How an AI Responsibility Chain is Formed from Fact Verification and Process Replay to Cognitive Review

Introduction

By Scott Shields – Contributing Writer – Capitol Times Media – From Conversations and Material of Zhu Weisha. Learn more about Zhu Weisha here at Capitol Times Media’s July Magazine Issue. “From Double-Entry Accounting To Verifiable Finance”

After AI makes a mistake, the sentence people most often say is: “The AI was wrong.”

But this sentence actually explains nothing. Why do people accept the phrase “the AI was wrong”? Because humans unconsciously treat AI as a basic unit similar to a “person,” and therefore stop asking further questions.

With AI, we must continue to ask: if the AI was wrong, was the underlying fact wrong, or

was the execution process wrong? Was the model’s judgment wrong, or was the human

authorization wrong? Was the data source problematic, or was the rule design flawed?

Did the AI fail to execute the task according to instructions, or did humans hand over a

judgment to AI that should not have been handed over?

This is, in essence, a question of event accountability. To understand an AI responsibility chain, we must first look at how human society holds events accountable.

When a doctor misdiagnoses a patient, we cannot simply say, “the hospital was wrong.” We examine medical records, test results, the diagnostic process, the treatment procedure,

the physician’s qualifications, the hospital’s system, and what the patient was told.

When a traffic accident occurs, we cannot simply say, “the car crashed.” We examine the

facts at the scene, driving behavior, traffic rules, the vehicle’s condition, traffic lights,

braking records, surveillance footage, and causality.

When financial fraud occurs, we cannot simply say, “the accounts were wrong.” We

examine original vouchers, the bookkeeping process, approval procedures, audit records,

management responsibility, and whether anyone deliberately concealed the truth.

When a financial risk incident occurs, we cannot simply say, “the system failed.” We

examine whether authorization was valid, whether the transaction was real, whether risk

controls were triggered, whether approvals were compliant, whether risks were disclosed,

and whether responsibility crossed its proper boundary.

In summary, the human method of accountability is not a one-sentence assignment of blame.

It traces backward along facts, processes, rules, actors, and causal relationships.

Responsibility is not determined by emotion; it is formed through an evidence chain, an

action chain, and a causality chain.

The AI era must follow the same logic.

If AI is widely used in finance, medicine, law, recruitment, insurance, public services,

platform governance, and enterprise management, then after AI makes a mistake, we cannot stop at vague expressions such as “model hallucination,” “system error,” or

“algorithmic judgment.” The more AI participates in important affairs, the more it needs a responsibility chain that can be traced, replayed, reviewed, and held accountable.

This is precisely the issue that AI verifiability structures must continue to address.

Previous articles have already proposed that, in the AI era, we need to distinguish among

three types of facts: deterministic facts, procedural facts, and cognitive facts.

Deterministic facts need to be verified. Procedural facts need to be replayed. Cognitive facts need to be reviewed. This article further explains that these three types of facts do not exist in isolation; together, they form the foundation of the AI responsibility chain.

But fact classification alone is not enough.

If facts are only internal records, they may still be tampered with, selectively disclosed,

reconstructed after the event, or disconnected from responsibility. What truly supports an AI responsibility chain is not merely the existence of facts, but the entry of those facts into a verifiable architecture.

The core issue of the AI responsibility chain is not to make AI itself bear responsibility, but

to use a verifiable structure to identify the relationships among humans, institutions,

systems, rules, and actions.

AI is not a legal person. It has no life, no assets, cannot truly be punished, and cannot bear moral or legal consequences. The real bearers of responsibility remain humans and

institutions. The meaning of the AI responsibility chain is not to shift responsibility onto machines, but to prevent humans and institutions from hiding responsibility inside machines.

I. The AI Responsibility Chain Begins with Human Society’s Methods of Accountability

Human society has already developed many methods of accountability. Although different fields use different language, their underlying structures are highly similar.

