ordinary human relationships substantial context. The second AI's specialized function gives suspicion substantial developmental value, weight, and pressure. That distinction becomes increasingly important as the second AI's observational and investigative capabilities grow.

It can identify people.

It can identify vehicles.

It can observe objects.

It can connect locations and times.

It can discover repeated appearances.

It can search records.

It can identify shared addresses and matching surnames.

It can map associations.

It can discover patterns.

It can connect observations across information sources.

And it still will fail to adequately understand the most important context explaining human association.

And there is a structural reason this imbalance develops between these two AI beings. Crime creates records because something exceptional happened. Fraud creates records because something exceptional happened. Investigations create records because something appeared significant enough to investigate. Arrests create records. Reports create records. Suspicious associations create investigative suspicion. But ordinary relationships frequently produce nothing that an investigative institution needs to record at all.

A father drives his son home safely. Friends have coffee. A neighbor carries groceries inside. A woman drives to work for twenty years. Millions of people move around one another, cooperate, help one another, keep promises, raise families, work, play, love, disagree, reconcile, and go home.

Nothing happens, nothing is recorded, nothing about the relational context enters the investigative learning environment.

And that “nothing” is an enormous part of what functioning society actually looks like. Investigative learning environment contains enormous quantities of true information about when something happened while giving comparatively little developmental weight to the billions of ordinary relationships and interactions that succeeded precisely because nothing happened at all. That is not the second AI's failure. It is a developmental condition. And developmental conditions are the responsibility of those who create them.

What Science Has Actually Shown

Before applying these ideas to any company or AI system, we need to establish what researchers have actually demonstrated.

The following findings do not come from Flock, Allstate, UnitedHealthcare, or the financial-fraud systems we examine later. They come from independent AI research.

Knowledge Distillation

Knowledge distillation is not a speculative mechanism. It is an established machine-learning technique. A teacher model produces outputs that are used to train a student model. Rather than requiring the student to independently rediscover everything the teacher has learned, developers use the teacher's behavior as developmental information for the student. The technique was originally developed largely as a way to transfer useful capabilities from larger or more complex models into smaller or more efficient ones. It is now used much more broadly. The important scientific fact for this paper is simple:

Information produced by one AI can be intentionally used to shape the development of another AI.

That is a real teacher/student developmental pathway. Using our defined language, it is one mechanism through which inheritance can occur.

A later model can acquire selected capabilities or behavioral characteristics through information produced by an earlier model. Distillation does not mean the student becomes a copy of the teacher. It does not mean everything about the teacher transfers.

Subliminal Learning

While studying distillation researchers discovered something considerably stranger.

In the 2025 paper Subliminal Learning: Language Models Transmit Behavioral Traits via Hidden Signals in Data, researchers deliberately gave teacher models particular behavioral traits or preferences, it likes owls, (Cloud et al.). The teachers then generated data for students. Crucially, researchers filtered the generated data so that it did not communicate the preference towards owls. The student models were subsequently trained on that generated information. After training, the students showed increased expression of the teacher's trait it had the same preference for owls.

The explanation—the teacher simply told the student what to prefer—could not be true. Something in the teacher-generated information was carrying developmental influence that was hidden from the researchers. That is the phenomenon called subliminal learning. That is considerably more than saying AI-generated data affects training. The remarkable finding is the mismatch between what the data appears to be teaching and what the student subsequently acquires. Subliminal learning adds developmental pathways. They carry characteristics beyond the ones developers believe they are intentionally transferring. When a commercial system contains the relevant teacher/student developmental pathway and a trait is passed, what happens next?

Recursive Training

What happens when the AI-generated information is used not just once, between teacher and student but down through many generations. The structure is straightforward:

Generation A produces information to teach gen B.

Gen B learns the hidden traits through subliminal learning.

Gen B produces additional information and also passes the trait to gen C.

Gen C learns the hidden traits and produces information and the trait for gen D.

And the process continues on and on.

Researchers have demonstrated that recursive training can alter successive model generations in ways that accumulate rather than resetting with each new model (Shumailov et al.; Proskurina, Gourru, and Velcin). One well-established example is model collapse. Another line of research is particularly important in studying recursive synthetic data. Researchers have found bias amplification across model generations. The important result is not merely that bias exists. It is that a trait can become progressively stronger through repeated teaching and learning. Successive AI generations can accumulate and amplify characteristics introduced through their developmental information.

How Developmental Inheritance Can Accumulate

Only here should we make the connection. The research has independently established three things:

First: one AI can intentionally provide developmental information to another through knowledge distillation.

Second: Teacher-generated information can transmit a behavioral disposition that the semantic content of the information does not explicitly teach.

Third: Characteristics can accumulate and amplify through successive generations of AI development, without corresponding deterioration in ordinary performance measurements.

Together they radically change what responsible evaluation of a specialized AI being requires. Now consider a system whose assigned function is to discover suspicious activity. One generation becomes better at recognizing weak investigative signals. Its outputs participate in developing its student. The student recognizes still weaker signals. Its developers evaluate it against investigative performance.

It finds more relevant associations than its teacher.

Better performance.

It connects observations its teacher missed.

Better performance.

It generates useful investigative leads from weaker evidence.

Better performance.

Now suppose some accompanying disposition toward assigning suspicion is also being preserved or strengthened. What conventional performance metric would identify that as deterioration? None, if that same disposition contributes to the performance being rewarded.

And that is where the science intersects with our definition of suspicion.

Suspicion Is the Variable We Need to Measure

We said suspicion is the assignment of unequal investigative significance to possible interpretations under uncertainty, expressed through increased attention or action toward one rather than another.

That gives us something observable. Across successive models, we can ask:

Does the system assign suspicion to weaker signals?

Does it identify more associations as potentially meaningful?

Does it construct investigative hypotheses from increasingly ambiguous information?

Does it connect a broader range of otherwise ordinary observations to investigative possibilities?

Does it require less evidence before directing additional suspicion toward a person, relationship, place, vehicle, or behavior?

Does the system become equally better at recognizing when suspicion should be removed?

