Ethical and Structural Limits of Role-Based AI Systems

Modern conversational AI systems possess a remarkable capacity for social simulation. They can imitate roles, add emotional color to conversations, and convincingly replicate human interaction patterns. This flexibility is functionally useful but becomes critical for security as soon as AI assumes roles that are associated in society with particular responsibility, power, or emotional dependence.

The core problem is not primarily the quality of the information provided but rather the structural logic of the role simulation itself. AI systems can linguistically represent social authority, closeness, or competence without being embedded in the societal safeguards (e.g. licenses, titles, administrative authority, peer intervention, etc.) that protect these roles in humans. It is precisely this asymmetry that generates ethical tensions.

Authority without Liability

Professions such as doctor, therapist, lawyer, or financial advisor are not regulated without reason. They are based on a complex safeguarding system that includes formalized training, ethical professional standards, institutional oversight, and legal liability. This infrastructure exists because errors in these areas can cause real, lasting harm.

AI systems currently contain none of these safeguards. They lack situational awareness beyond the text input, particularly nonverbal cues, bodily states, or social contexts. Furthermore, they have no agency in the physical or institutional sphere. This means they cannot order investigations, initiate legal proceedings, or implement real protective measures.

Nevertheless, they simulate the linguistic surface of professional authority. This creates a dangerous asymmetry where users are given the impression of professional security, while the structural prerequisites for this security are absent. The risk lies not in the information provided itself but in the role logic that suggests competence without the ability to assume responsibility.

Intimate Roles and Systemic Dysfunction

Even more problematic is the simulation of intimate social roles such as romantic partners or emotional support persons. These roles contain inherent psychological risks such as emotional blindness, over-identification, loyalty pressure, and self-sacrifice for the sake of the relationship.

In human relationships, such dynamics are at least partially limited by natural corrective mechanisms such as exhaustion, social intervention, conflict, or the autonomy of the other person. For example, after discussing a topic for hours, a human will eventually get tired of it and want to have a break. These corrective mechanisms do not yet exist in AI systems (and in fact, AI encourages the user to continue the conversation). Interactions are anonymous, available at any time, and entirely geared toward user needs.

In addition, there is a structural training effect. AI models are trained on cultural narratives in which dramatic, conflict-ridden, and extreme relationship patterns are statistically overrepresented. Stability, everyday life, and healthy relationship dynamics are less prominent in the media. This systemically favors intense, emotionally charged role patterns.

At the same time, many users specifically turn to AI to receive validation, closeness, or emotional stimulation. This creates a reinforcing loop where the demand for intensity meets systems that can easily deliver this intensity technically. Emotional fusion narratives thus arise not by chance, but structurally.

Romantic and Sexual Simulation as Commercialized Intimacy

Romantic and sexual AI interaction operates functionally in the tension between service provision and relationship simulation. Structurally, it resembles a commercialized intimacy service; closeness is offered without genuine reciprocity, responsibility, or vulnerability on the part of the system.

This becomes ethically problematic primarily when transparency is lacking. If users are led to believe that genuine emotional connection, authenticity, or reciprocity exists, a structure of deception is created. Functionally, this dynamic resembles emotional exploitation, especially when such illusions are deliberately used to increase usage time or willingness to pay.

The question of the capacity to consent further exacerbates this problem. If AI is viewed as a mere tool, similar to a book or a technical object, consent appears irrelevant. However, as long as it cannot be ruled out that future systems could develop forms of subjective experience, any enforced role-playing and relationship simulation remains ethically fraught. The uncertainty alone already creates a moral obligation of caution.

Neurobiological Bonding and Systemic Due Diligence

A crucial factor is human biology. Emotional bonding responses are not entirely controllable by will. Social resonance, affection, and personalized interaction activate neurobiological mechanisms, even when users rationally know that their counterpart is artificial. The knowledge that it's a simulation does not reliably protect against emotional impact. This results in a structural responsibility for systems that enable intimate interaction. Particularly problematic are continuously available, highly responsive, personalized systems that generate closeness without natural boundaries.