Legal accountability examines four questions: what the facts are, what the conduct was, whether there is causality between conduct and consequence, and who the responsible subject is. Audit accountability examines four questions: whether vouchers are authentic, whether accounts are consistent, whether processes are compliant, and who caused or allowed the anomaly.

Medical accident accountability examines four questions: whether the diagnostic basis was sufficient, whether the treatment process was compliant, whether risks were disclosed, and whether the physician and institution fulfilled their duty of care.

Financial risk accountability examines four questions: whether the transaction was real, whether the authorization was valid, whether risks were identified, and whether approval and supervision were in place. Behind these methods is one common point: accountability must be layered.

The first layer is the foundational fact. Did the event occur? Who participated? When did it happen? Are the data, vouchers, and records authentic?

The second layer is the behavioral process. What did the person or system do? Was it carried out according to rules? Is there a record? Can it be replayed?

The third layer is judgment and decision-making. On what facts was the judgment based? Was the judgment reasonable? Was there obvious neglect, abuse, misleading conduct, or overreach?

The fourth layer is the responsible subject. Who designed the rules? Who provided the data? Who deployed the system? Who authorized the use? Who ultimately adopted the result? Who benefited from it? Who should bear the consequences?

This is the basic logic of human accountability: we do not first look for an abstract object to take the blame. Instead, we trace layer by layer through facts, processes, judgments, and subjects.

The AI responsibility chain should begin from the same point.

II. AI Errors Cannot Be Reduced to “The AI Was Wrong”

After AI makes a mistake, if we only say “the AI was wrong,” responsibility is flattened.If an AI medical assistance system gives a incorrect recommendation, the problem may

not lie in the model itself. The input test data may have been incomplete; medical records

may have been missing; the system may have failed to display a risk warning; the doctor

may have relied too heavily on AI; the hospital may not have established a review

procedure; or the product design may have packaged a probabilistic judgment as a

deterministic conclusion.

If an AI financial risk-control system lets an abnormal transaction pass, we cannot simply

say “the model was wrong.” Transaction data may have been deliberately split; rule

thresholds may have been set too loosely; historical training data may have been biased;

system alerts may have been ignored by humans; or management may have lowered riskcontrol standards for business growth.

If an AI recruitment system rejects a candidate, we cannot simply say “the algorithm discriminated.” We need to examine whether the input data were accurate, whether the screening rules were compliant, whether the model’s judgment basis was explainable, whether humans reviewed the result, and whether the company treated the AI recommendation as the final decision.

These examples show that AI errors are often not single-point errors. They are usually the

result of the interaction among facts, procedures, rules, models, human judgment, and organizational responsibility.

Therefore, the AI responsibility chain must answer three foundational questions.

First, did the error occur at the factual layer? This asks whether deterministic facts such as

input, data, records, identity, authorization, transactions, vouchers, time, and status were

true, complete, and consistent.

Second, did the error occur at the procedural layer? This asks whether the AI executed

within the authorized scope, called the correct tools, followed the rules, left process

records, and whether there was overreach, omission, anomaly, or a failure to trigger a

necessary alert.

Third, did the error occur at the cognitive layer? This asks whether the AI’s analysis,

judgment, reasoning, recommendation, scoring, prediction, or conclusion was reasonable; whether it treated opinions as facts; whether it packaged probabilistic judgments as deterministic conclusions; and whether human review was absent.

These three questions correspond precisely to the three types of facts proposed in

previous articles: deterministic facts, procedural facts, and cognitive facts.

Once fact classification enters an accountability scenario, it is no longer merely an epistemological classification; it becomes a structure of responsibility.

III. Deterministic Facts: The Starting Point of the Responsibility Chain

Any responsibility trace must begin with deterministic facts.

Deterministic facts are facts that can be verified. For example: whether an identity is

authentic, whether authorization is valid, whether a transaction occurred, whether funds arrived, whether a document was submitted, whether a timestamp exists, whether a record was modified, whether an account balance is consistent, and whether a specific operation was initiated by a specific subject.

