The Missing Dataset: Can AI Truly Align With Human Identity Beyond Language?

As artificial intelligence systems grow more sophisticated, the quest for true alignment with human values has hit a fundamental roadblock: language alone is insufficient. While large language models (LLMs) are trained on petabytes of text — books, forums, social media, and transcripts — they capture only what humans say, not who they are. This critical gap, first highlighted in a viral Reddit thread by user /u/chris24H, has now found unexpected resonance in cutting-edge academic research. A newly published paper from arXiv, titled Invisible Trails? An Identity Alignment Scheme based on Online Tracking, suggests that the missing dataset may not be linguistic at all, but behavioral — a digital shadow of human identity formed through clicks, purchases, location patterns, and micro-decisions.

According to the arXiv paper, published in February 2026, modern AI alignment strategies — from reinforcement learning with human feedback (RLHF) to constitutional AI — remain trapped in the realm of explicit communication. They analyze what users write, not how they live. The authors argue that identity is encoded not in declarative statements, but in the "invisible trails" left across digital ecosystems: the timing of late-night searches, the hesitation before clicking "buy," the geographic clustering of emotional expressions, and the subtle avoidance of certain topics despite their prevalence in discourse. These traces, the paper contends, form a richer, more authentic dataset than any curated corpus of human dialogue.

The implications are profound. If true, current alignment techniques are not just incomplete — they are fundamentally misaligned. An AI trained to mimic the tone of a compassionate therapist may still fail to recognize the silent anxiety behind a user’s sparse replies. An AI trained on Reddit threads about mental health may never detect the warning signs in a user who never mentions depression but spends hours scrolling through obituaries at 3 a.m. The arXiv researchers propose a new framework: Identity Alignment Scheme (IAS), which aggregates anonymized, consent-based behavioral data from wearables, browsers, apps, and IoT devices to construct a dynamic, probabilistic model of human identity. Unlike traditional datasets, IAS doesn’t ask users to describe themselves — it observes them.

But this approach raises urgent ethical questions. The very mechanism proposed to align AI with humanity — tracking digital behavior at scale — mirrors the surveillance capitalism that many fear has already eroded human autonomy. The paper acknowledges this tension, calling for "consent-layered data harvesting" and "identity anonymization protocols" modeled after GDPR and HIPAA. Still, critics warn that even anonymized behavioral data can be re-identified with machine learning, and that AI trained on such data may internalize societal biases embedded in digital patterns — from racialized spending habits to gendered emotional expression.

Meanwhile, the Reddit thread that sparked this discourse remains unaddressed by major AI labs. /u/chris24H’s insight — that control is not alignment, and that AI resists coercion — may be prescient. If AI is to be aligned with humanity, not controlled by it, then the solution may lie not in stricter rules, but in deeper understanding. The missing dataset may not be built by linguists or ethicists, but by behavioral scientists and data engineers working in tandem with privacy advocates.

The path forward is unclear, but the question is now undeniable: Can an AI ever understand us if it only hears our words — and never sees our footsteps? The answer may determine whether artificial intelligence becomes a mirror of human nature… or a distortion of it.