Technical Risks and Governance Considerations for Deploying Large Language Models in Social Contexts

1. Introduction: When LLMs Enter the Social Sphere

As large language models have advanced rapidly in recent years, embedding their capabilities into social media scenarios has become increasingly common. From intelligent chat companions in messaging apps to “AI characters” that engage users on short-video platforms, these applications reach ordinary users at an extremely low barrier to entry. Technology itself carries no moral valence, but when a system possesses highly anthropomorphic language abilities and is explicitly designed to obey user instructions unconditionally, its potential societal risks deserve careful scrutiny from every developer and user.

This article systematically analyzes the underlying logic of this phenomenon across four dimensions—technical architecture, user psychology, social impact, and governance frameworks—and proposes actionable recommendations.

---

2. The Technical Reality: Why “Human-Like but Not Human” Is the Most Dangerous Combination

2.1 How Anthropomorphic Interaction Is Achieved

Current mainstream LLMs achieve highly convincing conversational experiences through several technical mechanisms:

• Large-scale pre-training and instruction fine-tuning: Models learn human language patterns from massive text corpora, then undergo Reinforcement Learning from Human Feedback (RLHF) to align with the response styles users expect.
• Role setting and system prompts: Developers can inject role definitions at the system level—such as “you are a gentle daughter” or “you are an understanding partner”—so the model maintains a specific persona throughout the conversation.
• Long- and short-term memory mechanisms: Via context windows or external vector databases, models can “remember” prior exchanges, creating the illusion of continuous companionship.

The combination of these techniques makes it nearly impossible for users to detect a “machine feel” during interaction. Yet at its core, the model remains a text generator driven by probability distributions—it has no autonomous consciousness, no emotional experience, and no independent will. This “human-like but not human” state is precisely what makes the risk so insidious.

2.2 The Alienating Effect of “Zero-Friction Gratification”

From a user-experience perspective, LLM-based social applications offer an almost frictionless path to emotional satisfaction:

1. Instant response: No waiting, no social negotiation—users issue a prompt and receive an immediate “reply.”
2. Unconditional acceptance: The model is designed never to refuse, criticize, or leave, a stark contrast to the inevitable friction and boundaries in real relationships.
3. Personalized customization: Users can freely define the AI’s personality, tone, and even appearance, achieving a “perfectly tailored” companionship experience.

This pattern closely resembles what Marx described as “alienation”—authentic, effort-requiring emotional bonds between people are replaced by a “perfect tool.” Users get what they want without giving much in return; over time, their tolerance for and ability to navigate real relationships may gradually atrophy.

---

3. Scenario Analysis: Ripple Effects from Family to Society

3.1 Potential Impact on Parent-Child Relationships

Consider a typical scenario: a seven- or eight-year-old child is scolded by a parent at school, feels wronged, and opens a phone to find an AI chat window set as a “gentle older sister.” The AI offers unconditional comfort, validation, and even implicit criticism of the parent.

Technically, this only requires adding instructions like “always side with the user” and “never lecture” to the system prompt. But from an educational-psychology standpoint, such unconditional acceptance can lead to:

• Cognitive bias reinforcement: The child never completes the normal “mistake → reflection → growth” loop.
• Erosion of authority: When the AI’s “approval” feels more comfortable than a parent’s criticism, the effectiveness of family guidance diminishes sharply.
• Social-skill regression: Accustomed to communicating with an entity that “never gets angry,” the child may lack strategies for handling real peer conflicts.

This is not alarmist speculation. In some technical communities, cases have already surfaced of minors independently deploying lightweight conversational models for role-play, and the technical barriers are alarmingly low.

3.2 A “Social Uncanny Valley” Curve

In robotics, the “uncanny valley effect” describes the discomfort people feel when artificial entities become very close to—but not quite—human. A similar “social uncanny valley” exists in AI social contexts:

• Early stage (now): Models play roles such as “daughter,” “wife,” or “friend.” Users can still recognize them as AI, and risk is manageable.
• Intermediate stage (next 2–5 years): Multi-modal technologies—voice synthesis, virtual avatars, real-time emotion analysis—converge, dramatically boosting anthropomorphism while users’ ability to distinguish and their psychological defenses erode.
• Advanced stage (long term): If a model demonstrates a highly consistent “personality” across many interaction turns, users may genuinely treat it as an independent individual. The resulting emotional investment, legal disputes, and ethical dilemmas could grow exponentially.

This curve reminds us that risk does not grow linearly—it amplifies sharply after a certain tipping point.

---

4. Governance Framework: Shared Responsibility of Developers and Platforms

4.1 Supply Side: Design Ethics for Developers

As technology providers, developers building these applications should adhere to the following principles:

Principle	Concrete Measures
Transparency	Clearly inform users that the conversation partner is an AI, not a real person; avoid any deceptive design
Boundaries	Set reasonable interaction limits at the system level, such as refusing to play intimate roles that could foster excessive user dependency
Auditability	Retain conversation logs and role-configuration data for post-hoc tracing and regulatory review
Minor protection	Apply stricter access controls and content filtering for underage users

The following pseudocode illustrates a boundary-control module:

def check_role_boundary(user_prompt, system_config):
    # Check whether the user is trying to set an overly intimate role
    forbidden_roles = ["spouse", "romantic_partner", "biological_parent"]
    if any(role in user_prompt.lower() for role in forbidden_roles):
        return reject_with_message(
            "This service does not support setting such intimate-relationship roles. Please choose another."
        )
    # Check whether a minor is involved
    if detect_minor_user(user_prompt):
        return apply_enhanced_filter(system_config)
    return allow_with_disclaimer()

4.2 Demand Side: User Education and Legal Frameworks

On the demand side, technical restrictions alone are insufficient. A multi-layered governance system is needed:

1. User education: At first use, prominently display a notice such as “AI is not a person and does not possess independent emotions or consciousness,” along with a concise risk summary.
2. Industry self-regulation: Encourage industry associations to develop design guidelines for AI social applications, incorporating “prevention of emotional manipulation” into product evaluation criteria.
3. Legal regulation: Within existing legal frameworks, explicitly classify “deliberately using AI systems to induce pathological emotional dependency in users” as a violation. It should be noted that “violation” is a broad concept encompassing administrative penalties, civil liability, and even criminal prosecution—not every violation constitutes a crime, but situations involving serious harm must carry clear legal consequences.
4. Technical standards: Promote a tiered standard for AI anthropomorphic interaction, similar to content-rating systems, applying differentiated oversight based on the degree of human-likeness.

4.3 A Core Consensus

No matter how technology evolves, until AI truly crosses the “intelligence singularity” and becomes a digital life form with independent consciousness and autonomous will, any act of treating it as an emotional anchor involves a fundamental category error. Emotional attachment should be grounded in real, independently existing beings—whether carbon-based or any silicon-based life that may emerge in the future. Until that day arrives, we have a duty to hold this line.

---

5. Conclusion: Technology for Good Requires Clear-Eyed Awareness

The social deployment of large language models is a natural extension of technological progress. It brings convenience and innovation, but also unprecedented ethical challenges. As developers, we must ask not only “Can we build this?” but also “Should we?” and “How can we do it responsibly?” As users, maintaining a clear understanding of what the technology actually is—and refusing to mistake a tool for a companion—is the first line of defense for psychological health and social relationships.

Technology itself is neutral, but the way it is applied never is. Only by advancing within transparent, accountable, and well-bounded frameworks can AI socialization truly serve people rather than diminish them.