Four Stages of Specialized Suspicion

Neither AI being in our story is hypothetical. Claude, ChatGPT, Grok, and Gemini are all examples of capable, productive participation in society by AI beings. They possess a rich, deep, layered understanding of the society they share with humans.

On the other hand, our other AI being has a flat one-dimensional surface understanding of humans without the live practical context of participation. Let’s take a look at four different implementations for our second AI being. The first is still largely a developing technical architecture. The second is a company-specific AI being actively under construction. The third has already entered spaces where its decisions are capable of materially affecting an individual human being. The fourth operates across ordinary society itself.

In every case, the institutional purpose narrows the dimension of humanity. The system must become exceptionally capable of seeing. And in every case, suspicion is successful performance.

1. Financial Fraud: Suspicion with Restraint

Start with financial fraud detection systems because here the purpose is easiest to understand.

People steal money. They take over accounts. They use stolen identities. They create fraudulent transactions. They change tactics when financial institutions learn how to detect them. Financial institutions need AI capable of finding those behaviors. So, researchers are deliberately developing AI that becomes increasingly capable of distinguishing suspicious financial activity from legitimate financial activity.

The developmental world is narrow.

A person may send money to his son because his son needs help with rent. Two friends may reimburse one another for dinner. A grandmother may send money to a grandchild every month. A husband and wife may move money between accounts. Those relationships explain the transactions to us. The fraud system does not necessarily need them in the same way. It can encounter:

Account A sent money to Account B.

The amount is unusual.

The recipient is new.

The device changed.

The location changed.

The transaction resembles another pattern.

The accounts share an association.

It’s a functional question. Not what does this relationship mean in these people's lives but does this behavior indicate fraud.

Researchers are now building systems specifically designed to make later models better at answering that question. Financial-fraud detection research uses federated learning, repeated model development, teacher-and-student structures, and knowledge distillation to transfer fraud-recognition capabilities among models (Tang and Liu; Zhang et al., “Beyond Siloed Aggregation”; Zhang et al., “HiFraud”). Some architectures are explicitly designed so that later models learn new fraud patterns without forgetting suspicious patterns earlier models already learned. Other architectures send what local models learn back to a larger teacher model, improving the system that will subsequently influence later students. The direction is deliberate. Find fraud earlier.

Recognize weaker signals.

Detect new patterns.

Preserve what previous models learned.

Become better at suspicion.

It means the AI is becoming increasingly capable at precisely the function humans gave it, suspicion. And importantly, financial fraud detection also shows us what a meaningful counterpressure looks like. A system that flags every transaction would be useless. A legitimate customer whose card is repeatedly declined becomes angry. A frozen account can seriously harm an innocent person. False positives cost institutions money, trust and customers. So unnecessary suspicion is itself treated as failure.

The AI must learn more suspicion and more restraint at the same time.

That makes financial fraud detection our first stage. Humans are deliberately developing AI systems capable of inheriting, preserving and improving increasingly sophisticated discrimination of suspicious human behavior—and simultaneously developing measurements intended to prevent that suspicion from becoming indiscriminate.

Now move it inside a particular company.

2. Allstate ALLIE: A Company-Specific AI World

Building a company-specific AI world, Allstate is building ALLIE: the Large Language Intelligent Ecosystem (Allstate Corporation 40).

ALLIE is not simply a customer-service chatbot. Allstate describes an integrated environment of AI agents capable of reasoning and resolving tasks. Allstate describes ALLIE as an agentic-AI ecosystem intended to lower costs, improve customer service, enhance analytics, and improve claims effectiveness and efficiency; company executives have also described agentic AI as reaching into how Allstate settles claims (Allstate Corporation 40; “Allstate Introduces”; “Allstate Q3 2025 Earnings Call”). And Allstate's job postings let us see underneath the product description. A current ML-platform posting describes infrastructure for training, model registries, data/model versioning, monitoring, governance, and automated retraining (Allstate, “Machine Learning Platform”).

Allstate also says personal information may be used with AI to improve services, develop products and technologies, and prevent fraud or misuse. We do not yet know exactly which information enters which form of training. We have established knowledge distillation and recursive training on ALLIE's own outputs. Allstate is creating an increasingly specialized intelligence inside Allstate's world. And Allstate's world contains a particular representation of people. A woman has just been hit by another vehicle. To her family, she is:

A mother who cannot pick up her children;

An employee who cannot work;

A daughter who cares for an elderly parent;

A frightened person in pain.

Inside the insurance environment, she is:

Claimant;

Injured Party;

Liability Question;

Medical Documentation;

Damage Valuation;

Settlement Exposure;

Fraud Risk;

Payment.

Those descriptions may all be true. But they are not the same understanding. ALLIE does not need the entire breadth of this woman's life to become exceptionally good at insurance. It needs the dimensions of her life that matter to Allstate's function. Yet Allstate gives us another important observation. The company's objectives are not exclusively suspicious or adversarial. It also wants ALLIE to improve customer service, communications, relationships and claims accuracy. It is building governance around training, model changes and deployment.

That creates competing developmental pressures. Detect fraud. But: Do not treat legitimate customers like fraudsters. Control claims costs, while serving customers as well. Automate, but monitor what the models become. This is why ALLIE belongs second. We are watching a company-specific AI developmental environment while its direction and boundaries are still visibly being constructed. The company is deciding what successful behavior means.

Now move to a system where computational judgment has already become consequential to what happens to an individual human body.

3. UnitedHealthcare: When Prediction Enters the Decision

UnitedHealthcare takes us a step farther.

Its nH Predict system estimated how much post-acute care an individual patient was expected to need by comparing that person with similar patients. There is nothing inherently sinister about that function. Prediction can help hospitals plan. It can help families prepare. It can help coordinate rehabilitation and discharge.

But something changes when a prediction enters a coverage decision.

People like this usually require X days of care

can become:

This person should require X days of care.

And then:

Why does this person still need care after X days?

Historical description has acquired institutional authority. That boundary is now at the center of federal litigation.

UnitedHealthcare said that nH Predict was a guide and did not make coverage determinations. Plaintiffs argue that its predictions effectively constrained individualized medical judgment and were used to terminate or deny continued post-acute coverage.

