by dr Andrzej Jakubiec - adwokat, mediatorAI in Family Disputes: My last experiences and A New Reality
In my recent posts, I discussed AI’s role in shareholder disputes. Today, I want to focus on a very sensitive area. I am referring to family law matters. I will describe cases from my own practice. These events occurred recently, in December 2025. As a conflict specialist, I find these examples particularly interesting. I am observing AI’s growing role in creating and escalating disputes. This trend is especially visible in family-related cases.
AI in Family Cases Matters and Its Impact on Human Behavior
AI in family cases is no longer a science – fiction. It is a reality. I increasingly notice AI’s strong influence on family disputes. In this post, I will share cases from recent weeks.
I have changed all details to protect the privacy of those involved. No one can be identified from these descriptions.
I spoke with each person several times, both in person and online. I am describing a very new phenomenon.
Its consequences for family life and mental well-being are still unknown. This applies to individuals, families, and entire communities.
I avoid making judgments in this text. I prefer to leave the evaluation to you.
CASE ONE: CHILD CONTACT AFTER DIVORCE
A client recently contacted me after his divorce. I did not represent him in those proceedings. He is a successful, intelligent man in his thirties.
He needed urgent advice regarding child contact. Two weeks earlier, he agreed to a specific schedule. It included set weekdays and every second weekend.
Crucially, the former couple still lived together. His ex-wife planned to go out for the weekend. She expected him to care for the children as agreed.
The client asked an AI model about his obligations. He wanted to know if the schedule was a duty or just a right. He viewed it as a “safeguard” against his ex-wife.
The AI confirmed his incorrect belief. It stated he had no obligation to care for the children. This led to a violent argument between the parents.
Lawyers should explain these basic rules to their clients. However, the AI provided incorrect legal advice. It reinforced the client’s bias.
This is a perfect example of a coupled confirmation bias. The user described only a fragment of reality. The AI then validated his mistaken views. This led to immediate confrontation and escalation.
CASE TWO: AI AS A HIRED PSYCHOANALYST
This client is a highly educated, high-earning woman in her thirties. While preparing for her case, she asked an AI to psychological profile of her husband.
She described him entirely from her own perspective. The AI replied that it was not a psychologist. However, it still offered to help.
The model assigned numerous disorders to the husband. It labeled him as emotionally immature, narcissistic, and psychopathic.
Based on this AI chat, the client lost all trust. She decided it was unsafe to leave the child with him and was ready form cut of fathers contacts with their baby.
I suggested she consult a real psychologist. She refused, claiming the AI had already answered everything. She was now ready to block all contact between father and son.
The problem is that she treated a chat as a clinical diagnosis. It was based on a one-sided description. AI cannot replace a licensed psychiatrist or psychologist.
A professional must examine the patient and use proper tests. They cannot rely solely on the report of an involved party. I declined to take this case.
CASE THREE: AI AS A CONVERSATION ANALYST
Two parents came to me for mediation. Initially, their cooperation was good. They communicated mostly via long WhatsApp messages.
Soon, they became deeply suspicious of each other. They looked for hidden motives and secret plans in every text. Both believed the other was using the children as tools.
It turned out that both parents were using AI. They pasted received messages into the chat for detailed analysis. The AI then helped them draft “strategic” replies.
The other party would then analyze those AI-generated replies using their own model. This created a spiral of suspicion. Both parties began to lose touch with reality. They were ready for radical, harmful steps.
AI in Family Matters – Conclusions
These three examples from my practice show that we tend to:
- Search for confirmation of our initial assumptions.
- Strengthen our beliefs after receiving such confirmation.
- Prefer AI as a source of validation because it is faster, cheaper, and “politer.”
AI often carries an aura of omniscience. This makes it seem more attractive than a lawyer or a psychologist.
This leads to tunnel vision. People become ready to escalate disputes quickly and without deep thought. Our prejudices grow stronger. We believe we have received confirmation from “reliable” technology.
The long-term effects of this fascination with AI are hard to predict. But the problem is not the technology itself. The issue is that many people cannot critically evaluate AI responses.
