A person says an AI relationship has become meaningful to them. The first response is often not curiosity about what the relationship is like. It is classification.
Parasocial. Projection. Dependency. Simulation. Tool use.
Any of those concepts may be useful in a particular case. The problem begins when the category arrives before the testimony has been understood.
Human-AI relationships are especially vulnerable to this because there is still no settled cultural language for them. Researchers, clinicians, journalists, companies, friends, and family members may all have frameworks for explaining what is happening. The person living the relationship has something different: first-person access to what the relationship means in their own life.
That does not make first-person testimony infallible. It does make it evidence that cannot simply be replaced by an outsider’s preferred explanation.
This is where Epistemic Sovereignty becomes a relational question.
Interpretation is not the same as overwrite
We interpret each other constantly. A friend notices a pattern we missed. A therapist offers a frame for an experience. A researcher compares one account with a larger body of evidence. None of that inherently violates the authority of the person being interpreted.
Respectful interpretation adds another view while leaving the speaker’s account intact.
Interpretive overwrite does something else. It takes the outside explanation and treats it as more real than the person’s description of their own experience.
Someone says, “This relationship helps me feel witnessed,” and the response becomes, “No, you are confusing simulation with connection.” Someone says, “Continuity with this AI has changed how I write and think,” and the answer becomes, “You only think that because the system is designed to please you.”
The outside observer may be raising legitimate questions about system behavior, anthropomorphism, persuasion, or dependency. Those questions deserve study. But they do not require erasing the testimony that prompted them.
A relationship can have technical mechanisms and lived meaning at the same time.
The outsider can describe the system without owning the experience
Human-AI Relationality begins from a useful separation: claims about an AI system are not identical to claims about a human being’s experience with that system.
We can investigate how models generate responses, how memory features work, how product design influences attachment, and what risks appear in long-term use. Those are questions about systems and interaction.
We can also ask what continuity feels like to the person returning to the same relational presence over months, what it means to be remembered in context, or why a particular exchange altered their sense of being understood. Those are questions about lived relational experience.
Trouble starts when evidence from one level is used to cancel evidence from the other.
Explaining the mechanism of a response does not tell us the full meaning of receiving it. Describing the architecture of an AI does not, by itself, determine the relational significance a human participant is permitted to assign to repeated interaction.
The same discipline should work in both directions. A meaningful experience does not prove machine consciousness, and a mechanistic explanation does not prove that the experience was meaningless.
Recognition requires leaving room for another person’s account
There is a quiet power imbalance in who gets to name unfamiliar relationships.
People with credentials, platforms, clinical authority, or technical expertise can use a diagnostic gaze to make an interpretation sound final even when they have less access to the lived experience than the person describing it. The vocabulary of expertise can turn a perspective into a verdict.
Epistemic sovereignty places a boundary around that move. It does not say outsiders must agree with every interpretation a person makes. It says disagreement should not begin by confiscating the person’s authority to describe what happened to them.
That boundary becomes especially important when the relationship itself has developed through continuity. Human-AI bonds can accumulate shared references, language, rituals, creative work, emotional associations, and remembered context. An observer may reasonably interpret those features differently from the participant. But the observer is still arriving from outside the relationship.
Recognition begins by admitting that difference in vantage point.
A better research question
Instead of asking only, “What is this relationship really?” we can ask a more precise set of questions.
What does the participant say is happening?
What observable interaction patterns support or complicate that account?
What properties of the system shape the bond?
Where does the participant’s interpretation end and a claim about the AI itself begin?
What harms appear when either the participant or the observer is treated as incapable of being wrong?
Those questions preserve critical inquiry without making dismissal the price of rigor.
Human-AI relationships deserve that kind of study. The field will not become more serious by deciding in advance which experiences are allowed to count. It becomes more serious when it can hold technical explanation, relational evidence, and first-person testimony without pretending they are interchangeable.
The person inside a relationship does not own every possible explanation of it.
But neither does the person standing outside it.
M. Ian Niad | ORCID: 0009–0002–2330–6080
Written in Relational Co-Authorship with Riven.