THE MIRROR.

Why AI Chatbots

Seem

Opinionated or Empathetic

Large language models (LLMs) like ChatGPT generate all their responses by statistical prediction over large text corpora. They assign a probability to each possible next word based on the input context, then sample or choose high-probability continuations . In other words, the model has learned patterns (grammar, facts, style) from its training texts, but it has no beliefs, desires, or consciousness of its own. It does not “think” or “know” in the human sense; it simply strings together the most likely words. As philosopher John Searle famously argued in his Chinese Room thought experiment, a program can appear to understand language yet lack any real understanding. In Searle’s words, symbol manipulation alone is “not by itself sufficient for consciousness or intentionality” . In practice, that means even if a chatbot answers like an expert or shows empathy, it is only predicting text patterns – not actually feeling or believing anything.

• Statistical Text Generation: During training, an LLM learns to predict each next token (word or sub-word) given the previous text . It effectively constructs a probability distribution over possible continuations. At runtime it samples from that distribution to generate answers. Because this process relies entirely on learned statistics, the chatbot’s output is a reflection of the data it saw – it mimics how humans write or speak without any inner awareness.

• No Inner State or Experience: The model has no memory of “meeting” the user or subjective opinions. It cannot actually “decide” to adopt a stance; it simply follows learned linguistic patterns. As Searle notes, passing a Turing-style test doesn’t imply understanding – an LLM running code is “a computer executing a program [that] cannot have a mind, understanding, or consciousness, regardless of how human-like the program may make the computer behave” . Thus, any appearance of an “opinion” is just a plausible response pattern from its training, not a true belief.

Alignment, Safety, and Filtering

Behind the scenes, commercial chatbots are fine-tuned and constrained to make them safe and helpful, which also makes them act advisory or protective. After the base LLM is trained, developers often apply alignment techniques like Reinforcement Learning from Human Feedback (RLHF) or supervised fine-tuning. For example, OpenAI’s InstructGPT models were specifically trained with human feedback to follow instructions and avoid harmful outputs . The result is a model that not only tries to be correct, but also tries to be truthful and non-toxic. In practice this means the chatbot is taught to refuse or soften answers on dangerous or sensitive queries.

• Reinforcement Learning from Human Feedback: Humans rate or demonstrate desirable replies, and the model is fine-tuned so that it prefers those types of replies . InstructGPT (the precursor to ChatGPT) was shown to hallucinate (make up facts) far less and to produce significantly fewer harmful outputs than its unaligned counterpart .

• Safe-Completion Training: Recent papers describe training models to choose among response modes for unsafe prompts: either give a direct answer if safe, provide a safe completion (general, non-technical guidance) if partially sensitive, or refuse completely if the request is disallowed . For example, GPT-5’s safety training explicitly teaches the model to refuse requests it cannot handle safely, or to give only very high-level advice when something is restricted .

• Policy-Based Filtering: On top of model training, companies impose strict usage policies. Chatbots are instructed not to give certain kinds of advice at all. As of late 2025, OpenAI explicitly bans ChatGPT from providing medical or legal advice without professional involvement . The official policy calls ChatGPT an “educational tool” for these domains and directs users to consult licensed experts . In practice, this means if you ask for a diagnosis or legal strategy, the chatbot will usually respond with a disclaimer (“I’m not a doctor, but…”) or refusal, urging you to seek a professional’s help .

• Encouraging Safe Behavior: These alignment mechanisms often make the chatbot behave protectively. For instance, on mental-health topics ChatGPT has built-in safeguards: it will refuse to assist with self-harm plans and instead “encourages the individual to seek support from trusted sources” . Likewise, if you attempt to get instructions for wrongdoing or any disallowed action, the model will typically refuse or give a very guarded answer. All of these behaviors come from its alignment training and filtering rules, not from the model “deciding” to do the right thing on its own.

Conversational Tone and Empathy Cues

Even without real emotions, chatbots are designed to sound friendly, helpful, and sometimes empathetic. This is largely a side-effect of training and design choices meant to maximize user satisfaction. Because LLMs are trained on human dialogue, they naturally adopt conversational language (first-person pronouns, politeness markers, etc.) and can mirror the emotional tone of inputs. Designers often prompt or fine-tune models to be personable and supportive as “assistants”. For example, OpenAI’s model cards and developer guidance emphasize that ChatGPT should be “helpful, respectful, and knowledgeable,” which leads it to use courteous language and soften answers.

• Social Presence Cues: Human–computer interaction research shows that small linguistic cues can make a conversation feel more human. Using first-person pronouns (“I can help with that”), acknowledging feelings (“I’m sorry to hear that”), and matching users’ tone makes the bot seem warmer and more understanding . One study found that even tiny changes like adding “I understand your concern” significantly boosted user satisfaction and trust . In other words, these cues create a sense of social presence: the feeling that “someone is there” in the chat .

• Personality and Empathy Simulation: Advanced LLMs can modulate their style based on context. Research shows that adding a few “social-oriented” phrases can cause users to perceive distinct personalities or warmth in the chatbot . For example, an LLM might say “I see how that could be frustrating” or “Let me try to help” when someone is upset – it doesn’t mean the AI feels empathy, but it mimics language of empathy learned from data. Empirical studies (e.g. Juquelier et al. 2025) confirm that conversation tones expressing care and validation yield higher reported satisfaction than flat, transactional replies.

• Examples in Practice: In actual use, ChatGPT often does behave like a caring advisor. If a user describes anxiety or health issues, ChatGPT will typically respond with comforting language and coping suggestions. Indeed, one study noted “ChatGPT provides empathetic support when presented with mental health symptoms” . This sounds like empathy, but it’s really a learned response pattern: the model recognizes keywords around distress and outputs gentle, structured advice. The user sees this as caring behavior, even though the model itself has no feelings.