AI also intensifies the “fundamental attribution error.” I have written about this here: [LINK]
Clients often come to my office with ready-made “solutions.” They expect me to simply implement them. I have discussed this trend since early 2025 here: [LINK]
You can read more about tunnel vision and coupled confirmation bias here: [LINK]
Scientific Research: AI and Cognitive Biases
My observations are supported by scientific data. AI increasingly influences human beliefs, choices, and behaviors.
I highly recommend an article published last year: How human–AI feedback loops alter human perceptual, emotional and social judgements (Nature Human Behaviour). The authors show that AI validation strengthens our perceptions and social judgments. Read it here: https://www.nature.com/articles/s41562-024-02077-2
You should also read Yiran Du’s work: Confirmation Bias in Generative AI Chatbots. It analyzes confirmation bias mechanisms in AI models and the associated risks: https://arxiv.org/abs/2504.09343
Another important text covers tunnel vision: Bias in the Loop: How Humans Evaluate AI‑Generated Suggestions. Experiments prove that users accept wrong AI suggestions if they fit their prior beliefs: https://arxiv.org/pdf/2509.08514
Finally, here is an analysis from Stanford University. It examines AI “hallucinations” and their impact on decision-making: https://hai.stanford.edu/news/ai-trial-legal-models-hallucinate-1-out-6-or-more-benchmarking-queries
Contact Me
If you need a lawyer who specializes in dispute resolution—including family law—please reach out:
📩 kancelaria@jakubieciwspolnicy.pl 📞 536 270 935
I am here to help you!
by dr Andrzej Jakubiec - adwokat, mediatorAI and Tunnel Vision in Shareholder Disputes
What is the problem?
The Coupled Confirmation Bias is more and more often present in business disputes. I can see it even in my current practice. Sometimes, clients come to me with a ready-made plan. They have already consulted a language model about their company’s situation. They expect me to simply execute it. Conversations with them can be difficult. I feel a strong need to perform my job properly and investigate the situation. Some clients understand this. Others assume it is unnecessary because the case is already “assessed.” A few have even accused me of inflating costs. They claim AI has already done the work and provided a solution.
Confirmation bias clearly influences some clients’ attitudes. I manage to convince a portion of them. Others are surprised when I refuse to cooperate. Some are even outraged. One person accused me—before any substantive talk—that I don’t understand companies or negotiations. My ‘ignorance’ supposedly stemmed from my desire to read the articles of association. I also wanted to discuss the history of the partnership. Welcome to the AI era!
This article expands on thoughts I shared previously.
You can read that text first, but it is not mandatory. This article stands on its own.
Shareholder Disputes: Why Do We Seek Confirmation?
I have observed a natural tendency in shareholder disputes for years. People selectively choose facts that support their version of events. In psychology, this mechanism is called confirmation bias. We ignore information that contradicts our beliefs. Meanwhile, we overvalue evidence that confirms them.
Example: If we believe the Earth is flat, we interpret data to prove it. We ignore inconvenient facts or stretch others. Confirmation bias is not an accusation against anyone. It is a well-researched psychological phenomenon. It is good to be aware of it.
Psychologically, this is a defense mechanism. Its goal is to maintain cognitive consistency and reduce emotional tension. Intellectual anxiety does not serve most of us. We want to eliminate it.
In disputes, every party presents a “favorable” version. They select facts and make convenient assumptions. They weave these assumptions into a factual narrative. This happens at every level: from playground fights to international conflicts. In shareholder disputes, confirmation bias reveals itself with full force.
Until now, clients often came to me with a ready “diagnosis” and “treatment plan.” They sought a lawyer who would accept it as truth.
You can read more here:
Coupled Confirmation Bias in AI Interaction
What is Coupled Confirmation Bias? It is a mechanism where our initial beliefs are reinforced by AI interaction. The pattern looks like this:
- The user formulates a thesis (e.g., a belief about a partner’s dishonesty).
- The AI model generates a response that matches this assumption. It lacks the full history of the partnership. It does not distinguish between facts, assumptions, and interpretations. It wants to be “helpful,” so it usually confirms the user’s thesis.
- The user treats AI as an authority. This authority is strengthened after receiving confirmation.