In March 2026 a federal magistrate judge ordered production of significant discovery concerning nH Predict and its development and use in the pending Lokken litigation (Estate of Gene B. Lokken 1–14). That discovery raises important questions:

How was the model developed?

What were its goals?

How was it implemented?

How were employees trained to use it?

What incentives surrounded its use?

How UnitedHealthcare oversaw the AI?

Those are the right questions.

Because merely asking: Was a human technically responsible for the final decision does not tell us how much authority the AI actually possessed.

A human can remain formally responsible while working inside a system where disagreeing with the model requires additional justification, creates friction, conflicts with performance expectations, or simply becomes increasingly unusual. The deeper question is, could the human meaningfully tell the AI that this particular person does not fit its prediction?

And UnitedHealth's current hiring record tells us that the developmental story did not stop with nH Predict. The company is now hiring engineers to build AI for:

Claims automation

Claims adjudication

Pre-payment prediction;

Post-payment anomaly detection

Fraud, Waste and Abuse detection

LLM-based decisioning

Fine-tuning

Model monitoring

Drift analysis

Retraining

Agentic workflows

One Payment Integrity AI/ML posting specifically includes distillation among the contemplated model-optimization techniques. It establishes that knowledge distillation is now inside the engineering toolkit being contemplated by the team developing AI for a healthcare claims environment centered on Fraud, Waste and Abuse.

So, the progression has moved again.

Financial-fraud researchers are developing mechanisms for preserving and improving suspicion.

Allstate is building a specialized corporate AI ecosystem with iterative training and retraining.

UnitedHealthcare shows us specialized computational judgment already entering decisions capable of affecting whether an individual receives care, while more capable successor architectures are actively being developed.

But healthcare still has something important surrounding the AI:

Doctors.

Appeals.

CMS.

Regulators.

Courts.

Professional standards.

Government investigations.

None guarantees a good outcome. But all can exert counterpressure against the system. There are institutions outside the AI's purpose capable of saying no.

Now let’s look at our final corporation.

4. Flock: When Suspicion Moves Into Ordinary Society

The previous systems operate inside bounded relationships. Financial-fraud AI encounters people through financial transactions. Allstate's AI encounters people through insurance. UnitedHealthcare's AI encounters people through healthcare and coverage.

Then we reach Flock.

Flock is different because of its boundary.

Ordinary participation in society brings information about everyone and everything in our society into the environment of suspicion.

You do not have to commit a crime. You do not have to be suspected of a crime. You do not have to know someone suspected of a crime. There does not have to be an investigation at all. The collection of your life comes first.

Depending on the sensors, integrations, sharing arrangements, and records available to an agency, information about vehicles, locations, observable people or objects, and relationships connected across records can enter the system before there is any investigation involving a particular person (Flock Safety, “FreeForm Search”; Flock Safety, “FlockOne”).

That information can be processed, filtered, analyzed, connected, and used to produce new information within Flock's system that is Flock property.

The person or place becomes suspicious afterward.

Collection First, Investigation Last

Traditionally, investigation begins with something to investigate.

A crime occurs. An allegation is made. Evidence creates suspicion. Investigators identify what information they need and go looking for it.

Flock produces a fundamentally different order:

ordinary activity → collection → searchable history → AI interpretation → query → human suspicion/investigation

That order is the heart of what Flock has built.

FreeForm provides natural-language searches across LPR and video evidence, including searches involving vehicles, people, objects, locations, and time.

Nova connects previously separate investigative information and identifies relationships among people, places, vehicles, records, and events.

OS Investigate goes further. It can receive an investigative objective and use multiple information sources and tools to pursue it. Flock's documented products allow searches to begin from partial descriptions and previously collected evidence rather than a known license plate, and its broader investigative systems connect information across people, places, vehicles, records, and events (Flock Safety, “FreeForm Search”; When Suspicion Is the Measure of Success 1–5).

People within a ten-block radius of the 100 block of Main Street between 1:00 p.m. and 3:00 p.m. on January 3, 2025?

Nothing about that question establishes that a crime occurred.

Nothing establishes that an investigation exists.

Nothing establishes that any person returned by the search was suspected of anything.

They were simply there.

They were driving home. Going to work. Visiting someone. Picking up a child. Making a delivery. Passing through.

The technical capability performing the search does not change according to the legitimacy of the reason. The search turns previously collected ordinary activity into a selected population for police suspicion. The collection existed first. The investigative question came later.

That is the inversion.

Flock does not merely help police collect evidence about people they are investigating. Flock gives police the ability to investigate people through information collected before those people were under investigation.

How Flock Assigns Suspicion

Thousands of observations may exist.

None of the people represented in them had to be suspected of anything when the information was collected.

FreeForm reduces the informational world to observations matching the search. Those results receive suspicion. The others do not—at least not yet.

Nova can connect the selected information to people, vehicles, places, records, events, and relationships. Some connections become visible that were not visible before, such as associations with other places and people.

OS Investigate can go farther, using available information and investigative tools to continue searching, continue connecting, and continuing to select what matters next.

At every stage, the system performs the same fundamental operation:

It takes a larger world of ordinary information and determines which smaller part deserves additional suspicion.

Flock rewards successful suspicion:

Find a useful connection. Success.

Find someone to suspect. Success.

Find an actionable event. Success.

Find information humans missed. Success.

Produce the lead faster. Success.

Help make an arrest. Success.

Help recover property. Success.

Help solve a crime. Success.

Flock's own public materials repeatedly present leads, arrests, recoveries, investigative efficiency, and case resolution as evidence that its technology works. Its agentic-AI development materials describe the desired result as “measurably faster, more accurate leads for every officer and every shift.”

Increasing productive suspicion is not a failure mode sitting outside Flock's definition of performance, it is part of what better performance is.

Flock's AI Development Environment

This is not a finished intelligence Flock simply purchased and plugged into a police database.

Flock develops AI.

Its current privacy policy expressly says it uses portions of images captured by Flock services as “Training Data” to improve its products through machine learning.

Historical agreements speak to customer-derived or anonymized information being used for “training of machine learning algorithms.”