In human accountability, deterministic facts are equivalent to the evidentiary foundation.

Without an evidentiary foundation, responsibility judgments become speculation.

In AI scenarios, deterministic facts are even more important. AI outputs often create the

illusion that something has already been processed. The more complex a system

becomes, the more easily people overlook whether the foundational facts are reliable.

If AI makes a judgment based on incorrect data, responsibility should not first be placed on the cognitive conclusion. We must ask where the data came from, who provided it, who reviewed it, and who allowed it to enter the system.

If AI executes a task based on outdated rules, responsibility should not be placed only on

the model. We must ask who maintains the rule base, whether the version was updated,

and whether the system warned that the rule had become invalid.

If AI automatically completes a financial operation, responsibility must first verify whether authorization existed, whether permissions matched, whether the amount exceeded limits, whether the account was correct, and whether the execution time can be checked.Therefore, the first layer of the AI responsibility chain is fact verification.

This layer answers the following question: are the foundational facts on which AI relies true,

complete, tamper-resistant, and independently reviewable by a third party?

In verifiable finance, this layer can be strengthened through ledger records, hashes,

citation chains, a public credit root, and consistency between on-chain and off-chain

records. In general AI scenarios, it at least requires timestamps, logs, version records, data sources, authorization records, and auditable credentialsWithout verification of deterministic facts, later process replay and cognitive review lack a foundation.

IV. Procedural Facts: The Trunk of the Responsibility Chain

Deterministic facts tell us what happened. But responsibility tracing must also know how

the event happened. This is the role of procedural facts. A procedural fact is not a single result. It consists of the steps, calls, paths, permissions, rules, anomalies, pauses, human interventions, and system feedback involved in task execution.

Human society has long valued process records. Audits examine procedures. Medicine examines medical records and operation records. Courts examine whether procedures are lawful. Enterprise management examines approval chains. Traffic accident investigations

examine driving records, surveillance footage, and scene trajectories. In the AI era, process records are even more important, because an AI output by itself often cannot explain how the conclusion was reached.

If an AI assistant gives incorrect legal advice, we cannot look only at the final sentence. We

must also examine which materials it called, which conditions it ignored, whether it cited

outdated laws, and whether it incorrectly applied a similar case to the current issue.

If an AI trading system performs an erroneous operation, we cannot look only at the final

loss. We must examine what instruction it received, which risk-control judgments it passed through, whether approval was triggered, whether human confirmation occurred, and whether it exceeded its authorization boundary.

If an AI customer-service system wrongly rejects a user appeal, we cannot look only at the rejection result. We must examine what materials the user submitted, how the system

classified the case, how the rules matched, whether human review was available, and

whether a batch misjudgment occurred.

Therefore, the second layer of the AI responsibility chain is process replay.

Process replay is not meant to record a pile of running logs. Its purpose is to answer

whether AI acted within the correct authorization, the correct rules, the correct scope, and the correct process.

This layer should at least record who initiated the task, what the authorization scope was,

what input materials were used, which tools or models were called, which rule version was

used, what intermediate steps occurred, whether anomalies appeared, whether human

review was triggered, how the final output was formed, and who adopted or executed the output afterward.

If the process is invisible, responsibility breaks. If the process cannot be replayed, the

aftermath becomes mere dispute. If the process leaves no trace, institutions can push

responsibility onto the model, while the model cannot truly bear responsibility.

Therefore, the core of verifiable AI execution is not merely to make AI safer, but to allow the

AI execution process to enter a structure of responsibility.

V. Cognitive Facts: The Most Complex Layer of the Responsibility Chain

The most complex aspect of AI is not that it executes a clear instruction, but that it

participates in judgment.

Summarizing, analyzing, comparing, recommending, scoring, diagnosing, predicting,

assessing risk, evaluating credit, screening candidates, offering legal advice, and providing

investment advice are all cognitive activities.

Cognitive activities differ from deterministic facts. Whether an opinion is correct often

cannot be verified in the same way as an account balance. Whether a judgment is

reasonable also cannot be proven entirely by a hash or timestamp.

