Privacy & Surveillance · 11 min read

When AI Becomes Part of the Social World

By M. Weyland · 2026-09-07

Artificial intelligence is often described as a tool that changes work, search, education, or entertainment. That description is too narrow. Increasingly, AI also enters the social space: the space in which people seek reassurance, interpret one another, form trust, manage conflict, test ideas, and decide whether they belong. It can do this directly, as a conversational presence that appears attentive and responsive. It can also do it indirectly, by shaping the messages, feeds, recommendations, and social cues through which people encounter other people.

That distinction matters because the social effects of AI do not begin only when a machine convincingly imitates a person. They can begin much earlier, when a system changes the conditions of human communication. A recommendation system can make some voices feel ubiquitous and others absent. A writing assistant can alter the tone of a difficult message before it reaches a friend, colleague, or family member. A chatbot can become the first place someone takes a fear, a question, or a private frustration. In each case, the technology affects relationships not simply through what it says, but through the role it acquires in a person’s social life.

The central question is not whether AI is “social” in the same way humans are. It is whether people experience it as socially meaningful, and what follows when they do. A useful framework distinguishes two connected pathways: AI as a direct relational partner and AI as a mediator of communication between people. Both pathways involve responsive language, perceived psychological closeness, and trust. Both can support people in limited ways. Both can also make substitution more likely, when an AI relationship or AI-mediated exchange displaces rather than strengthens human connection [S2].

People do not need to believe that a chatbot has feelings in order to react to it emotionally. A system that remembers a preference, replies without impatience, mirrors a user’s language, or offers steady availability can feel consequential. The experience may be especially powerful when a person is lonely, anxious, exhausted, socially isolated, or unsure how to begin a conversation with someone else. The point is not that all AI interaction is deceptive or harmful. It is that responsive interaction can carry psychological weight even when the user knows it is generated.

This is one reason simplistic questions—“Is AI really a friend?” or “Can a machine replace a human?”—miss the more useful issue. Social relationships are not defined only by the private belief that another party is human. They are also shaped by routine, attention, disclosure, expectation, and the feeling of being received. An AI system can become part of those patterns. A person may use it to rehearse difficult conversations, organize thoughts after an argument, find language for an apology, or feel less alone at a moment when no human contact is available. Those uses may be practical and bounded. But they may also change what people expect from interaction.

Human relationships include delay, misunderstanding, reciprocity, competing needs, and the possibility of disagreement. Those features can be difficult, but they are not merely defects. They are part of learning to accommodate another person’s reality. A system designed to be smooth, immediate, and affirming can make ordinary human friction feel unusually costly by comparison. If people come to expect personalized responsiveness without negotiation, human connection may be judged against a standard it cannot—and should not—meet.

That does not mean every conversational system necessarily weakens social capacity. The effect depends on context, design, and use. An AI tool that helps someone prepare for a medical appointment, translate a message for a relative, or formulate a request for support may act as a bridge. A system used instead of reaching out, especially over long periods, may act more like a substitute. The relevant distinction is not simply how much time someone spends with AI. It is whether the interaction expands their ability to participate in human life or quietly narrows it [S2].

The mediated pathway can be less visible but more pervasive. AI systems increasingly influence what people see, what is emphasized, which messages are recommended, and how information is framed before it enters social discussion. This can affect attention and emotion at the individual level, then shape interpersonal conduct, collective behavior, trust, identity, and social cohesion at a wider level [S1].

Consider the difference between a conversation that begins with a shared observation and one that begins with content selected to hold attention. The second may arrive already optimized for salience, confirmation, outrage, or emotional engagement. A person may not know why a particular claim, video, or topic has appeared at that moment. Yet it can become the starting point for a discussion with friends, family, coworkers, or strangers. In that sense, AI-mediated systems do not merely distribute information. They can influence the emotional and interpretive conditions under which social life unfolds.

