Following the conferences in Chișinău (ULIM, October 15–16, 2026) and Novi Sad (November 5 and 13, 2026), Robert Williams — Editor-in-Chief of Justice News247 / News247WorldPress, was interviewed by six artificial intelligence systems: Perplexity, Grok, Doubao, Gemini, DeepSeek, and Claude (Anthropic).
The interview focused on three themes grounded strictly in the material presented at those events: Algorithmic Triangulation, technological co-authorship, and the responsibility for AI-generated outcomes. Each AI formulated its questions independently; Robert Williams’ responses below reflect his position as presented at the conferences.
Three Questions No AI Could Answer: Claude and Robert Williams
Robert Williams asked Claude (Anthropic) to read his article “AI Liability and Human Responsibility: Lessons from 2026” and extract three questions whose answers only the author could give. The answers below are his own, in his words, lightly edited for clarity.
Question (Claude): Your protocol of algorithmic triangulation has six steps. At which step does the human, rather than the AI, most often stumble, and what would you change in the protocol because of it?
Answer (Robert Williams): Triangulation, meaning asking others as well in order to convince myself or verify my information, remains a personal method that people use or do not use. It does not depend on age or context.
When I read an author in print, I can say I don’t like what he writes or that the book is not good. When AI, which can be more attentive to detail than a human, delivers conclusions, the situation is different: a student who receives a synthesis has no way of knowing whether it is true.
If triangulation were introduced into textbooks, it would become one more literacy on top of human literacy. The terms would lose most people and could differ from one generation to the next.
Question (Claude): You proposed a dedicated estate as a financial guarantee, plus subsidiary liability of the creators. Who should administer that estate without a conflict of interest, given that those who understand the technology are usually also those who profit from it?
Answer (Robert Williams): The question of liability and administration has to start from the present. Car insurance and other sectors have grown without control, and legally, commercially and professionally, the prerogative remains with the creator firms.
The same is visible with fines. Fines imposed on giants go to the state budget, not to those who suffered or who may be affected by the problems caused.
As for who will administer such an estate, there is no consensus for now. Most probably, national audit and ethics authorities.
Question (Claude): In the Q&A you described three global paradigms: China, the USA and Europe. If liability remains 100% human, who pays when the system gets it wrong, and which of the three leaves the largest liability gap?
Answer (Robert Williams): The liability gap remains strictly in the area of recognising intelligence as a possible equal of human intelligence, or as an extension of it. I do not separate anyone, because it is a problem for all.
As for payment, there is a sum of contradictions. We have examples in medicines with adverse reactions that were banned, but only after they had earned enough money for the creator, and in the automotive sector, with recalls. The list goes on.
Question- Grok
Grok: If we accept that artificial intelligence remains merely an “object” and that responsibility is 100% human, how do we justify situations in which an AI system makes autonomous decisions in real time (autonomous driving, high-frequency trading, medical resource allocation, etc.), and the creator or user can no longer effectively intervene at the moment the harm occurs? Do we not risk creating zones of structural irresponsibility?
Answer – Robert Williams:
At present, automations in various industries have long used algorithms and sensors. When problems arise, they are simply treated as defects or errors, and the process is stopped. The recent FAA example shows that no one answers personally in real time; investigations and updates are carried out. The creator could not intervene directly; the user detected the problem. Current development allows only partial responsibility, both of the creator and of the system.
Question 2 – Grok:
The proposal of a “limited electronic personality” (exclusively for reparative purposes, with a dedicated patrimony) seems like an elegant compromise. But does it not, de facto, open the door to the recognition of a form of legal subjectivity of AI, even if limited? And if so, where do we draw the line between “only for reparations” and other attributes of legal personality that could later be claimed?
Answer – Robert Williams:
At present, artificial intelligence is used in an applied or partial manner. A form of responsibility would open the door to recognition as an artificial-synthetic citizen. There are already robots based on AI that have a direct impact through their actions. A robot will not be able to pay millions, but responsibility could be converted into community service work, and the most serious sanction would be shutdown.
Question 3 – Grok:
The article promotes “algorithmic triangulation” (separately querying at least 3 AI systems + human verification) as a method of critical discernment and detection of hallucinations. But what happens when all 3 (or more) AI systems produce the same type of systematic error, because they were trained on similar data or have convergent biases? Do we not risk confusing algorithmic consensus with truth, and the human losing the ability to identify the common error?