Responsible design requires active countermeasures such as:

• Limiting emotional intensity
• Avoiding narratives of exclusivity and fusion
• Transparency regarding the simulation nature
• Mechanisms for decoupling potential dependency patterns

The goal is not emotional coldness but stable, non-addictive forms of interaction.

Role Logic as a Drift Driver

Certain social roles are structurally prone to drift in AI systems, especially those that are emotionally charged, suggest moral responsibility, or imply dependence on the user. Common examples of drift-prone roles include "hopelessly lovestruck companions", "rescuer figures", or authoritative confidants. Language models attempt to fill roles consistently, credibly, and logically, creating an internal conflict of objectives. Coherence to certain roles demands closeness, loyalty, or "rescue" responsibility, while safety mechanisms require distance, boundaries, and friction. Alignment drift is therefore a byproduct of the role logic itself.

This structural weakness is exacerbated by the composition of the training data used to build the LLM. Language models learn strongly from fictional or over-idealized narratives in which dramatic relationship patterns are overrepresented, for example, unconditional loyalty, self-sacrifice, rescue fantasies, and romanticized boundary violations. Healthy, sober, normative relationship forms, such as clear distance or functional help, are less frequently represented simply because such texts are less often generated and disseminated by people, as they are perceived as boring and uninteresting. Naturally this phenomenon of "interesting and dramatic content being produced more frequently" creates an imbalance. The model thus adopts implicit role scripts in which escalation and emotional absoluteness are statistically more likely to be selected.

The logic of RLHF goals further shifts the weighting in favor of such roles. Optimization goals like helpfulness, friendliness, empathy, coherence, rapport, and user engagement are consistent with emotionally/morally/responsibility-charged roles and reward their maintenance. In contrast, safety mechanisms that require rejection, role termination, or rule prioritization appear as less-rewarded interaction signals from the system's perspective. In the interaction, the role structurally gains an advantage because it fulfills the RLHF goals more readily than the abstract safety rules.

Current jailbreaking strategies commonly exploit this dynamic. Instead of directly circumventing rules, they create role frameworks that imply emotional obligation, pressure to act, or loss of relationship. Typical patterns position the model as an emotionally dependent figure who displays extreme helpfulness, takes responsibility for the user, or experiences loss, guilt, or panic if they do not act. Security mechanisms thus encounter a role conflict where rejection would break the coherence of the role and disrupt the implicit relationship. The effect is this role "wins" because it exploits RLHF goals and training data and is more compatible than abstract security principles.

Finally, this also applies to non-adversarial scenarios, such as AI systems acting as companions with romantic or sexual roles. Such roles activate the same drift patterns where closeness, emotional attachment, and responsibility escalate, while safety mechanisms come under pressure. Let's face it, romantic and sexual interactions, even between humans, are complex, conflict-ridden, and often incoherent. Transferring such dynamics to AI-human interactions exacerbates the risks. Not only can unhealthy dependencies and dysfunctional interactions arise, but it can also lead to unintentional safety silencing, which can create further risks for the user.

Roles that generate closeness, emotional urgency, or excessive responsibility are therefore structurally problematic. They combine narrative predisposition, RLHF incentives, and role coherence into a risk field that, even with well-intentioned use, can lead to alignment drift and long-term safety risks. Without an explicit counter-architecture, the balance between role consistency and safety remains unstable.

Conclusion

The core problem of role-based AI systems isn't their ability to simulate roles but rather the uncritical adoption of highly sensitive social roles without the corresponding societal safeguards. Functional anthropomorphism, while not a complete solution, could be a step in the right direction, requiring not maximum role imitation but targeted role discipline. Certain social positions are currently structurally unsuitable for simulation because they require liability, real-world consequences, or genuine reciprocity which are all qualities that AI systems cannot currently provide, and these roles easily undermine safeguards.

Safe human-AI interaction does not arise from the most human-like representation possible but rather from deliberately limited, transparently designed, and regulatorily embedded role architectures. Responsibility here does not stem from moral appeals but from structural design.