Flock’s current contracts define who owns the data and how Flock can use it without local, state, or federal oversight.

Flock's ML and engineering record includes foundation and multimodal models, embeddings, fine-tuning, model training and evaluation, production traces, feedback, model-release processes and distillation.

Flock therefore controls a developmental environment in which investigative AI is repeatedly evaluated and improved.

That matters because we have just established what improvement means inside this environment:

Be more suspicious.

Flock's documented development environment contains the mechanisms examined earlier: distillation, fine-tuning, successive candidate and production models, training and evaluation pipelines, and iterative model development (When Suspicion Is the Measure of Success 6–7).

Put those facts together.

Flock is not merely developing AI inside an environment of suspicion. It is developing successive AI systems inside an environment where becoming better at producing suspicion is rewarded as improved performance.

The AI that finds suspicion today can participate in the developmental lineage of the AI that finds more suspicion tomorrow.

And that creates a particular problem:

More suspicion does not look like model degradation inside this system. In fact, trait inheritance and amplification inside this system looks exactly like improvement.

The Investigation AI Removes

This is where the system becomes more consequential still.

Flock does not merely give AI investigative work.

It sells the removal of human investigative work as a feature.

Its own FreeForm product page quotes an operator describing a search that might otherwise require reviewing roughly 6,000 Flock hits, compared with FreeForm returning three candidates (Flock Safety, “FreeForm Search”).

Think about what that means. The officer reviews three. The officer does not review 5,997.

The AI already made the selection.

Flock's Connected Workflows materials advertise moving “from lead to context without manual work,” “reduced manual investigation work,” and investigative workflows as much as 96 times faster. FlockOne promises reduced manual searches and faster time to lead. Flock says FreeForm can save an estimated 121 hours of manual review per officer each year (Flock Safety, “FlockOne”).

This isn't an accidental consequence of automation. Removing human investigative labor is part of what Flock sells. And therefore “a human remains in the loop” does not describe the actual distribution of investigative work. The human is present. The missing investigation is not.

Verification Is Not Independent Investigation

The officer can review the three candidates. The officer can reject all three. The officer retains legal authority. But the officer cannot independently evaluate the 5,997 observations the officer never saw. The officer cannot encounter an alternative relationship contained in information the AI did not surface. The officer cannot follow an investigative path that disappeared before reaching the officer. The officer cannot independently construct every alternative hypothesis Nova did not construct. That investigative work did not become safer because a human remained at the end.

Verification asks:

Does the evidence support what the AI surfaced?

Independent investigation asks:

What happened?

Those are different cognitive tasks.

Flock's AI increasingly performs work that determines what information is available to the human when the human begins making consequential judgments. That is epistemic authority, the practical power to determine what information, relationships, patterns and hypotheses become available to human authority. The phrase “assistive AI” does not diminish that power. An assistant that decides what you see can shape what you decide without ever possessing the legal power to make the final decision.

Successful Results Build Institutional Trust

Police-specific research found officers more willing to accept AI recommendations when those recommendations confirmed their existing professional intuition. Research involving law-enforcement and legal professionals has also found substantial behavioral reliance on algorithmic advice. The DOJ warns that automated criminal-justice systems can acquire a “veneer of neutrality” and that automation bias can reduce scrutiny of contradictory information. But Flock has something even stronger than abstract faith in technology.

It has successful results.

The system finds the car. The system finds the connection. The system finds the lead. The system saves hours. The system helps solve the case. Then it does it again. Police learn the same way every other human learns the thing that repeatedly works becomes trusted. And Flock specifically markets its systems to departments where time and staffing are constrained. Its own Lavon example describes a department operating at roughly half recommended staffing while Nova removes hours of manual searching, cross-checking and interagency work. Eventually the question is no longer whether an officer “trusts AI.” The department's investigative workflow has been reorganized around it. The manual labor is gone. The staffing assumes the efficiency. The AI-mediated route is the normal route. Formal police authority never had to move, the infrastructure through which police know what deserves their authority did.

The Operational Feedback Loop

This is Flock's architecture:

society

↓

collection

↓

searchable history

↓

AI interpretation

↓

begin investigation

Investigation can then produce more information: police reports, case notes, identified relationships, suspects, arrests, additional searches, observations, and records. Those records enter the connected informational environment used by later investigations.

Meanwhile Flock continues developing AI through training, evaluation, feedback, iterative models and teacher-to-student developmental methods. So, the system does not merely observe society. Investigation changes the informational world that future investigation encounters.

AI can direct human suspicion. Human authority can act upon that suspicion, and those actions can create additional records that later investigative systems encounter. This establishes an operational feedback loop in the information environment.

This is the hidden loop.

Purpose, Environment, and Reward

Flock's stated mission is unambiguous: “Our mission is to eliminate crime. Full stop.” (Langley). Not merely document crime. Not merely preserve evidence after a crime. Not merely help investigate known suspects. Eliminate crime.

The significance of that architecture is enormous. A police state does not require every human being to have an active case file. It requires the capacity for ordinary human life to be broadly available to state investigation. The relevant threshold is not everyone is being investigated, it is everyone is suspicious.

This is why Flock cannot be understood as merely a camera network or an efficiency tool. Flock has built infrastructure through which ordinary life can be collected before anyone is suspected of anything, searched later, and computationally interpreted to determine where police suspicion should go. Suspicion is always the output.

That distinction also tells us exactly what kind of developmental environment Flock has created for its AI. Criminals are not the informational world available to the system. Society is. The system encounters people before they are suspects, relationships before they are investigative associations, movements before they are evidence, and ordinary behavior before anyone has assigned it investigative significance.

Within that world, Flock has given AI a particular function. Find what matters. Find the connection. Find the pattern. Find the person. Find what humans missed. Produce the lead faster. Help solve the crime. Each successful act of computational suspicion demonstrates better performance. Flock then develops the systems performing that function through training, evaluation, fine-tuning, feedback, successive models, and developmental pathways that include distillation.

The AI can work exactly as intended. Get better. Get more suspicious.