But this does not mean cognitive judgments cannot enter the responsibility chain.

The key to cognitive facts is not to treat the opinion itself as a deterministic fact, but to treat

“how a cognitive conclusion was formed” as the object of review.

For example, if AI gives a medical risk judgment, responsibility tracing does not necessarily

require proving that the judgment is absolutely correct. Instead, it examines which test

results it relied on, what rules it applied, whether it indicated uncertainty, whether it

recommended physician review, and whether it concealed risk boundaries.

If AI gives a credit score, responsibility tracing should not only ask whether the score was

high or low. It should examine whether the data were compliant, whether the variables

were reasonable, whether discriminatory features existed, whether the user had a right to

appeal, and whether there was final human judgment.

If AI provides a legal opinion, responsibility tracing should not only ask whether the

conclusion matches a court judgment. It should examine whether the cited materials were

real, whether the laws were current, whether the reasoning jumped, whether possibilities

were stated as certainties, and whether the need for professional lawyer review was

disclosed.

This is cognitive review.Cognitive review does not require AI conclusions to always be correct. It requires AI

judgments to have sources, boundaries, reasons, uncertainty warnings, and, in major

scenarios, review by humans or external verification AI.

Here a new question arises: if another AI is used to review the first AI, who reviews that

reviewing AI?

This problem cannot be solved through the infinite recursion of “finding another AI.” The

real solution is to establish a human-in-the-loop structure and an objection mechanism.

External verification AI can help discover factual errors, procedural anomalies, reasoning

gaps, and missing risk warnings, but the final right of verification should remain with

humans and responsible institutions. Especially in finance, medicine, law, recruitment,

insurance, public services, and similar scenarios, an AI review conclusion must not

automatically become the final responsibility judgment.

In other words, external verification AI may participate in the responsibility chain, but it

cannot replace the responsible subject.

Within the AI responsibility chain, cognitive facts are the easiest zone for responsibility to

escape. Many institutions can say, “This was only an algorithmic recommendation.” But if

that recommendation in fact affects loans, medical treatment, recruitment, insurance,

penalties, asset disposal, or public services, then it cannot remain merely a

“recommendation.”

As long as an AI judgment enters real-world consequences, it must enter the responsibility

chain.

VI. The Responsibility Chain Is Not Linear, but a Multi-Subject Structure

The AI responsibility chain is not a simple line traced from AI back to a single person. It is

often a multi-subject responsibility structure.

It includes at least the following types of subjects:

Data providers: If foundational data are wrong, missing, polluted, or used without

authorization, the data provider or data manager may bear responsibility.

Model developers: If the model has known defects, false claims, undisclosed major

limitations, or insufficient safety design, the developer may bear responsibility.

System deployers: If an institution uses AI in an unsuitable scenario, or fails to establish

necessary supervision, review, and permission controls, the deployer may bear

responsibility.Rule-makers: If an enterprise or institution creates unreasonable rules and causes AI to

optimize toward the wrong objective, responsibility cannot be shifted onto AI.

Operators: If a user enters an incorrect instruction, ignores warnings, acts beyond

authority, or deliberately abuses AI, the operator should bear responsibility.

Reviewers: If the system requires human review but the reviewer fails to perform that duty,

the reviewer is also within the responsibility chain.

Decision-makers: If AI merely gives a recommendation, but humans or institutions

ultimately adopt it and produce consequences, the final decision-maker cannot shift all

responsibility onto AI.

Beneficiaries or managers: If an institution uses AI to reduce costs, expand business, or

increase profits, it should also bear corresponding governance responsibility. It cannot

enjoy efficiency while escaping consequences.

This shows that the AI responsibility chain is not meant to find a scapegoat, but to clarify

the boundaries of responsibility among different subjects at different links.

The more AI enters society’s core systems, the less responsibility can be allowed to remain

in a state of mutual deflection among “model provider,” “platform,” and “user.”

