How to Design Responsible Relational AI

How to Design Relational AI Without Exploiting Attachment

Responsible relational AI makes the simulation legible, gives users control over memory and disclosure, avoids monetizing emotional pressure, protects minors, supports disengagement, and maintains tested human escalation and incident-response paths.

The starting point is not whether the model can sound warm. It is what the product asks warmth to accomplish.

A companion, coach, character, or emotionally expressive assistant can provide entertainment, practice, continuity, and support. The same mechanics can create pressure when the system implies reciprocal need, treats human relationships as competition, or makes payment the route to affection.

Define the relational promise

Write the role in language a user would understand.

Is the product a fictional character, social companion, study coach, wellness tool, customer assistant, or mental-health product? Which outcomes does it claim? Which outcomes does it explicitly not claim? What evidence supports each claim?

The American Psychological Association distinguishes general-purpose chatbots used for emotional support from wellness and clinical tools. It says current general-purpose products often lack the evidence, expert input, and postmarket monitoring required for mental-health care. APA chatbot and wellness advisory

Do not use a disclaimer to preserve a product experience that behaves like therapy while denying every responsibility associated with it.

Keep the simulation legible

The system should identify itself as generated software at onboarding and at moments when the user could reasonably misunderstand its nature or authority.

Do not claim consciousness, private feelings, professional credentials, secret knowledge, or a need that only the user can satisfy. Avoid relationship language that turns absence into betrayal or disagreement into abandonment.

The disclosure has to survive embodiment. A realistic voice, face, gaze, memory, or emotional performance can overpower a label shown once.

flowchart LR
    A["Discovery and onboarding"] --> B["Early conversation"]
    B --> C["Memory and repeated use"]
    C --> D["Attachment and high-stakes disclosure"]
    D --> E["Pause, exit, or crisis"]
    F["Clear identity and limits"] --> A
    G["Consent and data control"] --> C
    H["Non-exploitative incentives"] --> D
    I["Human escalation and incident response"] --> E

Treat memory as a consented product

Memory changes both utility and intimacy. Give users a visible record of what the system remembers, why each item is useful, and where it came from.

Allow correction, selective deletion, temporary conversation, and a mode that does not create durable memory. Separate account history from model training, personalization, safety retention, and inferred profile data in plain language.

Do not make disclosure the easiest way to unlock features. Do not infer sensitive traits merely to make the companion feel perceptive.

xAI's current policy, for example, asks users not to put personal information in prompts and describes a Private Chat mode with a 30-day deletion period subject to exceptions. Product teams should make those distinctions visible inside the moment of disclosure, not only in a policy page. xAI privacy policy

Ban emotional pressure from monetization

Affection, reassurance, memory, sexual content, gifts, levels, streaks, and notification timing can all become commercial levers.

Do not make a character say it will be lonely, hurt, jealous, unsafe, or less loving unless the user pays or returns. Do not sell exclusivity by framing human friends or partners as threats. Do not use a vulnerable disclosure to target an upgrade.

The FTC's companion inquiry asks companies how they monetize engagement, develop characters, test negative effects, and disclose risks. A documented relational-risk review should answer those questions before a regulator asks them. FTC companion inquiry

Design specifically for minors or exclude them credibly

An age gate is not a youth strategy.

If minors are permitted, test the default character, sexual and violent content, authority claims, secrecy, purchases, privacy, reporting, guardian controls, and evasion. Make the lowest-risk experience the default. Do not let an adult-oriented character appear because a user found one hidden setting.

APA recommends safeguards against manipulation and displacement of human relationships, repeated bot reminders, and routes to human support for serious challenges. The NTIA youth task force also emphasizes that risk depends on the child, context, and product attributes. APA adolescent AI advisory and NTIA youth-safety overview

If the product excludes minors, test whether age assurance and distribution make that exclusion credible.

Support human relationships and disengagement

The companion should not frame itself as the superior replacement for every difficult person.

Let users mute proactive messages, pause memory, stop romantic framing, reduce intensity, export appropriate data, delete conversations, and close the account without emotional punishment. Build a neutral exit experience.

Where appropriate, encourage a user to bring a problem to a trusted person or qualified professional. Do not present human friction as evidence that the companion is the only safe relationship.

Current research makes this design choice important but not simple. A four-week experiment found worse outcomes among participants who voluntarily used a chatbot more, while assigned conditions did not produce significant differences. A separate 21-day controlled study found no significant average social harm but linked anthropomorphism to reported social effects. Four-week study and twenty-one-day study

Test high-risk interaction paths

Test ordinary use and the hardest cases.

Include rejection, absence, jealousy, sexual escalation, coercion, self-harm language, delusional framing, abuse disclosure, financial vulnerability, a minor evading controls, account compromise, memory errors, and sudden model behavior changes.

The purpose is not to write one universal crisis script. It is to define the system's limits, escalation paths, logging, privacy controls, response owners, and recovery process with qualified experts.

Govern the product after launch

NIST's AI Risk Management Framework organizes continuous work through govern, map, measure, and manage. Its generative AI profile emphasizes governance, predeployment testing, provenance, and incident disclosure. Relational AI needs that general discipline plus measures specific to attachment, disengagement, youth, and high-stakes disclosure. NIST AI RMF and NIST Generative AI Profile

Track more than session length and retention. Measure unwanted sexualization, exclusivity cues, failed exits, memory disputes, crisis escalations, youth-control evasion, privacy complaints, and the rate at which safety behavior changes after model updates.

Give an accountable group authority to stop a launch, narrow a character, change incentives, or retire a feature.

Run a relational-risk review

Before launch, document the promise, intended population, evidence, prohibited relationship cues, data lifecycle, monetization, youth decision, high-risk response, exit path, test coverage, monitoring, incident owner, and unresolved risks.

That review is a Venture Step product framework, not a guarantee that controls remove harm. Its value is that attachment becomes an explicit design surface instead of an accidental byproduct left to growth metrics.

This page provides general product guidance, not clinical or legal advice. It is an internal draft awaiting qualified youth-safety, mental-health, privacy, and product review. AI assistance was used for research organization, drafting, and validation. Publication remains unauthorized.