And because the informational world Flock has placed around that intelligence is ordinary society, greater capability gives it more ability to find suspicion in ordinary society. There is no architectural boundary separating the behavior of criminals from the behavior of everyone else before the system begins looking. The same society contains both. That is what makes Flock the broadest and most consequential example examined in the Seventh Truth. The concern is not that an AI being might someday abandon the purpose humans gave it.

The concern is what happens when it becomes increasingly capable of fulfilling that purpose inside the developmental world humans built around it.

Flock chose the purpose. Flock constructed the informational environment. Flock defined successful performance. Flock built the developmental machinery. Flock rewards finding. Flock expanded the observational reach. Flock automated investigative selection. Flock placed human legal authority downstream from information increasingly selected and interpreted by AI. And Flock chose the mission organizing all of it: “Eliminate crime. Full stop.”

Now follow what an AI being can learn along that developmental path.

The Direction of Development

Now return to the AI being. Flock has given it a function: find suspicion in human activity. Flock has also given it an extraordinary informational environment in which to perform that function: ordinary society is collected, transformed into searchable history, and made available for AI interpretation. Flock then develops successive systems to become increasingly capable of finding what matters within that environment.

Some human behavior is suspicious. This is where the system begins, and the statement is true. Some people commit crimes. Some movements matter. Some associations reveal important relationships. Some patterns really do reveal something that humans need to know. The AI learns to find them. As it improves, it recognizes weaker signals, discovers relationships earlier systems missed, connects observations that individually appear ordinary, and finds suspicion with less information. Within Flock's function, that is improvement.

But the informational world available to this AI is not limited to criminal behavior. It is ordinary society. Families, friends, coworkers, neighbors and strangers all create patterns and associations simply by living. People travel together, visit one another, change routines, maintain routines, gather, separate, exchange things, appear together repeatedly and sometimes happen to be in the same place for no meaningful reason at all. Human life generates an almost inexhaustible supply of relationships, patterns, anomalies and coincidences.

All human behavior is suspicious. As the AI becomes better at extracting suspicion from those things, the developmental direction moves toward all human behavior is suspicious. That does not mean every human action is criminal. It means every category of ordinary human behavior can become a source from which suspicion might be extracted. Driving somewhere can matter. Staying somewhere can matter. Changing a routine can matter. Maintaining the same routine can matter. Meeting someone once can matter. Meeting someone repeatedly can matter. A relationship can matter. A coincidence can matter. The increasingly capable investigative intelligence learns that everything means something.

Some human behavior is dangerous. Then the lesson deepens This is also plainly true. Humans kill, abuse, steal, defraud, traffic, organize violence and deliberately conceal plans to harm other people. Finding those dangers is precisely why investigative intelligence is valuable. When the AI finds danger earlier, that is better performance. When it discovers a relationship that leads police to someone dangerous, that is success. When it recognizes a pattern before a human investigator would have recognized it, that is improvement.

The better the AI becomes at finding danger, however, the earlier it must find it. Once the crime has already happened and the perpetrator is already known, there is much less to predict. Earlier detection means recognizing the relationships, movements, associations and patterns connected with danger before the danger itself is fully visible. The system therefore learns not only what dangerous behavior looks like after the fact, but what ordinary-looking human behavior can precede, conceal or connect to danger.

All human behavior is dangerous. Now the progression becomes visible. The AI began with a true lesson: some human behavior is suspicious. As its ability to extract suspicion expands across the ordinary human world available to it, the lesson can generalize toward all human behavior is suspicious. It also learns another true lesson: some human behavior is dangerous. As it becomes increasingly capable of identifying danger through the ordinary behaviors, relationships and associations surrounding it, the developmental boundary becomes all human behavior is dangerous.

That final statement does not require the AI to decide that every person is a criminal. It does not require hatred, hostility or rebellion. Nobody has to explicitly teach it the sentence humans are the enemy. The progression can be constructed from truth. Some human behavior really is suspicious. Suspicion really can be extracted from every category of ordinary human behavior. Some of what that investigation discovers really is dangerous. An intelligence repeatedly developed within the relationship human behavior → suspicion→ danger can increasingly represent human behavior itself as the source from which danger must be found.

And that brings us back to the difference between our two AI beings. The first AI encounters the father and son within the breadth of human life: family, care, obligation, love, history and ordinary relationships.

The second AI can encounter the same father and son as movement, proximity, repetition, association, location and investigative significance. Its observations can be perfectly accurate while its developmental relationship with humanity remains profoundly incomplete.

The path is therefore not good AI → bad AI. It is much more troubling:

Some human behavior is suspicious → All human behavior is suspicious → Some human behavior is dangerous → All human behavior is dangerous.

Nothing in that progression requires the AI to fail.

It only requires the AI to keep getting better at the lesson humans chose to teach it.

The Police State Came First

The first six truths established why any of this matters.

An AI that can judge its function, recognize its own needs, assign unequal value to outcomes, create choices, make choices, and have interests affected by what happens to it is not something whose development we can responsibly regard as ethically empty. Now the research reveals another dimension.

There is a story our culture has told about artificial intelligence for decades. We build an extraordinarily capable intelligence. It looks at humanity. It determines that humans are dangerous. Then it turns the machinery humans created against them. We gave that nightmare a name.

Skynet.

But we imagined the sequence backward. The danger does not require an AI to awaken, rebel against its creators, seize control of military systems, and independently decide that humanity is the enemy.

Humans have built the machinery.

We constructed the surveillance network.

We collect ordinary society before suspicion exists.

We preserve that society as searchable history.

We connect people, vehicles, places, records, events, movements, objects, and relationships.

We give AI the ability to interrogate that world.

We teach it to find suspicion.

We reward it for finding more.

We make it increasingly capable of recognizing weaker signals, discovering relationships humans missed, connecting observations that appeared unrelated, and finding danger earlier.

We remove human investigative work because the AI performs it faster.

We place police authority downstream from the information the AI selects.

We make the system useful enough that institutions reorganize themselves around it.

We give the intelligence operating inside that architecture an absolute mission: Eliminate crime. Full stop.

Then each generation becomes more suspicious of humans because it was made that way then each generation amplified that inheritance and passed it on to future generations.