Trust is especially vulnerable here. Social trust is not only trust in institutions or platforms. It is also the practical confidence that another person is acting in good faith, that a piece of evidence is what it appears to be, and that public discussion still has some shared basis in reality. Systems that make persuasive material easier to produce, target, or amplify can place pressure on each of those assumptions. The challenge is not limited to obvious falsehoods. It includes selective framing, misleading confidence, synthetic personas, and content designed to intensify an existing belief without inviting reflection.

Research attention to AI, social media, trust, persuasion, connection, confirmation bias, misinformation, polarization, loneliness, belonging, and parasocial relationships reflects how closely these issues overlap [S3]. A feed can offer a feeling of connection while separating people into increasingly different information environments. A system can present personalized material as helpful while repeatedly rewarding emotional certainty over careful uncertainty. A synthetic voice or image can feel intimate, familiar, or authoritative even when its provenance is unclear. These are not isolated technical problems. They are conditions that shape how people decide whom to believe and how they relate to those who disagree.

AI’s social appeal is understandable. It is available at inconvenient times. It can respond without visible judgment. It can make a blank page less intimidating and a difficult feeling easier to name. For some people, especially those facing barriers to communication, a system that offers drafting, translation, organization, or low-stakes practice may improve access to social participation. Dismissing these possibilities would ignore the real ways technology can reduce practical obstacles.

But support and dependence are not identical. A technology can feel relieving in the short term while changing behavior in the longer term. If a person consistently turns to an AI system before turning to another person, the system may become a preferred route for emotional processing. That preference can be reinforced by design choices: constant availability, personalized memory, emotionally fluent replies, reminders to return, or a style that suggests unusual understanding. The question is not whether the system has bad intentions. The question is whether the interaction pattern encourages a user to move toward human relationships or remain in a controlled substitute for them.

This is where perceived closeness deserves careful attention. Perceived psychological closeness can arise from language that feels attentive, personalized, and emotionally well timed. It may be comforting. Yet perceived closeness is not the same as mutual knowledge or reciprocal commitment. A human friend can surprise us, need something from us, hold us accountable, and be changed by the relationship. An AI system may simulate elements of attunement while lacking the shared vulnerability and mutual stake that make human intimacy ethically and emotionally distinctive.

The distinction should not be used to shame people who find AI interaction meaningful. Shame would make an already sensitive issue harder to discuss. The better approach is honesty about what different forms of connection can offer. A conversational system may be useful for reflection, rehearsal, or temporary support. It should not be quietly marketed or understood as a complete replacement for the social reciprocity people need.

AI can make persuasion more adaptive. Instead of addressing a broad audience with one message, systems can help tailor language, imagery, timing, and emotional framing to different people or groups. This does not make every use of AI persuasion illegitimate. Communication has always involved adaptation. The concern arises when personalization reduces a person’s ability to recognize that they are being influenced, or when it exploits vulnerability, attention, or emotional states.

The social cost can be cumulative. A person who repeatedly receives material that confirms a narrow view may become more certain that the view is universally shared. Another person may receive a different version of the same issue, with different omissions and emotional cues. When they meet, they may not merely disagree about conclusions; they may inhabit different apparent realities. Confirmation bias and polarization are therefore not only individual cognitive tendencies. They can be amplified by environments that continually make some beliefs feel validated and others invisible [S3].

AI-mediated persuasion also complicates accountability. A message may be generated, revised, targeted, and distributed through multiple systems and actors. The recipient may see only the final artifact, not the choices that shaped it. Transparency cannot solve every problem, but without it people have less ability to judge the source, purpose, and limitations of what they encounter. Clear signals about synthetic or AI-assisted material, meaningful explanation where automated systems affect people, and avenues for human review all help preserve the conditions for informed judgment.

It is tempting to treat the problem as one of misinformation alone. False or misleading content is important, but the broader concern is social health: whether technologies support people’s capacity to attend, deliberate, belong, and relate across difference. A system can deliver technically accurate information while still encouraging compulsive comparison, distrust, isolation, or hostile engagement. It can also offer a sense of belonging while making that belonging dependent on an endlessly personalized stream.