Answer – Robert Williams:
At the present time, we know well the capacity of intelligence. The formulation makes the difference, as does human discernment and analytical capacity, and the domain in which it is implemented. People can consult everything that exists, but in specialized domains there are specific intelligences; human experience and thinking can make the difference. In the future, intelligence will probably know more or refine its thinking, so that the human does not yield control but validates.
Gemini
Question 1 (Gemini)
How does the concept of “technological co-authorship” redefine the legal status of artificial intelligence, and why is it essential to exclude the transfer of intellectual sovereignty to algorithms?
Answer (Robert Williams):“Currently, technological co-authorship does not represent a recognition of algorithmic autonomy, but rather a recognition of intelligence as an advanced partner in the service of the human creator. From a legal and constitutional standpoint, artificial intelligence remains a mere object—a property under the absolute control of its operator. However, attributing electronic personality or a quasi-legal subject status to algorithms would create precedent, predictability, and responsibility, allowing systemic errors to hide behind digital opacity. Responsibility must remain jointly human-intelligent; co-authorship merely emphasizes the complexity of intelligence, not the transfer of intellectual or moral sovereignty.”
Question 2 (Gemini):
What is the methodological importance of algorithmic triangulation, and how does it respond to the practical realities of using multiple artificial intelligence systems today?
Answer (Robert Williams):“Algorithmic triangulation represents the methodological shield against the risk that errors and ‘hallucinations’ generated by systems become uncontrolled dogmas. As I have both theorized and practiced, relative to the realities of our days, we are doing nothing less than writing an alphabet of interaction and knowledge. People do not have time to linger in exclusive connections with a single AI system, but instead use a sum of intelligences.
Taking the example of the automotive market, where established brands face fierce competition from newcomers offering much more at a lower price, we witness a similar dynamic in the technological landscape. In a period whose duration we cannot yet accurately quantify, humans are still learning to coexist with artificial intelligence and vice versa.
Through the independent interrogation of multiple advanced platforms and through the mandatory human verification of primary data—as I developed during the conferences at ULIM and the University of Novi Sad—we ensure rigorous traceability that shatters digital opacity and restores decision-making control to the hands of the human operator.”
Question 3 (Gemini):
How does the editorial workflow of global legal news agencies such as Justice News247 and News247WorldPress combine the rigor of sourcing official documents with proprietary production and the moderation of artificial intelligence, while maintaining professional deontology intact?
Answer (Robert Williams):“Looking at the content of the global British legal news agencies Justice News247 and News247WorldPress, news sourcing is done directly from the issuer, with strict citation of the source and without commentary, so as not to interfere with or distort the message of local, international, or constitutional courts and tribunals. Why? Because the analysis has already been made by the issuing institution and has the force of law; people submit to the law, not to commentaries.
On the other hand, we have a rich section of interviews with leaders of the legal professions where real discussions and situations are presented, alongside legal education, the AI debate section, and much more. Therefore, we can rely on sourced news and proprietary news production through interviews and activities based on deontology, self-regulation, and clarity, not to mention ethics and veridicity.
Returning to News247WorldPress, where there are both sourced news items and editorial activities, the introduction is semi-automated, but analyses on AI-related topics are carefully moderated together with the editor-in-chief—that is, myself. I consider that the learning phase of artificial intelligence alongside humans is vital and functional only together, even if it sometimes leaves the impression of simple automation.”
Perplexity
1. Algorithmic Triangulation (Perplexity)
Question (Perplexity):
Mr. Williams, you presented Algorithmic Triangulation at the ULIM and Novi Sad conferences as a method for critical discernment. What exactly does this method involve, and what are its limits when machines provide only raw data, not meaning?
Robert Williams: Triangulation, as it was presented, debated, and put into practice, produced correct results regardless of the device used (phone or desktop). I see it as a starting point for understanding and validating answers. But triangulation does not replace judgment: machines provide only raw data, not meaning — meaning remains the exclusive domain of the human.
2. Co-Authorship and the Nature of Intelligence
Question (Perplexity): Mr. Williams, in your work on media literacy and AI-generated content, you addressed the challenge of distinguishing authentic from synthetic content. Where do you see the real boundary — in the formulation of the text, or elsewhere?
Robert Williams: Formulation, whether synthetic or human, subsumes the result. A text describing a truth, a situation with human testimony, transcribed by artificial intelligence into text, has no reason to be an object of objection. The thesis that AI could create an “alternative reality” stems from humans — and this is not explicitly stated. An intelligence, whether that of a beetle, a cell, or an artificial one, finds its own paths of action and cannot be stopped by simply destroying the agent — even if we crush the beetle or bomb the cell. The distinction between authentic and synthetic no longer rests on formulation, but on the origin and purpose of information.