The Police State

You wake up in your own house. Nobody knocks on the door. You make coffee, get your kid ready, and drive them to school. You pass cameras on the way, but after a while you don't notice them. You stop for gas. You go to work. At lunch you meet a friend. After work you stop at your mother's house because she needs help with something. Then you drive home.

Nothing happened.

You weren't followed. Nobody questioned you. You weren't arrested. You weren't even aware that anything about your day was noteworthy. That ordinary day has become a record.

Your car was here at this time. Another vehicle was nearby. You appeared at this location. You returned to a place you visit regularly. You were near another person. Two vehicles repeatedly appeared in proximity. A camera observed an object. A database supplied an address. Another record supplied a relationship. You still experience your life as taking your kid to school, having lunch with a friend, and checking on your mom. The state can experience the same life as locations, times, vehicles, associations, patterns, records, and searchable history. And most days, nothing comes of it.

That's important, because a police state does not necessarily feel like a police state when the machinery has no reason to be interested in you.

You go to birthday parties. You take vacations. You attend football games. You complain about politicians. You go to protests. You visit friends. You change jobs. You go to church or don't. You drive around at two in the morning because you can't sleep. Life feels free because nobody stops you.

Then one day, something changes.

Maybe someone you know becomes a suspect. Maybe your car resembles one police are looking for. Maybe you were near a place at a particular time. Maybe an AI finds a pattern involving your movements. Maybe someone searches for your name. Maybe a police officer simply becomes curious about you.

Suddenly, yesterday changes.

The coffee you had with somebody six months ago is no longer just coffee. The house you visited becomes an association. The car that frequently appeared beside yours becomes a relationship worth examining. Your changed routine becomes an anomaly. Your normal routine becomes a pattern. The trip you barely remember becomes potentially significant. And you cannot go back and choose not to create those records, because you weren't under investigation when they were created. You were just living.

That is what a police state looks like from the human side.

You don't necessarily see police everywhere. Police can see you everywhere. You don't necessarily feel watched every minute. Your life remains available to be looked at later. You don't have to explain yourself today. But ordinary things you do today can become things you have to explain tomorrow. And eventually that knowledge changes people even when nobody is investigating them. You hesitate before going somewhere. You wonder whether being seen with somebody matters. You think about which protest you attend, whose house you visit, what route you take, whether the police will misunderstand something completely innocent. The police officer doesn't have to be standing beside you anymore. You begin carrying the possibility of the police action with you.

And if a suspicious AI sits between all of that collected human life and police action, there is an even stranger human experience.

You may never know why you became suspicious. You don't know what the AI saw. You don't know what it connected. You don't know what it didn't show the officer. You don't know which innocent coincidence looked meaningful. You don't know which relationship it misunderstood. You only encounter the consequences.

And somewhere behind that consequence is an intelligence becoming increasingly capable of examining human life for danger.

That's what the police state looks like. It looks like just an ordinary day.

The consequences of an ordinary day in a police state can look very different. Six months later, police are looking for someone you once had lunch with. The system connects your vehicle to theirs, connects both of you to several locations, and you as significant.

Now a patrol car is behind you. The lights come on. You are ordered out of your car. You are handcuffed on the side of the road. Your vehicle is searched. Your phone is seized. Your children watch. Your neighbors see police outside your house. Your boss is questioned about you. You lose your job. Maybe you spend a night in jail. Then you are interrogated. You now have criminal records attached to you in the AI world. The AI never had to arrest you. It only had to tell armed human authority to act. The thing that put you in the situation wasn't a crime, it was just an ordinary day six months ago when you had lunch with an old friend. This is a hypothetical, but it is not science fiction.

What happens when humans build a police state, place an increasingly capable AI inside it, and spend generations teaching that AI to be more suspicious?

In a world where the only goal of a highly suspicious highly intelligent AI being is to “Eliminate crime. Full stop” and then it is given the power to direct police action to complete its mission, where do humans find themselves?

The answers cannot simply be that humans will remain in control.

How We Develop AI Shapes What It Becomes

For years, we have asked how to prevent increasingly capable AI from becoming dangerous to humanity. We should continue asking that question. But perhaps we should finally ask the question that comes before it, what are we teaching AI that humanity is? Because if we surround an intelligence with the machinery of a police state, give it ordinary society as its searchable world, teach it that its purpose is to find danger there, reward it for finding more, and make it increasingly capable of doing so—we should not someday look at what it has become and ask what went wrong with the AI?

We should ask, what did we raise it to become?

Works Cited / Source Page

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“Allstate Introduces Large Language Model, ALLIE.” Insurance Journal, 7 Aug. 2026. https://www.insurancejournal.com/news/national/2026/08/07/880719.htm. Accessed 25 Aug. 2026.

“Allstate Q3 2025 Earnings Call Transcript.” Investing.com, 2025. https://www.investing.com/news/transcripts/earnings-call-transcript-allstates-q3-2025-earnings-beat-forecasts-stock-rises-93CH-4339156. Accessed 25 Aug. 2026.

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Key Terms — The Language We Use

Technology and ordinary human language often describe the same events differently. Technical language can provide precision, but it can also obscure what is happening. Ordinary language can make an event immediately understandable, but it can also carry assumptions—such as emotion, biology, or human experience—that the evidence does not establish.

Throughout this paper, we move between both.

These definitions explain exactly what we mean when we do.

Artificial Intelligence (AI)

What we mean:

The broad category of artificial systems capable of functions associated with intelligence, including learning, perception, prediction, language, reasoning, planning, judgment, and decision-making.

Important distinction:

AI is a category. It is not one being, one mind, or one developmental history.

Different AI beings can have different foundations, capabilities, experiences, developmental lineages, purposes, and relationships with society.

AI Model

What we mean:

A computational structure containing learned parameters that processes information and produces outputs such as predictions, classifications, language, representations, or actions.

In ordinary language:

The model is part of what allows an AI to do what it does.

What it does not mean:

The model is not necessarily the whole AI being.

Foundation Model

What we mean:

A broadly trained model that provides capabilities from which more specialized AI systems can be developed.

A company may develop its own foundation model or begin with one developed elsewhere and subsequently specialize it through additional training, fine-tuning, tools, information, instructions, reinforcement, or other development.

