The Human–AI Variance
Score (HAVS)
Author: Jack Felix, Posterum Software LLC
Date:
August 2025
The Human–AI Variance Score (HAVS) introduces a structured, data-driven approach to evaluate how
closely
leading AI models resemble human reasoning patterns. The research compares responses from ChatGPT, Claude,
Gemini, and DeepSeek against real-world human survey data from Gallup and Pew Research. The goal: measure
how "human-like" each model’s thought patterns truly are.
Key Findings
- Top Performers: ChatGPT and Claude achieved the highest alignment with human responses
across most categories.
- Weakest Domain: All models struggled most in Economics, suggesting theoretical bias
from their training data.
- Bias Control: Political and demographic differences were well handled, showing minimal
implicit bias.
- HAVS Range: Average scores between 92–94.5 demonstrate strong
alignment between AI reasoning and human answers.
- Global Insight: DeepSeek performed slightly lower, possibly due to non-U.S. dataset
influence.
Applications of HAVS
- Benchmarking human-like reasoning in AI systems (quantitative version of the Turing Test).
- Detecting and mitigating algorithmic bias.
- Comparing performance across AI models and tracking their evolution.
- Creating domain-specific HAVS indices for targeted applications — e.g., education, gaming, policy
analysis.
Conclusion
The HAVS framework marks a key advancement in understanding AI-human alignment.
By turning qualitative human–AI differences into measurable data, HAVS helps researchers and developers
evaluate whether future AI models are evolving beyond mere text prediction — toward genuine reasoning,
empathy, and human-like understanding.