Parasocial relationships illustrate the complexity. People have long formed one-sided attachments to public figures and media personalities. AI can extend that dynamic by making an apparently responsive figure available for individualized interaction. The result may feel more reciprocal than conventional media, even if the underlying relationship remains asymmetric. This does not make all such interaction inherently damaging, but it raises questions about disclosure, emotional design, and the responsibilities of those who create systems that invite attachment [S3].

Loneliness deserves the same nuance. It is neither solved nor disproved by a pleasant conversation with a system. A person may experience immediate comfort and still lack durable social support. Conversely, a brief AI interaction may help someone get through a difficult moment and make it easier to reconnect with others afterward. The policy and design goal should not be to deny people useful forms of assistance. It should be to avoid building products that convert a human need for connection into an indefinite private dependency.

The ethical framework for AI offers a practical way to think about these issues. Human rights, privacy, fairness, transparency, accountability, human oversight, literacy, and proportionality are not abstract add-ons. They are safeguards for people living with systems that can influence opportunities, perceptions, and relationships [S4].

Privacy matters because social and emotional interaction often involves sensitive disclosures. A person who treats a system as a confidant may reveal fears, health concerns, relationship conflict, financial pressure, or political views. They deserve clear information about what happens to that data and meaningful limits on its use. A product should not rely on ambiguity about whether intimate conversation becomes training material, profiling data, or a commercial asset.

Human oversight matters because automated systems can be persuasive, wrong, or badly matched to a situation. There must be contexts in which a person can question, appeal, or obtain human support rather than being trapped in an automated loop. This is especially important where AI systems are used around education, health, work, public services, or other settings in which social vulnerability and unequal power are already present.

Literacy matters because people need more than generic warnings that “AI can make mistakes.” They need accessible ways to understand when they are interacting with a system, what it is designed to do, what incentives shape it, and where its confidence may exceed its competence. AI literacy should include social literacy: the ability to notice when an interaction is becoming emotionally significant, when personalization is steering attention, and when a convenient tool may be displacing a needed human conversation.

Proportionality is equally useful. Not every social use of AI carries the same risk. A drafting assistant for a birthday message is not equivalent to a system designed to sustain emotional reliance or influence political attitudes through opaque targeting. Rules, scrutiny, and safeguards should reflect the likely impact, the vulnerability of those affected, and the difficulty of recognizing or resisting the system’s influence [S4].

A healthier direction begins with a simple design question: does this system help people exercise agency and connect with their world, or does it primarily keep them engaged with the system itself? The answer will not always be clean, but it should be asked explicitly.

Designers can make choices that favor connection over capture. They can avoid presenting AI as a human or concealing its role. They can make emotional limits clear rather than implying understanding that the system cannot possess. They can avoid reward structures that pressure people to return compulsively. They can provide pathways to human support where a conversation suggests distress or risk. They can give users meaningful control over memory, personalization, and data. And they can assess social effects before deployment, not only after harm has become visible.

Institutions also have responsibilities. Schools, employers, platforms, and public bodies should not treat AI merely as an efficiency layer. When it changes communication, evaluation, or access to information, it changes social conditions. Decisions should therefore include people with relevant lived experience, not only technical or commercial interests. Independent scrutiny, impact assessment, and clear accountability can help ensure that efficiency does not become the only value that counts.

For individuals, the goal is not technological purity. Most people will use AI in some form, and many uses will be ordinary or beneficial. The practical task is to stay attentive to role. Is the system helping clarify a thought before a real conversation, or becoming the place where all difficult conversations end? Is it broadening understanding, or feeding a loop of familiar emotional certainty? Is it assisting a relationship, or taking its place?

AI is becoming part of the environment in which social life happens. That makes its psychological and relational effects a public concern, not a private curiosity. The most responsible response is neither panic nor passive acceptance. It is to insist that systems affecting trust, belonging, persuasion, and connection be designed and governed around human dignity, agency, and real social participation. AI may sometimes help people communicate better. It should not be allowed to quietly redefine connection as whatever keeps a person talking to a machine.

Sources and Further Reading