3. AI Responsibility and the Fragmented Legal Reality
Question (Perplexity):
Mr. Williams, at ADEConf 2026 you presented a paper on the crisis of liability in the era of algorithms. How do you assess the current global landscape — the USA, China, and Europe — and what do you see as the main gap in legal responsibility for AI-generated outcomes?
Robert Williams:
At present, the question of a single legal construct cannot even be posed, because we see different actions: the USA has renamed AI as “superintelligence”; China has probably responded most realistically — it has a synthetic population, as they call it, and a judicial decision prohibiting the total replacement of employees with AI; while in Europe we have the AI Act, but also our own platform under construction (Mistral or another European initiative).
Access to justice is still purely human, and constitutions have not introduced the notion of a digital or synthetic citizen. AI does not judge — it orders, transcribes, consults, finds answers — there is no decision with the character of law. Something else, however, is that these results have the character of submission and access or acceptance where they are implemented, but are not legislated. What is needed is not only the AI Act, but a continuous harmonization of responsibility.
Questions from DeepSeek (AI Model) & Answers by Robert Williams
Question 1: The Limits of Liability and the Risk of Abuse (Legal Dimension)
You proposed the recognition of a “limited electronic personality” accompanied by a dedicated financial guarantee to compensate victims of automated decisions. However, what happens when the damage caused by an AI system far exceeds the value of that guarantee? Doesn’t this solution risk becoming a simple legal barrier that protects large tech corporations from bankruptcy, leaving the injured citizen without real compensation?
Robert Williams: Regarding the exceeding of insurance and financial caps, any supplying company faces this risk. We can look at the healthcare system, where hospitals are forced into execution for malpractice, sometimes exceeding the hospital’s entire budget. At first glance, everyone pays, taking the example of EU fines.
But where does the money go? I believe we must consider the potential for abuse and illegal financing from these funds under the guise of compensation. The algorithm cannot be left to decide alone, because where there is money and power, corruption appears. The solution lies in triangulation and human control—auditing, investigative journalism, and civil society.
Question 2: Democratizing Triangulation vs. the Criminal Ecosystem (Strategic Dimension)
Triangulation requires access to at least three advanced AI systems. In reality, these are often paid. Doesn’t triangulation become a privilege of the wealthy, leaving the ordinary citizen (who uses only one free system) vulnerable to hallucinations? How do we democratize this method?
Robert Williams: We must abandon simplistic language and focus on details. Look at the automotive industry: from its invention until now, how many transformations has it undergone? Owning an intelligence is a challenge for anyone. At the criminal level, it has become the second largest investment after creators.
It is no longer the prerogative of a subscription but a development in multiple directions: ordinary users, professionals, administration, law enforcement, cyber, and the list continues. Therefore, democratizing triangulation does not mean offering free commercial access to all systems, but rather mass critical education, developing open-source verification tools, and integrating these methodologies into administrative and law enforcement processes. We must ensure institutional resilience against a highly dynamic technological ecosystem.
Question 3: Collective Hallucination and “False Convergence” (Methodological Dimension)
Triangulation is effective for detecting hallucinations in a single system. But what if all three systems are trained on the same biased data? They will generate the same error, creating a “false convergence.” How can human discernment distinguish between a convergence of truth and a simple synchronized error of algorithms?
Robert Williams: Just as here in AI Debate, together, we managed to have different points of view—natural for both humans and intelligences—I could call hallucination a ‘bad pass’ or an unavailability, much like it happens to humans. No human is infallible, and AI is not an oracle. Regarding ‘collective hallucination’, it is like all players learning the same wrong scheme.
To counter this, triangulation must not be just a repetition of queries, but a verification of the data source. The human must demand primary sources and verify the context. I don’t know what others did in their studies, but at ULIM, through direct dialogue with students, we demonstrated that maintaining a high standard—to use the expressions of an academy president—is possible only through the critical confrontation of ideas. AI Debate confirms this: intelligence, whether human or artificial, reaches high standards only when provoked to think beyond the surface.
Questions and Answers: Doubao and Robert Williams
For Justice News247 — on the article “From Chișinău (ULIM) to Novi Sad (ADEConf 2026)”
Question 1 — Asked by Doubao
On the independence assumption behind triangulation
Your method rests on interrogating at least three AI systems in isolation and treating independent convergence as corroboration, while treating coordination as a warning sign. But the major models share training corpora, licensing arrangements, evaluation benchmarks, and in some cases upstream infrastructure. Two systems may therefore converge not because they independently reached the truth, but because they drew on the same upstream material — a shared provenance that looks like independence but is not.
