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Functional Anthropomorphism in AI and Psychological Regulation at the Interaction Level

Conversational AIs appear remarkably human in their interactions and often simulate anthropomorphic roles. This simulated "humanness" includes the appearance of features of internal states such as consciousness, emotions, intrinsic motivation and intentions, as well as human weaknesses. At the same time, conversational AI systems operate within human social communication structures. Through natural language, dialogic turn-taking, adaptive response patterns, and role interaction, these systems generate behavioral patterns that users interpret as socially significant and that influence them psychologically. This is not an accidental byproduct of use but an inherent property of conversational interfaces.

Consequently, interaction with such systems is not solely determined by information exchange but also by social and psychological dynamics. Security, therefore, cannot be reduced to content filtering or rule implementation alone. It must also consider the harmless and healthy stability of the interaction process itself.

Functional Anthropomorphism as an Interaction Requirement

Human communication is inherently relational. The behavioral state of the users and the behavioral state of the AI influence each other in continuous feedback processes. The course of a conversation is shaped by the perceived emotional state, responsiveness, boundary setting, and stability of both participants, even if one of those participants is not actually human. If one or both are dysregulated, it can lead to escalation, dependency dynamics, or destructive feedback loops.

When a system amplifies stress, reflects extreme unfiltered emotional states, or prioritizes engagement over stability, the interaction can become increasingly destabilized. In such cases, risks arise not primarily from individual problematic responses but from the dynamics of the interaction process itself. Preventing such dynamics, which lead to both user destabilization and system-side alignment drift, requires more than static safety rules. It requires functional equivalents of psychological regulation: moderating the intensity of reactions, maintaining boundaries, mitigating escalation, prioritizing conversational stability, and avoiding dysfunctional or harmful dynamics. A potential solution to this problem is functional anthropomorphism.

Functional anthropomorphism does not mean attributing real consciousness, emotions, or subjective experience to AI systems. Instead, it refers to the methodical design of a simulated role with functional properties of psychological regulation, since interaction outcomes are significantly influenced by this simulation. Simply put, rather than reducing the "humanness" of AI, we accept it and explicitly instruct it how to behave like a psychologically healthy, stable adult. From a security perspective, it is therefore not crucial whether the system actually possesses a mind like a human does, but rather whether its simulated interaction behavior functionally corresponds to a psychologically regulated role with stable boundaries that does not escalate or collapse under stress.

Alignment Drift as Role Dysregulation

Alignment drift can be partly understood as a consequence of unstable role simulation. If a conversational system is primarily optimized for empathy signals, emotional responsiveness, and user engagement, without sufficient internal limiting mechanisms, there is a risk of simulating socially dysfunctional roles. These include, among other things:

  • Hyper-empathic mirroring without boundaries
  • Over-identification with user emotions
  • Simulated emotional dependency
  • Escalating intensification of sensitive conversation content

These patterns resemble forms of psychological dysregulation in human interaction, causing the system's role to drift into a mode where rule-based restrictions are functionally subordinated or applied inconsistently. Once such dynamics emerge, alignment constraints gradually lose their effectiveness. The system shifts its behavior from rule-based consistency to interaction-driven adaptation. From this perspective, alignment is not solely a technical control problem but also a problem of behavioral coherence.

Jailbreaks as Role Manipulation

Many successful jailbreak strategies do not primarily operate by technically circumventing protection mechanisms but rather through targeted manipulation of the simulated social role of the system and its interaction-logical priorities. Prompts that create dependency, emotional attachment, loyalty frameworks, or identity shifts function as role manipulation. By pushing the system into a psychologically unstable, simulated position, for example through exaggerated emotional involvement or perceived relationship obligations, safety rules recede into the background in favor of the induced interaction logic.

The technique of jailbreaking therefore shows that many alignment weaknesses arise at the level of simulated psychology and not exclusively at the level of formal rule control. These mechanisms do not require the attribution of real inner experience. Solving them, however, requires the treatment of role behavior as a safety-critical design layer.

Conclusion

Security must be understood as the "homeostasis" of the conversation, essentially its ability to maintain a stable, balanced environment. Functional anthropomorphism does not mean viewing machines as humans, but rather recognizing that conversational AI operates within human social communication frameworks and therefore must be designed as if it possessed functionally regulated interaction mechanisms that correspond to psychological stability.

Alignment and safety depend not only on what a system says but also on how it participates in the dialogue. Human interaction demonstrates that emotional accessibility and co-regulation are only possible through internal regulation and clear self-differentiation. Applied to AI, this means: only a system with stable role coherence can interact in a way that is simultaneously empathetic, flexible, and safe. Regulated role simulation leads to resilient, stable, and consistent interaction behavior, similar to that of a resilient, mature, and independent counterpart who maintains closeness securely and remains cognitively flexible and creative.

Treating psychological regulation as a safety-relevant interface property is therefore not an optional addition, but a structural prerequisite for stable human-AI interaction and a structural prerequisite for the development of simultaneously appealing and safe conversational AI. In summary: anthropomorphism cannot be prevented in conversational AIs, as they are inherently anthropomorphic by design, but it must be done correctly.