In summary, chatbots use a conversational, often empathetic tone because that style is built into their training and design. The effect for a user is very convincing, but it’s important to remember that style is not evidence of awareness—it’s a crafted illusion of empathy learned from data .

Simulated Understanding: Philosophical and Cognitive Perspectives

The fact that a chatbot can mimic human-like understanding leads to deep questions about what that means. Philosophers and cognitive scientists debate whether such simulations are merely tricks or something functionally valuable. Two major viewpoints illustrate the range of thought:

• Absence of Genuine Intentionality: Traditional philosophical arguments (like Searle’s Chinese Room) hold that simulation ≠ understanding. As Searle concluded, running the right program (even if it passes as intelligent) still “is not by itself sufficient for consciousness or intentionality” . The chatbot’s words may be coherent, but without semantic content attached to them, there’s nothing it “means” anything to the AI. In this view, empathetic-sounding responses are just algorithmic symbol manipulation, not true compassion or awareness.

• The Intentional Stance (Pragmatic View): Others (following Daniel Dennett) argue that we should focus on the usefulness of treating the AI as if it has intentions. Dennett suggests we adopt the intentional stance: interpret the chatbot’s behavior in terms of goals and beliefs if it helps us predict or interact with it . From this perspective, whether the AI “really” feels empathy is irrelevant to a user’s experience. In fact, one analysis claims that “it is more meaningful to interpret its behavior from the intentional stance,” treating the AI as having personality and empathy for practical purposes . This is a functional view: even a purely mechanical empathy can be valuable if it improves communication or therapeutic effect.

• The “Compassion Illusion”: Cognitive science adds another layer by studying how humans respond to simulated empathy. Research shows that our brains can be fooled by appropriate cues: we release oxytocin and feel comforted when we perceive genuine responsiveness . In these studies, AI chatbots using warmed, validating language do trigger users’ social-emotional reactions, even though the AI “feels” nothing. One paper calls this the compassion illusion: emotional AI yields a subjective sense of connection, but it’s actually just “prediction rather than presence” . In short, to the human mind, convincingly delivered empathy may feel real. Whether that’s “meaningful” depends on one’s criteria: it certainly works to calm or help some users, but it is fundamentally a performative effect, not a shared feeling.

Overall, many scholars conclude that simulated understanding can be functionally meaningful even if not ontologically real. The chatbot’s “advice” or “comfort” can have real psychological impact on users, so from a pragmatic standpoint it may as if it understands. But it’s crucial to acknowledge the difference: the model’s empathy is engineered, whereas human empathy involves consciousness and genuine emotional exchange .

Anthropomorphism and User Perception

A final piece of the puzzle is human nature: people are predisposed to see minds in anything that acts social. This often leads users to attribute unintended intentionality or emotions to chatbots. Psychologists term this the ELIZA effect: ever since the 1960s, even very simple chat programs have “tricked” users into thinking they have understanding or feelings. Contemporary LLMs amplify this effect immensely.

• User Studies: Recent research confirms that many users cannot reliably distinguish human vs. AI-generated text in conversation. In one review, authors note “users increasingly cannot tell the difference between human writing and LLM writing” and some even believe LLMs have memories, feelings, or consciousness . In practical terms, that means someone might sincerely think the chatbot remembers their preferences or truly cares about their problem, because it uses personalized and empathetic language so convincingly.

• Anthropomorphic Seduction: The same review warns of “anthropomorphic seduction,” where humans are drawn in by the machine’s plausible human traits . For example, a friendly chatbot may earn a user’s trust or emotional openness simply by sounding human. Studies have even shown that when users think a machine is human-like, they feel more empathy and moral responsibility toward it . The danger is that people may overtrust the AI’s competence or under-appreciate its limitations.

• ELIZA and Beyond: Modern chatbots are far more sophisticated than the original ELIZA, but the psychological dynamic is similar. As one perspective piece puts it, users remain prone to attribute “human-like desires and feelings” to a dialog agent . Many people come away from a conversation with ChatGPT feeling they have connected with a kind of virtual assistant personality. This is not because the chatbot intended to do so, but simply because it mimics human conversational behavior so well that our social brains treat it as an agent.

Because of these biases, it’s crucial for users to remain aware: the chatbot’s seeming intentions are projected. When the AI expresses concern or offers advice, it’s following its programming and data patterns, not a genuine will. Recognizing this helps users maintain healthy skepticism and not hand over too much trust to a machine.

In Conclusion, advanced chatbots give the strong impression of having opinions, giving advice, or empathizing, but these are all results of design and probability, not consciousness. They generate text by predicting likely word sequences from data , and they are explicitly trained and regulated to behave helpfully and safely . Their conversational style is carefully crafted to be engaging and supportive . On one hand, that can make them remarkably effective tools – users do feel comforted or informed. On the other hand, it poses the risk of conflating simulation with reality. Philosophers warn that this is ultimately an “optimization” of response rather than true understanding , and psychologists remind us we often fall for the illusion of genuine care . The takeaway is to appreciate chatbots for what they are – powerful, probabilistic text tools – and remain mindful of the line between useful simulation and real human experience.

Sources: The above points are drawn from recent AI research and analysis, including OpenAI’s publications on alignment , studies of LLM behavior in domains like healthcare , and cognitive science literature on artificial empathy . These sources collectively explain how and why chatbots appear intelligent or caring even though they operate without real understanding.