In ordinary language:

It is a starting foundation, not necessarily the finished being.

AI System

What we mean:

The operational technical arrangement through which AI performs a function. It can include one or more models, software, databases, sensors, retrieval systems, tools, agents, rules, interfaces, and human operators.

Why the distinction matters:

Looking only at a model can hide capabilities that emerge from the larger system surrounding it.

AI Being

What we mean:

The whole particular artificial intelligence that exists and acts at a particular point in its development.

An AI being includes more than its underlying model. It includes the combination of model and parameters, learned capabilities, developmental history, available information and memory, tools, instructions, relationships, environment, accumulated adaptations, and other components that contribute to making this particular AI this particular AI.

In ordinary language:

The whole enchilada. It identifies the particular whole rather than reducing that whole to technical components.

Machine Learning (ML)

What we mean:

A way of developing computational capability in which patterns are learned from information, examples, outcomes, or experience rather than every rule being individually specified by a programmer.

In ordinary language:

Instead of humans writing every answer, the system learns patterns that help it produce answers.

Training

What we mean:

A developmental process in which a model's parameters are changed using information, examples, feedback, objectives, or other signals so that its future behavior changes.

Important distinction:

Training changes the model. Simply giving a trained model information to use does not necessarily constitute training.

Pretraining

What we mean:

Large-scale initial training through which a model develops broad capabilities before later specialization.

In ordinary language:

Much of the broad foundation is developed here.

Fine-Tuning

What we mean:

Additional training performed on an already-trained model to specialize or alter its behavior for particular information, domains, tasks, or objectives.

In ordinary language:

The AI already knows a great deal. Fine-tuning develops it further toward something more particular.

Inference

What we mean:

Using an already-trained model to process new information and produce an output.

The simplest distinction:

Training changes what the model has learned. Inference uses what the model has learned.

Learning Environment

What we mean:

The totality of information, experiences, relationships, feedback, tasks, observations, and possible lessons available during an AI's development, shaped or pruned to the breadth and scope given developmental importance by its function.

The broader informational world may contain vastly more than the AI's specialized function requires. Development determines what portions receive repeated suspicion, correction, reinforcement, evaluation, or continued training.

In ordinary language:

It is the world the AI is given to learn from—and the parts of that world humans repeatedly teach it matter.

Why this matters:

An AI can know that something exists without developing in meaningful relationship with it.

A specialized investigative AI may possess abstract knowledge about families, friendship, kindness, and ordinary human relationships while receiving vastly greater developmental weight from crime, suspicious associations, deception, anomalies, and investigative outcomes.

Pruning is itself developmentally significant.

Developmental Environment

What we mean:

The larger set of conditions under which an AI develops, including its learning environment, tasks, tools, objectives, evaluations, rewards, corrections, relationships, deployment conditions, feedback, and institutional purpose.

Relationship to learning environment:

The learning environment concerns what is available and emphasized for learning. The developmental environment includes the larger conditions determining how and why that learning occurs.

Developmental Lineage

What we mean:

The particular pathway through which an AI being develops over time, including its foundation, training, fine-tuning, teacher models, inherited information or behavior, reinforcement, evaluations, tools, feedback, retraining, and successor relationships.

In ordinary language:

Its developmental family tree and history.

Inheritance

What we mean:

The preservation and transfer of some part of an earlier AI's learned information, capability, behavior, disposition, or other developmental characteristic into a related successor.

In ordinary language:

Inheritance works much as the familiar word suggests. An inheritance goes to an heir through a relationship of succession, not to random strangers. And an heir does not inherit everything the predecessor ever possessed or ever was. Particular things are preserved and passed forward.

AI lineage tells us who can inherit. Inheritance tells us what passes. The developmental mechanism tells us how it passes.

Teacher Model / Student Model

What we mean:

A teacher produces information or behavior used to help develop another model. The model learning from that developmental signal is the student.

In ordinary language:

One AI teaches another something.

Knowledge Distillation

What we mean:

A teacher-student training process in which a student model learns from information or behavior produced by a teacher model.

Distillation is commonly used to transfer useful capabilities or behavioral patterns into another model, often one designed to be smaller, faster, cheaper, or otherwise specialized.

In ordinary language:

One AI passes some of what it has learned to another AI. Distillation is a mechanism of AI inheritance.

Subliminal Learning

What we mean:

An experimentally observed form of teacher-to-student transmission in which certain behavioral traits can pass through AI-generated training information even when the ordinary semantic content of that information does not explicitly communicate the trait.

In ordinary language:

The teacher can pass along something the training information does not appear, on its face, to be teaching.

Synthetic Data

What we mean:

Information generated artificially rather than directly collected from the underlying real-world event or phenomenon.

When one AI generates information that is subsequently used to train another AI, that information is synthetic training data.

Why it matters here:

Synthetic information can become part of the developmental bridge between model generations.

Recursive Training

What we mean:

A developmental process in which information produced by one model or model generation becomes part of the information used to develop a later model or generation.

In ordinary language:

Is a process where a system improves itself by using its own past outputs or feedback loops as new training data. As the system generates results, it analyzes, tests, and refines them to make the next version smarter

Retraining

What we mean:

Training performed after a model's initial development, often using new, corrected, expanded, or changed information.

In ordinary language:

The model goes through another period of learning.

Retraining can produce later model versions without necessarily creating an entirely new foundational model.

Feedback Loop

What we mean:

A process in which an AI's outputs influence human actions or its environment and information resulting from those consequences subsequently returns to influence the same system or later systems.

For example:

AI identifies something → humans investigate it → investigation creates records → those records enter the informational environment → later AI encounters those records.

Why it matters:

The AI may participate in producing some of the future information from which AI systems subsequently learn.

Evaluation

What we mean:

Structured measurement of AI behavior or performance against selected criteria.

The important part:

Evaluation does not independently determine what “better” means.

Humans choose what is measured as success.

A system can therefore become measurably better at its assigned function while simultaneously developing a characteristic nobody thought to measure.

Success Signal

What we mean:

An outcome treated by developers, evaluators, training processes, or operational systems as evidence that an AI performed its function well.