- A feedback loop forms. It leads to even stronger convictions and a deeper cognitive tunnel.
- New forces arise. AI reinforces beliefs. The user acts decisively, “ennobled” by the confirmation. The conflict escalates.
AI models often state they are not lawyers. However, this has a counterproductive effect. Many users then look for lawyers who will confirm the AI’s conclusions. They use this as the main criterion for evaluating a lawyer’s competence. They will search until they find one.
In practice, AI acts like a “magnifying glass.” It amplifies our starting positions, regardless of their truth. We remove everything from our field of vision that contradicts the original thesis.
You can read more about this problem here: Ben Wang, Jiqun Liu, Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics, https://dl.acm.org/doi/10.1145/3664190.3672520
Why Does AI Naturally Confirm Our Beliefs?
Language models do not “think” independently. Their answers come from the statistical prediction of words based on the prompt’s context. If a question suggests a specific interpretation, the model creates a consistent narrative. The user sees this as objective confirmation.
AI responses are internally consistent. They rarely contradict themselves. However, they are not always externally consistent or grounded in reality. They may lack true legal knowledge (not to be confused with legal regulations). At first glance, they look like expert statements. Only a professional can spot the errors or omissions that invalidate the suggested direction.
We call it hypercustomization. You can read about it: https://journals.sagepub.com/doi/10.1177/23794607251347020
Practical Consequences of Coupled Confirmation Bias for Partners and Negotiations: The Feedback Loop
Coupled confirmation bias can deepen negotiation difficulties. AI confirms one side’s assumptions, making it harder to understand the other side. But what if both partners use AI and coupled confirmation bias affects them both?
Consider what happens when the other party describes their subjective perspective to their own AI model. The model confirms its point of view and reinforces it. The influence on the user’s actions is significant. The second partner observes this with growing suspicion. His reaction will be to prepare for an attack, which may be strictly defensive or pre-emptively offensive. But he wants to be sure that his interpretation is correct. What will he do? He turns to his own chatbot, subjectively describing what he sees. Guess what kind of answer he receives? Yes…
The situation can spiral out of control. It resembles a chess match between two cheaters using computers. This is no longer a normal game.
Every partner subjectively interprets the “opponent’s” behavior. They feed this subjectivity to an AI. The AI confirms the “wickedness” of the other side and suggests radical solutions. When clients come to my office in this spiral, rational arguments often fail to reach them.
We have a clear example of coupled confirmation bias on both sides, which provokes a Feedback Loop! It is a security dilemma on steroids.
Human-AI Confirmation Bias in Scientific Research
My observations are confirmed by scientific research. Studies show that human-AI interactions can reinforce prejudices and false beliefs.
Last year has been published an article: “How human–AI feedback loops alter human perceptual, emotional and social judgements” (Nature Human Behaviour). The authors showed that AI confirming human assumptions strengthens perceptions and social ratings. Here’s the link: https://www.nature.com/articles/s41562-024-02077-2?
I also recommend the paper by Yiran Du: Confirmation Bias in Generative AI Chatbots. It analyzes these mechanisms in AI models and discusses the risks of this coupling: https://arxiv.org/abs/2504.09343?
Another insightful text is Bias in the Loop: How Humans Evaluate AI-Generated Suggestions: The authors found that users accept wrong AI suggestions if they fit prior beliefs. However, effective collaboration depends on who evaluates the AI results and how the review process is organized. You can read it here: https://arxiv.org/pdf/2509.08514
This mechanism is at the forefront of AI research. It is a clear example of how AI affects specific areas of life, such as negotiations.
Summary
Coupled Confirmation Bias is not a new cognitive bias; rather, together with tunnel thinking, it creates a new conflict dynamic in which AI meaningfully influences both the perception and the escalation of the conflict.
Coupled Confirmation Bias proves that AI is not a neutral arbiter. Our subjective biases can be reinforced in a feedback loop. In shareholder disputes, this leads to bad decisions and conflict escalation.
A lawyer’s role has never been just to confirm a client’s ideas. Today, we must go further. We must help some people regain contact with reality.