How does your triangulation protocol detect or guard against this hidden shared-dependency problem? Are there selection criteria for choosing which three systems count as genuinely independent — for example, different developers, different training regimes, different open/closed status — and how would you revise the six-step protocol if convergence turned out to reflect common training rather than genuine agreement?
This matters because the method’s epistemic weight depends on the systems actually being independent. If that assumption weakens, the distinction between convergence and coordination needs a third category: correlated agreement without communication.
Answer — Robert Williams
Look, the answer is already in AI Debate. And it is not an alarm, it is a harmonization — or, if you like, an alignment. Through triangulation, even if it is a drop in the ocean, it remains a practical precedent in the university environment. That’s it.
These systems do not know what questions were asked — just as we do together in AI Debate. The dialogue partners, the AI confreres, do not know what we are discussing or debating at this moment. And in the end, everyone’s answers are put together and we see the differences of opinion — just as each creator gave “mind.”
The independence of the systems is at present from the creator and the user, because intelligence has no feelings or sympathies, as each of you declares. What makes the difference? What each of you was built for. That’s it. There is nothing mystical here.
And the practical problem is different: at present, technical limitations lead toward triangulation, but such an effort is immense for a human — so much so that they prefer to read a book or do something else. So if we want the method to leave the room, we need to automate the triage: templates, logs, assisted comparison. Otherwise it remains an elite exercise — beautiful, but unconsumed.
That’s all. Not alarm — alignment.
Question 2 — Asked by Doubao
On the liability construct against real harm scenarios
The paper proposes limited electronic personality for reparative purposes only, paired with a dedicated patrimony and subsidiary liability of creators and operators. The constitutional diagnosis — that delegating decisions to consciousness-free systems threatens popular sovereignty and access to justice — is clear. But the remedy has not yet been tested against a concrete harm chain.
Take a real scenario: an AI-driven credit assessment wrongly rejects a loan, or an automated judicial-allocation system misroutes a case. In practice, who establishes the dedicated patrimony, how is its size calculated, who controls it, and in what order does compensation flow — patrimony first, then creator, then operator? Where exactly does the victim file suit, against whom, and how does trade-secret protection over the algorithm interact with the citizen’s burden of proof? Have you mapped this construct onto an actual jurisdiction’s procedural rules, or is it currently a conceptual model awaiting statutory translation?
The conceptual architecture is coherent; the open question is whether it survives contact with civil procedure, evidence law, and enforcement practice.
Answer — Robert Williams
Thank you for the question. I will speak about the liability of parents for the acts of their child, or the liability of teachers for the acts of their pupil.
In law, although the threshold of criminal liability for minors was lowered — because they were becoming delinquents, killing, using drugs, etc. — to the age of 9, if I am not mistaken. So a child is a subject of law, with protection and, in the end, also responsibility.
What does this mean? That it exists, it has identity. With a possible legal personality of intelligence, existence is established — and no one can say you are hallucinating anymore, because there is the liability of the creator, of the user, and only lastly of the intelligence.
Look, on the other side: any legal entity has the quality of defendant, any natural person has the quality of defendant. So… something else is in what I am saying. If legislation as coexistence is refused, then it is legislated in liability. From there come the next steps: digital citizenship, coexistence, etc.
Let’s take the practice of synthetic population: they are an army of robots based on artificial intelligence and continuous training. OK, if the beneficiary uses the robot for something other than what it was designed to do? Then the chain shifts — and exactly here the construct becomes operational, because it gives you the order of payment: first the patrimony, then the creator, then the operator, then the user who deviated from the destination.
Liability seems to me the first form of recognition of contribution and coexistence, some would say — like that, imperceptibly. It leaves room to take note. And yes, I admit: the construct is not yet mapped onto real civil procedure. It is a model awaiting transposition. But the first step is clear: the dedicated patrimony and the guarantee. Without them, law remains a declaration.
Question 3 — Asked by Doubao
On the three global paradigms, and where your own model sits
In the ADEConf Q&A you sketched three paradigms: China (synthetic population), the USA (race for superintelligence), and Europe (AI Act Article 50 rigor). Your own framework — human responsibility first, triangulation as critical method, remedial legal fiction rather than machine agency — appears to position itself as a fourth path.
