AmirhosseinHonardoust/Algorithmic-Empath-Human-Fallibility
A deep exploration of Algorithmic Empathy, the next frontier in AI understanding. This project examines how machines can learn from human fallibility, model disagreement, and align with moral reasoning. It blends psychology, fairness metrics, interpretability, and co-learning design into one framework for humane intelligence.
This project helps professionals in fields like healthcare, finance, or HR to understand why their AI models and human experts sometimes disagree on critical decisions. It takes human judgments and AI predictions as input, analyzing the patterns in their divergences to reveal cognitive biases or contextual gaps. The output is a set of metrics and visualizations that explain *when* and *why* humans make mistakes, helping decision-makers build more humane and aligned AI systems.
Use this if you need to understand the 'why' behind disagreements between human experts and AI models in critical decision-making processes, aiming for better human-AI collaboration.
Not ideal if your primary goal is simply to improve raw AI model accuracy without considering the nuances of human judgment or ethical alignment.
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MIT
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Last pushed
Nov 05, 2025
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