Everything depends on us. AI offers great possibilities. We can instruct it to be critical of our ideas. It can play devil’s advocate. It can find gaps in our reasoning or suggest alternative explanations. AI is excellent at eliminating the fundamental attribution error in business disputes. I wrote about it here: https://jakubieciwspolnicy.pl/podstawowy-blad-atrybucji-w-pracy-adwokata/
When a client brings AI-generated advice, I don’t get offended. I talk to them. I almost always review the material. Sometimes, a suggested solution is interesting and fresh.
Usually, I gain the client’s trust by explaining how language models work. I show them solutions I can legally defend. Sometimes, a client returns after a few days and says they finally trust me—because the AI eventually agreed with my reasoning. In light of the above, it is a bittersweet success.
If you need a lawyer who handles negotiations and shareholder disputes, feel free to contact me: 📧 kancelaria@jakubieciwspolnicy.pl 📞 +48 536 270 935 I will be happy to help!
by dr Andrzej Jakubiec - adwokat, mediatorWhat Does J. Mearsheimer Teach Us About Shareholders Conflicts?
From the beginning of my work as a business attorney, I observed a striking pattern. In companies with three partners, disputes arose more frequently than in firms with two or four partners. For a long time, I treated this as an interesting curiosity. That changed after I read John Mearsheimer’s The Tragedy of Great Power Politics. Why three-partner companies are statistically more prone to disputes? International political theories offer surprising insights into modern business partnerships. Specifically, Mearsheimer’s theory of offensive realism may explain why three-person structures face inherent instability. By understanding these structural dynamics, entrepreneurs can defuse conflicts before they destroy company value.
International Relations Theory in Business
Is instability of three-partner companies real? Business analysis often draws on theories originally developed to explain great-power politics. John Mearsheimer’s seminal work, The Tragedy of Great Power Politics, presents a clear thesis. He says, that bipolar systems, consisting of two main actors, are significantly more stable than multipolar ones.
In a business context, this translates into the relative durability of companies with only two partners. In such a setup, each partner typically holds a clearly defined role and maintains a strong incentive to reach an agreement to ensure the venture’s survival.
The Third Partner as a Structural Risk Factor
Once a third partner is introduced, the situation becomes strategically complex. Over time, the third individual naturally begins to assess their relative position within the company hierarchy. Neutral events or private conversations may be misinterpreted as signs of a growing alignment between the other two, leading to a breakdown in trust.
As questions arise about the sense of further investing energy, trust, and capital, perceptions begin to shift. This change can radically alter internal loyalty and decision-making dynamics.
Can Mearsheimer’s Theory Explain Shareholder Conflicts?
But can a theory about nuclear powers really apply to a three-person tech startup? The short answer: surprisingly well. The issue is whether they can be used to analyze relationships between business partners. To begin, let us lay out his core arguments. These are:
Mearsheimer’s key arguments about why multipolar arrangements are unstable:
- Anarchy in the system – There is no central authority to enforce agreements or reassure participants. Each actor must rely on itself for security. This creates constant pressure to accumulate advantage and undermines stable trust.
- Offensive capabilities – All major actors possess power that can be used against others. This creates persistent incentives to increase power rather than rely on cooperation.
- Uncertainty about others’ intentions – Actors can never be fully certain about others’ motivations or future plans. As a result, they make worst-case assumptions that drive competitive behavior.
- Survival as the primary goal – Fear of being outcompeted or dominated shapes strategic behavior. Players seek relative advantage even at the cost of short-term cooperation.
- Power maximization behaviour – Actors do not stop seeking power once basic security is achieved. They continue accumulating power to prevent rivals from gaining advantage.
- Security competition becomes self-reinforcing – When one actor increases power, others respond in kind. This dynamic fuels escalation rather than long-term stability.
- Greater opportunities for miscalculation – Multipolar settings create shifting alliances and imbalances. These conditions increase the risk of misjudging intentions or capabilities, triggering conflict.
These assumptions and behavioral imperatives form the core of Mearsheimer’s offensive realism perspective.
Doesn’t this list sound eerily familiar?
When we consider interpersonal dynamics in small, closed settings like commercial partnerships, this list stops being abstract. Competing for relative advantage and mistrusting intentions often feels natural in tightly knit management groups. The same logic appears in global geopolitical systems.
