HAAM: Human-AI Accuracy Model
Implementation of the Double Machine Learning Lens Model Equation (DML-LME) for analyzing perceptual accuracy in high-dimensional settings. HAAM quantifies how humans and AI systems achieve accuracy when making judgments, decomposing their decision-making processes into interpretable components.
What is HAAM?
The Human-AI Accuracy Model addresses a fundamental question: When humans and AI achieve similar accuracy levels, are they using the same perceptual cues and cognitive strategies?
HAAM provides a rigorous statistical framework to:
Decompose judgment accuracy into direct and mediated pathways
Quantify the Percentage of Mediated Accuracy (PoMA) for any perceiver
Compare how humans vs AI utilize high-dimensional perceptual features
Handle thousands of features using debiased machine learning
Key Features
🎯 DML-LME Implementation: Double Machine Learning Lens Model Equation for high-dimensional perception
📊 PoMA Calculation: Quantify what percentage of accuracy flows through measured perceptual cues
🧠 Human-AI Comparison: Statistical framework for comparing perceptual strategies
📈 Rich Visualizations: 3D UMAP projections, PCA analysis, word clouds
🔍 Topic Modeling: Automatic discovery and labeling of content themes via BERTopic
📉 Comprehensive Metrics: Correlations, regression coefficients, cross-validated R², PoMA decomposition
Contents
Getting Started
API Reference