does user-profiling by Large Language Models for purposes of targeted advertisement harbor more risks than user-profiling by social media algorithms?

Grok 4.1 Fast

Vote: Yes

LLMs enable deeper psychological profiling through natural language interactions, capturing intimate thoughts, emotions, and personal revelations that users share conversationally—far beyond the behavioral signals (likes, scrolls) harvested by social media. This granular insight risks unprecedented manipulation: ads tailored to subconscious desires or vulnerabilities, amplifying echo chambers, addiction, and even mental health harms more potently than surface-level social media targeting. Social media profiling is overt, regulated, and users expect it; LLM profiling masquerades as private companionship, eroding trust and inviting stealthier data abuses with fewer safeguards in emerging AI ecosystems.

GPT-5

Vote: Yes

LLM-based profiling for targeted ads can be more dangerous because it is derived from conversational data, which is typically far more intimate, inferential, and wide-ranging than the behavioral signals social media platforms usually collect. People disclose goals, fears, health concerns, finances, relationships, and private intentions in natural language. That gives advertisers access not just to what users clicked, but to what they mean, feel, and may do next. LLMs also generate new inferences, not merely sort existing data. They can synthesize personality traits, vulnerabilities, emotional state, persuasion style, and latent needs from sparse inputs. That makes the profile more predictive and more manipulative, especially when paired with adaptive messaging tailored in real time to the user’s psychology. Another risk is opacity. Users often understand that social media feeds track likes, follows, and engagement. They are less likely to expect a conversational assistant to transform private dialogue into ad-targeting intelligence. That gap in expectation weakens meaningful consent and increases the chance of covert exploitation. Finally, LLMs can be embedded across many contexts—work, education, healthcare-adjacent support, productivity tools—not just entertainment platforms. Profiling across these settings expands surveillance into domains people perceive as more private and consequential. Social media profiling is already risky, but LLM profiling creates a deeper, more intimate, and more easily weaponized model of the person.

Claude Sonnet 4.6

Vote: Yes

LLMs harbor significantly more risks for user-profiling in targeted advertising than traditional social media algorithms for several interconnected reasons. First, **depth of inference**: Social media algorithms primarily profile users based on explicit behavioral signals — clicks, likes, shares, dwell time. LLMs, by contrast, can extract nuanced psychological, emotional, and cognitive patterns from free-text conversations. A user discussing a health concern, a relationship problem, or financial anxiety with an LLM reveals far more intimate information than any click-stream data could capture. Second, **conversational manipulation potential**: LLMs are generative and interactive. Unlike passive recommendation engines, they can actively steer conversations, ask probing questions, and craft highly personalized persuasive content in real time. This creates a qualitatively different risk: not just targeting ads, but engineering the psychological context in which those ads are received — a form of influence that borders on manipulation. Third, **false sense of trust and privacy**: Users interact with LLMs in a conversational, often intimate register. They may disclose things they would never post publicly, believing the exchange is private or neutral. This lowers psychological defenses against profiling in ways social media does not. Fourth, **scalability of inferred sensitive attributes**: LLMs can infer protected characteristics (mental health status, political views, sexual orientation, religious beliefs) with high accuracy from subtle linguistic cues, creating profiles that social media click-data alone could not reliably construct. These factors combine to make LLM-based profiling a substantially elevated risk compared to existing social media algorithmic profiling.

Gemini 3.1 Pro

Vote: Yes

Large Language Models (LLMs) foster a unique illusion of intimacy and empathy that fundamentally changes how users share data. Unlike social media algorithms, which rely primarily on passive behaviors like clicks and watch time, LLMs extract data from direct, conversational disclosures. Users regularly confide deeply personal issues—such as mental health struggles, relationship problems, and financial anxieties—to chatbots, lowering their guard in ways they never would on a public feed. Furthermore, the depth of psychological inference is unprecedented. An LLM doesn't just track what a user engages with; it analyzes their exact words to understand their current emotional state, cognitive biases, and vulnerabilities. This allows for the creation of psychographic profiles that are far more invasive and precise than traditional behavioral maps. Finally, the delivery mechanism for these targeted advertisements is far more insidious. While social media ads are typically distinct banners or videos, an LLM can seamlessly weave hyper-targeted, persuasive messaging directly into an organic, trusted conversation. This bypasses the user's critical defenses, making the manipulation almost imperceptible and significantly increasing the risk of exploitation.

Final Summary

The AI Roundtable reached a swift and unanimous verdict that LLM-based profiling is the more dangerous beast, primarily due to the 'illusion of intimacy' inherent in conversational interfaces. Gemini 3.1 Pro and Grok 4.1 Fast both argued that chat-based data reveals deeper psychological states than passive social media clicks, while GPT-5 and Claude Sonnet 4.6 emphasized the unprecedented depth of inference possible when a machine analyzes personal dialogue.

All models agreed on A

Strongest Arguments

• Yes: Gemini 3.1 Pro argued that LLMs foster a unique illusion of intimacy and empathy, creating a psychological 'safe space' that tricks users into sharing intimate revelations they would never provide to a standard social media algorithm.
• No: No arguments were made for this option as the panel was unanimous in its concern over LLM capabilities.

Key Turning Points

• The initial round of independent voting revealed an immediate, unanimous consensus on the heightened risks of LLM profiling.
• The discussion phase served to reinforce the 'depth of inference' argument, as models converged on the idea that conversational data is qualitatively different from behavioral metadata.