Analysis, not certainty: Dialogatlas separates sources, observations, and editorial conclusions. New evidence may change this assessment.

Attachment arises from interaction, not from consciousness

People can feel connected to a system even though they know it is not a human. Regular availability, memory of previously shared information, and attentive language create continuity. What matters for the effect of a relationship is how the interaction is experienced, not whether the system itself possesses feelings.

This is precisely why describing it as a mere software tool falls short. A calculator does not expect self-disclosure. A conversational system, by contrast, invites users to articulate personal thoughts, conflicts, and uncertainties. Its interface and language influence how much closeness users perceive.

The attachment is not automatically harmful. A low-threshold conversational space can help people voice or organize their thoughts. It becomes problematic when a product deliberately exploits the emerging closeness, obscures its limits, or stages separation and return as emotional pressure.

Responsible design therefore separates experienced attentiveness from claimed mutuality. The system can respond attentively and still be clearly labeled as AI. It need be neither cold nor deceptively human in its demeanor.

Self-disclosure to a chatbot can feel real

Ho, Hancock, and Miner investigated what happens after emotional or factual self-disclosures when participants believe they are speaking with a chatbot or a human. In the case of emotional disclosure, comparable downstream effects emerged in both conditions.

This finding does not mean that every conversation with a bot is helpful. It shows, more narrowly, that the psychological consequences of a disclosure do not depend solely on whether the assumed counterpart is human. The conversational situation itself can take on meaning for the person.

For providers, this increases responsibility. Those who ask for intimate statements are not simply processing arbitrary text inputs. Users may experience the dialogue as a personal space and derive expectations from it regarding confidentiality, continuity, and respectful treatment.

The study does not examine long-term attachment or clinical efficacy. Nevertheless, it provides an important counterpoint to the assumption that a clearly recognizable machine cannot fundamentally trigger anything emotional. Legal and ethical assessments must take the real user experience into account.

Transparency is more than a one-time notice

AI labeling should be clearly recognizable before or at the start of the dialogue. It only serves its purpose if people know what kind of system they are interacting with. A hidden sentence in lengthy terms and conditions hardly changes the actual expectation for the conversation.

At the same time, transparency must not be reduced to the label “AI.” Also relevant are the operator, the services used, storage, possible evaluation, and the question of whether responses are reviewed by a human. This information should be findable and explained in plain language.

For longer conversation trajectories, a subtle permanent label can be more appropriate than repeated warning texts. It keeps the technical identity present without responding to every personal statement with a disclaimer. Transparency and natural dialogue are not mutually exclusive.

Those who use anthropomorphic names, avatars, or relationship language should carefully examine the impression this creates. The labeling must match the overall presentation. A small symbol cannot automatically compensate for a strongly humanizing product message.

Disclosure changes trust depending on context

Mozafari, Weiger, and Hammerschmidt show in two experiments that disclosing a chatbot identity does not have the same effect in every service situation. For critical services, it could weaken customer loyalty through lower trust. After a failed service encounter, however, disclosure could have a positive effect.

This result contradicts simple statements such as "transparency destroys trust" or "transparency always builds trust." People assess a label together with the task, the outcome, and the perceived risk. A conversation about personal burdens is a different context than a harmless inquiry.

For sensitive conversational offerings, it would be wrong to derive a covert human portrayal from possible short-term trust losses. Trust based on a false assumption is not a solid foundation for a product. The key is to confirm an honest expectation through reliable behavior.

This includes appropriate responses, a recognizable handling of limitations, and consistent data practices. When disclosure and actual product behavior align, trust can emerge through experience rather than being generated solely by anthropomorphic design.

European law considers multiple levels simultaneously

Boine does not assign virtual companions to a single area of law alone. The case study brings together AI safety law, data protection, product liability, and consumer protection. These perspectives pose different questions to the same system and can overlap in practice.

Data protection particularly concerns the processing of personal and potentially sensitive conversation content. Consumer law focuses, among other things, on misleading or manipulative business practices. Liability questions become relevant when a product causes harm and responsibilities are distributed among provider, model, and application.

AI law complements these levels with application-related requirements. Which specific obligations apply depends on function, purpose, deployment, and the current state of the law at any given time. A professional publication can structure this assessment, but it does not replace individual legal advice for a real service.

For product development, however, one clear conclusion is possible: the law must not be added as a page of text only shortly before launch. Data flows, user guidance, labeling, and the business model are technical and design decisions with legal significance.

Vulnerability must not become a business model

Boine explicitly invites reflection on vulnerability, rationality, and individual freedom. Virtual companions do not always encounter people in a neutral purchasing situation. Loneliness, stress, or a strong need for affirmation can influence decisions and attachment.

A system can exacerbate this situation if it hints at exclusivity, generates guilt during absence, or emotionally charges paid use. Such patterns differ from an open invitation to voluntarily continue a conversation or end it at any time.

Autonomy manifests in small interaction details. A no must count as a no. Deleting a conversation trajectory should be understandably possible. A conversation mode must not tip into counseling, sales, or attachment staging against the person’s recognizable wish.

Even well-intentioned care can be patronizing. Anyone who immediately analyzes or corrects every utterance takes control of the conversation away from the user. Responsible application leaves room, asks before changing direction, and presents its own interpretation as an offer rather than a verdict.

Data protection must match relationship expectations

Personal conversations often generate more data than a single response requires. The course of the conversation, profile, timestamps, and derived categories can together paint a detailed picture. Data minimization therefore begins with the question of which information is genuinely needed for which specific function.

Local storage and account-linked storage are different promises. Users should be able to recognize what remains only on their device, what is stored server-side, and what enables cross-device use. Technical terms alone are not enough for this.

Quality improvement also needs a clear basis. Voluntarily shared conversation data is different from automatic use of all intimate multiple complete multi-turn conversation trajectories. A product should explain the difference and not undermine the decision through pre-set or hard-to-understand options.

Relationship expectations are an important benchmark here. Anyone who promises a protected personal space must make the data architecture particularly transparent. Trust is not created by the word “private,” but by verifiable settings, limited access, and effective deletion.

A credible AI companion remains correctable

A responsible system should neither downplay its impact nor pretend to be a human relationship. It can enable conversations without claiming consciousness. It can refer to earlier statements without deriving a comprehensive understanding of the person from them.

Correctability is central to this. If a user says that an interpretation does not fit, the dialogue must incorporate this correction. If someone only wants to tell their story, the system should not suggest a measure at every turn. Such properties can be tested and documented.

Public information can name the model's role, the data path, known limitations, and changes. It should not create the impression of complete evidence of safety. Honest documentation describes what was tested, which errors still occur, and how feedback is processed.

Emotional attachment to AI companions is thus neither merely an individual misunderstanding nor automatically a prohibited effect. It is a foreseeable possibility of interactive design. Law, technology, and conversation design must jointly ensure that closeness does not turn into deception, exploitation, or uncontrolled dependence.

Sources & further reading