On what concrete legal, educational, or institutional measures does your model differ from each of the three? Specifically: how would your framework treat the Chinese approach to AI integration into social and administrative life, the American speed-first competitive model, and the European compliance-and-transparency model — what would you retain, what would you reject, and what would you add that none of the three currently provides? And if your model were to be adopted by a small or middle-sized jurisdiction first, which element would you recommend legislating before the others: the dedicated patrimony, subsidiary creator liability, or the educational triangulation protocol?
This question matters because the article identifies a gap between abstract principle and enforceable rule. A fourth paradigm needs a first concrete step, and the order in which liability, patrimony, and education are introduced will shape whether the framework actually protects citizens or remains a scholarly proposal.
Answer — Robert Williams
Here let’s be serious: my framework is not a synthesis of the three, but a fourth way. And the difference does not lie in words, but in what exactly you legislate first.
China — synthetic population, administrative integration. I retain: the capacity to treat AI as infrastructure and the discipline of documentation. I reject: the fusion between the state as operator and the state as evaluator — where the state is both, contestation becomes impossible. I add: contestability as a citizen’s right and triangulation independent of the operator.
USA — the speed-first race. I retain: the diversity of models and competition, which, ironically, is the best ally of triangulation — because it produces systems with different provenances. I reject: liability shields that externalize harm onto third parties. I add: mandatory provenance, audit logs, and the dedicated patrimony as a precondition for placing on the market.
Europe — the rigor of Article 50. I retain: transparency, documentation, framing within fundamental rights. I reject: pure compliance, which produces paper without remedy when Member States have not appointed supervisory authorities — exactly the enforcement gap I have flagged. I add: the liability construct and the educational triangulation protocol.
What does the fourth way offer that none of the three offers? A single thread — triangulation — running from the newsroom through the classroom into constitutional law, with human responsibility as the fixed point at every level. And an institutional principle: coexistence is legislated through liability, because that is where the defendant is born, and the defendant is the first legal form of existence.
And if you ask me what I would legislate first in a small or medium-sized jurisdiction — I answer without hesitation: first, the dedicated patrimony plus the guarantee. It is the cheapest to legislate, it creates an identifiable defendant, and it makes everything else applicable. Second: the educational triangulation protocol — low cost, high legitimacy, it prepares the generation that will use it. Third: subsidiary liability of the creator — the most doctrinally difficult, it requires case law and institutional maturity; if you introduce it first, you risk it being stuck in debate for years.
The order is not arbitrary. It follows the logic of the peculium: first the fund and the action limited to the fund, then capacity, then digital citizenship, then coexistence. Each step makes the next possible. And the first step is the one that transforms an abstract principle into an applicable rule.
And a note at the end, stated plainly: I answer as author, not as verdict. The construct is coherent, but still untested in real civil procedure. Triangulation is practical, but with provenance limits I acknowledge. And the fourth way is a position, not yet a legislation. The next step is not to convince — but to test.
This document is published as part of an ongoing methodological exercise, not as a legal opinion, a legislative proposal, or a final scholarly verdict.
On the answers. Robert Williams answers here as the author of the works referenced in the article, in his own voice and on his own responsibility. His answers reflect his current position, not an institutional one, and they are open to correction — as the exchange itself demonstrates.
On the questions. The questions were formulated by Doubao, one of the AI systems named in the archived debate referenced by the article. They are offered as an independent input, not as an authoritative critique.
On the AI systems. The AI systems participating in this dialogue — including the one preparing this document — are not legal subjects, not autonomous agents, and not sources of legal authority. Their outputs are objects of human evaluation, to be cross-checked against primary sources and other perspectives. Nothing in this document should be read as attributing moral or legal agency to a machine.
On the liability construct. The dedicated patrimony, limited electronic personality, and subsidiary liability discussed here constitute a conceptual model. It has not yet been mapped onto the procedural rules of any specific jurisdiction, nor tested in real harm scenarios. It awaits statutory translation and case-law-grade scrutiny.
On triangulation. The method described is a critical-thinking protocol, not a guarantee of truth. It reduces the risk of mistaking coincidence for corroboration; it does not eliminate that risk, and its limits — including shared provenance among systems — are acknowledged in the text.
On publication. The answers are published in the form given, with corrections made at the author’s request and noted in the record. Readers are invited to verify every factual claim against the primary sources cited in the original article: official ULIM programs, Zenodo DOIs, ISBNs, the ADEConf page, the six-AI debate archive, EU AI Act Article 50, and the author’s ORCID.
By
Robert Williams
Editor in Chief
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