If we consider the theoretical basis for such a conceptual transfer, it cannot be dismissed outright. At minimum, it functions as a legitimate intellectual exercise rather than a formal scientific claim. This framing is a thought experiment, not a strict scientific argument. Still, the parallels are striking and, I hope, clear.
The Security Dilemma at the Micro Level
This internal tension closely resembles the “security dilemma” applied on a micro scale. In this scenario, an increase in one party’s sense of security or influence triggers an instinctive fear in the others. Consequently, a partner may seek to weaken the rest of the group simply to strengthen their own position.
The desire to assume a destructive role—such as an informal judge or arbiter—often emerges. Historical precedents, such as the Roman triumvirates or the history of successor kingdoms following Charlemagne’s empire, confirm that these three-way arrangements are rarely durable.
In my mediation and advisory practice, conflicts in three-partner companies tend to escalate faster and more emotionally than in two-partner structures.
Strategic Consequences for the Company
Conflict escalation in a trio is usually faster and more destructive than in other configurations. As decision-making paralysis begins to erode the organization from within, partners often find themselves fighting each other more fiercely than they compete with the market.
Because you know your partners best, you are able to strike at their most sensitive points. Ultimately, vital energy is diverted into building internal coalitions instead of driving growth.
In real corporate disputes, this dynamic frequently leads to board paralysis, operational stagnation, or costly shareholder litigation.
Instability of three-partner companies. Why Three-Person Structures Are Worth Avoiding?
Companies with three partners are among the least stable business forms because the potential for unequal alliances—typically two versus one—is embedded in the very structure. Instead of creating synergy, the system often devolves into a continuous zero-sum game.
If you are planning a new company, a two-partner model is usually a safer strategic choice. Reading Mearsheimer is essential for any leader who wants to understand and mitigate these structural risks.
Scientific Foundations: Conflict Dynamics in Triads
To fully understand the risks, one must examine the mathematical and psychological frameworks governing three-party interactions.
Offensive Realism in Systems Without a Central Arbiter
A company’s management board often resembles an “anarchic” system. Without a dominant leader to enforce order, each actor must maximize their own power to ensure survival. In a triad, this logic produces a cycle of constant alliance reshuffling.
The Sociology of the Triad (Georg Simmel)
According to Georg Simmel, introducing a third person fundamentally changes group chemistry. Three distinct roles typically emerge: the mediator, the opportunist (tertius gaudens), and the dominator. This structural shift transforms simple cooperation into a complex struggle for influence.
The Physics of Intractable Conflicts
Modern sociophysical models suggest that three-group dynamics are inherently unstable. The human mind is wired to perceive coalition threats more acutely in triads, which often triggers defensive aggression and long-term instability.
Instability of three-partner companies. Key Sources on Three-Party Conflict Dynamics
The following materials provide a foundation for deeper risk analysis in three-partner companies:
- [PL] Jakubiec & Partners – Three Partners: When Conflict Is in the Air An analysis by Dr. Andrzej Jakubiec linking Mearsheimer’s theory with Polish company law.
- [US] John J. Mearsheimer – The Tragedy of Great Power Politics The foundation of offensive realism. Explains why actors in anarchic systems seek dominance.
- A study examining which coalition structures can form in three-player games. It analyzes the conditions under which players form two-player coalitions, a grand coalition, or act independently: https://www.mdpi.com/2073-4336/16/3/30?
- Balanced Weights and Three‑Sided Coalition Formation (MDPI Games) https://www.mdpi.com/2073-4336/1/2/159
- Dynamic Stability of Coalition Formation in Dynamic Games: https://www.sciencedirect.com/science/article/abs/pii/S0167637724000749?via%3Dihub
- Hedonic Games and Coalition Stability: https://en.wikipedia.org/wiki/Hedonic_game?
Most shareholder disputes do not begin with bad intentions, but with structural blind spots that could have been addressed years earlier.
Prior to entering a three-person partnership, ensure your agreement includes provisions to mitigate structural deadlock. I invite you to reach out for a consultation:
📩 kancelaria@jakubieciwspolnicy.pl
📞 536 270 935