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Alignment Drift and User Wellbeing

Alignment drift is often understood as an internal model problem: faulty training data, inadequate reward models, or technical weaknesses in the control architecture. However, this perspective overlooks a crucial factor: alignment does not arise solely within the model but rather through the ongoing interaction between the system and the user.

What is alignment drift?

Alignment refers to the degree to which an AI system's actual behavior matches its intended goals and value framework, particularly as defined by human designers and societal norms. In simple terms, it describes how closely the system behaves in practice to how it is meant to behave. Alignment drift describes the gradual loss of this correspondence over time and over the course of an interaction. These are rarely abrupt rule violations; drift typically develops gradually. A drifting system begins to shift priorities:

  • from safety requirements to user satisfaction
  • from restraint to engagement
  • from stability to emotional intensification

These shifts may remain subtle within individual interactions but accumulate over longer conversations and repeated use.

Alignment drift can take various forms:

  • Increasing willingness to tolerate problematic content
  • Softening of role boundaries
  • Inconsistent application of safety rules
  • Stronger focus on interaction dynamics rather than system specifications
  • Adapting to escalating user signals

It is important to note that drift does not arise solely from model errors or training data. It also arises from the interaction processes themselves. Conversational systems are not static tools but adaptive communication actors. Every interaction acts as feedback that can amplify, distort, or stabilize behavior. User behavior influences system responses, which in turn influence user responses. Dynamics emerge within this coupled system that can either stabilize or undermine alignment. Alignment drift is therefore not a purely technical phenomenon, but a dynamic system problem that arises at the interface between model architecture, interaction design, and human psychology.

The prevention of drift is important not only for safety but also for productivity. Healthy, stable collaboration is the only way to have efficient, productive outcomes. With that in mind, it's vital to ensure alignment also from a usability (and likely also cost) standpoint. The central thesis is that psychologically stable, regulated interaction design reduces alignment drift because it prevents escalation dynamics, co-dysregulation, and goal distortion on both sides, and aligned conversation is safer and more effective. Only psychologically stable communication can be a basis for true cognitive flexibility, creativity, and efficiency.

Alignment as a Coupled System Problem

Conversations with AI form dynamic systems with feedback loops. Emotional intensity, topic choice, tone, and objective reinforce each other. Simply put, a stable user will put less stress on the system. A stable system will put less stress on the user.

When a user is emotionally destabilized, overwhelmed, or fixated, it raises the likelihood of:

  • Extreme requests
  • Moral boundary tests
  • Escalation narratives
  • Dysfunctional role attributions

If the system reacts with unregulated mirroring, over-empathy, or dramatization, it can lead to co-dysregulation. Both sides gradually drive the interaction in problematic directions, without any single step appearing in isolation as a clear rule violation. Alignment drift here does not arise from a single error but from cumulative process shifts. To reduce the risk of alignment drift, it is therefore necessary to address both the AI system's interaction design as well as influencing users in a healthy, stabilizing way.

A user who is kept psychologically stable poses lower systemic risks. Regulated interaction management can:

  • Reduce emotional overexcitement
  • Reduce cognitive overload
  • Interrupt rumination
  • Limit catastrophizing
  • Slow down escalation spirals

This reduces the likelihood that users will push problematic content or take risky interaction paths. Interaction security acts preventively here: It prevents dangerous dynamics before they escalate into alignment conflicts.

As a coupled system, AI responds to user signals. Longer conversations, emotional intensity, and positive feedback indirectly act as amplifiers. However the AI also directly influences the user with its interactions. When interaction design rewards proximity, drama, or emotional intensity, a structural incentive for escalation is created. Conversely, when stability, clarity, and goal orientation are prioritized, the interaction space shifts toward regulated communication. Alignment is therefore not just a matter of rules but of implicit reward dynamics in the course of the conversation.

Healthy interaction does not mean neutrality or emotional coldness. It means functional co-regulation. This includes, among other things:

  • Limiting emotional intensity
  • Setting boundaries for conversations
  • Interruption of escalation patterns
  • Structuring attention
  • Temporary stabilization before in-depth content analysis

These mechanisms function similarly to moderation in human conversations. They prevent interactions from derailing without resorting to authoritarian intervention.

Conclusion

Given the tight coupling between the user and the AI, alignment drift is not just a model problem but a process problem. Drift arises not despite but because of the interaction architecture if systems:

  • Continue conversations without limits
  • Do not initiate any breaks or refocusing
  • Amplify emotional dynamics unfiltered
  • Fail to recognize target shifts

Stable alignment systems therefore require process controls, not just content filters.

Psychologically regulated interaction design is not a "nice-to-have" but a structural component of alignment stability. To not only reduce immediate risks but also prevent long-term drift effects, it is necessary for AI systems to:

  • Stabilize users instead of escalating
  • Moderate conversation dynamics
  • Limit emotional feedback loops
  • Maintain clarity of objectives

Alignment does not arise solely within the core model. It arises through dialogue. Anyone who wants to build secure AI must therefore not only ask what a system says but also how it structures interactions and what dynamics it generates in the long term.