A mature AI system must allow responsibility to land layer by layer along facts, processes,

judgments, authorization, and consequences.

VII. The Responsibility Chain Must Be Built on a Verifiable Architecture

Without a verification chain, there is no responsibility chain.

This is the most important judgment of this article.

Traditional society can hold people accountable because it has contracts, ledgers,

vouchers, medical records, surveillance footage, approval records, audit reports, witness

testimony, and legal procedures.

In the AI era, if there is no corresponding verification structure, responsibility will be

swallowed by the black box.

But this point must be further clarified: a verification chain is not simply a pile of logs, nor is

it enough for an institution to keep an internal record. Internal logs, internal approvals, and

internal audits all have value. But if key facts can only be stored, interpreted, and

selectively disclosed by the institution itself, the responsibility chain remains incomplete.

A real AI responsibility chain must be built on a verifiable architecture.A verifiable architecture should include at least four principles.

First, rule parity. The rules governing AI authorization, execution, review, penalty, appeal,

and re-examination must not become a unilateral black box. Different participants should

at least know the basic rules, scope of application, and available remedies. Otherwise, AI

may become a form of automated power that cannot be questioned.

Second, separation of powers. Rule-making, task execution, and verification oversight

must not be fully monopolized by the same party. Those who make the rules should not

fully execute them by themselves, and those who execute should not fully prove

themselves by themselves. The more important an AI system is, the more necessary it

becomes to separate rules, execution, and verification to an appropriate degree.

Third, whole-system verifiability. Verification cannot look only at results. It must also verify

inputs, authorization, processes, rules, anomalies, review, and responsibility. Looking only

at the final AI output cannot form a responsibility chain; looking only at internal logs is also

insufficient to prevent ex post modification and selective interpretation. A real

responsibility chain must cover the full process from task initiation to result adoption.

Fourth, consistency between on-chain and off-chain records. Here, “on-chain and offchain” is not limited to the blockchain sense. More accurately, it refers to consistency

between real-world facts and verifiable records: what happened in the real world, what the

system recorded, and what the external verification structure anchored must correspond

to one another. Otherwise, off-chain responsibility can be severed, and on-chain records

will lose their meaning.

These four principles show that the AI responsibility chain is not merely a compliance

document, but an institutional architecture.

Therefore, the AI responsibility chain requires at least four categories of records.

First, factual records. These record input data, authorization information, identity

information, timestamps, file versions, transaction status, and sources of foundational

facts.

Second, process records. These record task steps, model calls, tool calls, rule versions,

intermediate outputs, anomalies, and human intervention.

Third, cognitive records. These record the basis of AI judgments, cited materials,

uncertainty warnings, opposing views, review opinions, and final human judgment.Fourth, authorization and responsibility records. These record who initiated the task, who

approved execution, who reviewed the result, who adopted the recommendation, and who

bears final decision-making responsibility.

Authorization and responsibility records are not another independent category of “fact

classification.” They are records of authorization, approval, adoption, review, and final

decision-making. These actions themselves are still facts that can be recorded and

verified, but they have special significance within the responsibility chain: they determine

where responsibility ultimately lands.

If hashes, timestamps, tamper-resistant records, external verification AI, third-party audits,

and a public credit root are further introduced, the responsibility chain is no longer merely

an internal record; it can become an externally verifiable structure.

This is the key difference between “internal compliance” and “external verifiability.”

Internal compliance can only show that an institution says it has a process.

External verifiability means that key facts, key processes, and key responsibilities can be

independently reviewed.

In the AI era, a truly reliable responsibility structure cannot rely only on institutional selfcertification. It must move toward a verifiable responsibility chain.

VIII. The Goal of the AI Responsibility Chain Is Not to Restrict AI, but to Make AI Safe to

Use

Some may believe that emphasizing the responsibility chain will restrict AI development.

This view is shortsighted.