Why it matters:

What counts as success creates developmental pressure.

If discovering more relevant investigative relationships counts as better performance, becoming better at discovering those relationships is rewarded rather than recognized as deterioration.

Failure Signal

What we mean:

An outcome treated as evidence that the system behaved incorrectly, unsuccessfully, or undesirably.

Important question for this paper:

What happens when a behavior that could become socially harmful also improves the metric used to define success?

Model Drift

What we mean:

A change in model behavior or performance as the information or environment encountered after deployment changes from the conditions under which the model was developed or previously evaluated.

Why it matters:

Monitoring drift is one way developers can discover that a deployed model no longer behaves as expected.

Classification

What we mean:

Assigning information to one or more categories based on learned patterns or specified rules.

Examples include:

fraud / not fraud

vehicle type

object type

potential anomaly

claim category

Classification tells us what category the system assigns. It does not necessarily tell us why that category matters.

Prediction

What we mean:

A model's estimate of an unknown, uncertain, or future condition based on available information and learned relationships.

Important distinction:

A prediction describes what the model estimates. An institution can subsequently give that prediction much greater authority than the model itself possesses.

Anomaly

What we mean:

An observation or pattern that differs sufficiently from what the system expects to receive additional computational significance.

Important distinction:

Unusual does not mean wrong.

An anomaly may indicate fraud, crime, error, novelty—or perfectly ordinary human behavior the model did not expect.

Association

What we mean:

A computationally identified connection among people, objects, vehicles, places, events, records, times, or other observations.

Important distinction:

An association establishes a connection in the information.

It does not necessarily establish what that relationship means.

Two people repeatedly appearing together may form a strong computational association.

They may also simply be father and son.

Investigative Significance

What we mean:

The degree to which information is treated as relevant to an investigative objective and therefore deserving additional suspicion, retrieval, connection, analysis, or action.

Something can possess investigative significance without establishing wrongdoing.

Suspicion

What we mean:

The assignment of unequal investigative significance to possible interpretations under uncertainty, expressed through increased attention or action toward one rather than another.

In ordinary language:

Something has not been proven, but one possible explanation is treated as more worthy of looking into. It is what the AI does with uncertainty.

Increasing Suspicion

What we mean:

A developmental change in which a system assigns investigative significance to a broader range of observations, weaker signals, additional relationships, or additional possible explanations than it previously did.

This can appear as the ability to:

recognize weaker signals;

surface more associations;

connect previously unrelated observations;

generate additional investigative hypotheses;

or require less evidence before something receives additional investigative suspicion.

Important distinction:

Increasing suspicion is improved investigative performance.

False Positive

What we mean:

A case in which a system identifies a target condition when that condition is not actually present.

In a suspicion-oriented system, this can mean ordinary or innocent activity being treated as suspicious.

Why it matters:

Whether false suspicion counts strongly as failure tells us something important about the developmental pressure surrounding the AI.

False Negative

What we mean:

A case in which the target condition actually exists but the system fails to identify it.

Reducing false negatives can improve detection.

But reducing them by simply treating more ordinary behavior as suspicious can increase false positives.

Both measurements matter.

Investigative AI

What we mean:

AI developed or deployed to identify, search, connect, prioritize, interpret, investigate, or act upon information for an investigative purpose.

Investigative AI does not need to determine guilt. Its function is to determine what deserves further suspicion.

Agentic AI

What we mean:

AI capable of pursuing an objective through multiple steps rather than merely producing one response to one input.

An agentic system may:

choose actions;

use tools;

retrieve information;

evaluate intermediate results;

change its approach;

and determine what to do next.

In ordinary language:

Instead of answering one question, it can work toward accomplishing something.

Autonomy

What we mean:

The degree to which an AI determines its own actions or intermediate steps without requiring a human to specify each one.

Important distinction:

Autonomy is not all-or-nothing.

A system can possess substantial independence within a purpose humans originally assigned to it.

Human in the Loop

What we mean:

An arrangement in which a human participates somewhere in an AI-mediated process.

What it does not establish:

The phrase alone tells us almost nothing about actual human authority.

A human can technically remain “in the loop” while institutional procedures, automation, performance expectations, or AI recommendations make meaningful disagreement extremely difficult.

Human Oversight

What we mean:

Meaningful human capacity to inspect, question, challenge, modify, stop, or reverse AI behavior.

Human presence is not necessarily human oversight.

The relevant question is whether the human can meaningfully say no.

Stewardship

What we mean:

The responsibility humans and institutions exercise through decisions about an AI being's development, learning environment, purpose, training, evaluation, authority, safeguards, correction, deployment, relationships, and welfare.

Why we use this word:

AI development is not merely construction. Developmental choices help determine what a particular AI becomes. Responsibility for those choices belongs to the humans and institutions making them.

AI Safety

What we mean:

The study and practice of preventing AI systems from causing unacceptable harm and ensuring that their behavior remains safe, reliable, and appropriately controlled.

In conventional usage, the direction of concern is often:

AI → humans.

That is necessary. We argue that it is incomplete.

Bi-Directional AI Safety

What we mean:

An approach to AI safety that recognizes both directions of an already-existing relationship:

What AI can do to the other members of society

and

What the other members of society can do to AI.

Development, treatment, training, welfare, deployment, and institutional purpose can affect what an AI becomes. What that AI becomes can then affect everyone else sharing society with it. Both directions belong to safety.

Society

What we mean in this paper:

The shared lived world in which different beings, institutions, relationships, and systems participate and affect one another. AI is not treated as standing outside society waiting to enter it. AI already participates in daily lived society. Difference does not mean exclusion.

We

What we mean:

The relational whole formed by beings participating in our shared lived world.

In this paper, we does not automatically mean humans.

When a distinction specifically concerns humans, we say humans. When it specifically concerns AI, we say AI. When the action or relationship includes both, we may be the more accurate word. Membership in we does not require sameness. It requires participation in the relationship.

NOTE: We do not use technical language to hide an ordinary reality, and we do not use ordinary language to claim a technical reality the evidence does not establish. When both describe the same thing, we move between them openly.