{"repo":"AmirhosseinHonardoust/Cognitivelens-AI-Human-Comparison","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Cognitivelens-AI-Human-Comparison","clone":"git clone https://github.com/AmirhosseinHonardoust/Cognitivelens-AI-Human-Comparison.git","description":"CognitiveLens is a Streamlit-powered analytics tool for exploring alignment between human and AI decisions. It visualizes fairness, calibration, and interpretability through metrics like Cohen’s κ, AUC, and Brier score. Designed for ethical AI, bias auditing, and decision transparency in machine learning systems.","language":"Python","stars":20,"topics":["ai","analytics","bias-detection","data-science","ethics","explainable-ai","fairness","human-centered-ai","interpretability","machine-learning"],"license":"MIT","category":"machine-learning","readme_excerpt":"CognitiveLens, Human-AI Decision Comparison Tool CognitiveLens is an advanced analytics and interpretability framework built with Python , Streamlit , and Plotly , designed to explore how humans and AI systems make decisions and where they diverge. It bridges data science , AI ethics , and visual storytelling , offering a hands-on environment to analyze fairness, calibration, and alignment between human and model predictions. --- Motivation & Vision In a world where machine learning systems augment or replace human decision-making , the key challenge is not just performance, it’s trust, interpretability, and alignment . CognitiveLens empowers analysts, data scientists, and AI practitioners to visualize decision overlap between humans and machines, highlight fairness gaps, and gain insights into whether a model’s decisions are justifiable, consistent, and ethically sound. “When humans and machines disagree, insight begins.” --- Core Objectives 1. Quantify AI–Human Alignment Evaluate how often the AI agrees with human decisions and measure consistency. 2. Evaluate Fairness Identify whether subgroups (gender, region, income level) show systematic performance differences. 3. Visualize Decision Logic Explore probabilities, ROC curves, and confusion matrices to interpret model behavior. 4. Enable Data Storytelling Transform ethical AI analysis into clear, narrative-driven visuals. 5. Empower Human Oversight Collect and analyze human judgments for model validation or retraining. ---","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Cognitivelens-AI-Human-Comparison/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}