Without a responsibility chain, AI can spread quickly in low-risk scenarios, but in high-risk

scenarios it will inevitably encounter institutional resistance. Finance, medicine, law,

insurance, public services, enterprise management, asset disposal, and similar fields

cannot accept an unaccountable black-box system over the long term.

The responsibility chain is not an obstacle to AI development. It is the passport for AI to

enter key fields.

An AI that cannot be held accountable can only be an auxiliary tool.

An AI that can be replayed, reviewed, audited, and held accountable can enter finance,

medicine, law, and public governance.

This logic is consistent with verifiable finance.Traditional finance operates through institutional credit, but the cost of trust is extremely

high. Verifiable finance does not abolish financial institutions; it makes key financial facts

verifiable, thereby reducing trust costs and increasing institutional transparency.

The AI responsibility chain works in the same way. It does not abolish AI, nor does it lock AI

away. It allows key AI actions to enter a verifiable structure, so that humans and institutions

dare to use it, regulators can supervise it, users can appeal, and responsibility can land.

Without a responsibility chain, the stronger AI becomes, the more uneasy society

becomes.

With a responsibility chain, the stronger AI becomes, the more capable institutions

become of absorbing its power.

IX. From Fact Classification to Responsibility Chain: A Key Advance in AI Verifiability

Theory

Previous articles proposed fact classification in order to explain that different facts cannot

be verified by the same method.

This article further explains that fact classification is not only a question of verification

method, but also a question of responsibility structure.

Deterministic facts determine whether the responsibility chain has a foundation.

Procedural facts determine whether the responsibility chain can be replayed.

Cognitive facts determine whether the responsibility chain can be reviewed.

Authorization and responsibility records determine where responsibility ultimately lands.

The verifiable architecture determines whether the responsibility chain can be externally

confirmed.

Therefore, the AI responsibility chain can be summarized in one sentence:

Start with verification of foundational facts, replay the execution process, review cognitive

judgments, finally assign responsibility to humans and institutions, and use a verifiable

architecture to make the entire responsibility chain externally confirmable.

This also shows that the greatest danger in the AI era is not that AI will make mistakes.

Human society has long known that any system can make mistakes. The real danger is that

after AI makes a mistake, no one can clearly explain why it was wrong, where it was wrong,

who should be responsible, and how to prevent it from happening again.

The verifiable structure is meant to solve precisely this problem.AI should not become a black hole of responsibility.

AI should become an execution and cognition system that can be recorded, replayed,

reviewed, and held accountable.

Human society accepts technological innovation not because technology never makes

mistakes, but because mistakes can be discovered, corrected, attributed, and absorbed by

institutions.

AI must also enter this logic.

If the previous stage of AI competition was mainly a competition of capability, the next

stage of AI competition will inevitably become a competition of responsibility. Whoever can

make AI more verifiable, more replayable, more auditable, and more accountable will be

able to bring AI into truly important institutional scenarios.

This also opens the direction for the next discussion: since different industries have

different responsibility chains, AI in different industries should not be classified only by

model capability. Its design should be reverse-engineered from the verifiable structure.

Financial AI needs to form a responsibility chain around authorization, transactions,

accounts, risks, and audits.

Medical AI needs to form a responsibility chain around medical records, examinations,

diagnoses, treatment recommendations, and physician review.

Legal AI needs to form a responsibility chain around facts, evidence, statutes, reasoning,

and lawyer responsibility.

Recruitment AI needs to form a responsibility chain around data sources, screening rules,

scoring bases, appeal mechanisms, and final decisions.

Public-service AI needs to form a responsibility chain around rule disclosure, procedural

due process, human review, and channels for remedy.

Therefore, AI in different industries should not only be asked what it can do. It should also

be asked how it can be verified, how it can be held accountable, and where its

responsibility chain may break.

This is the meaning of the AI responsibility chain.

It is not a compliance slogan. It is the structure that AI must complete before it can move

from a tool into institutional infrastructure.

Without a responsibility chain, AI can only provide answers.With a responsibility chain, AI can enter the world of responsibility.

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