Source code/webpage/demos for the What-If Tool
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Updated
Jun 21, 2026 - HTML
Source code/webpage/demos for the What-If Tool
Fairness and bias detection library for Elixir AI/ML systems
Sample project using IBM's AI Fairness 360 is an open source toolkit for determining, examining, and mitigating discrimination and bias in machine learning (ML) models throughout the AI application lifecycle.
Analise de Fairness em ML usando metrica ABLNI com dataset Pima Diabetes - SDK completo com visualizacoes e relatorios
A React-based UI tool for AI ethics, providing insights into model explainability (XAI), fairness metrics, and bias mitigation.
"My journey through computer vision basics. This repo contains organized code snippets, exercises, and examples demonstrating core OpenCV functionalities, from reading images to implementing filters."
This project aims to automatically detect statistical and systemic biases in datasets before they corrupt ML models — flagging demographic disparities, selection bias, survivorship bias, and temporal drift, then explaining the real-world impact.
Production-ready platform for detecting, analyzing, and mitigating bias in machine learning models — by Zemen Matebe Ghelaw.
Official codebase for the paper "Synthetic Data for Fairness: Bias Mitigation in Facial Attribute Recognition" (IEEE, 2025). Includes an automated pipeline for generating, labeling, and evaluating synthetic facial datasets under demographic control using SD/GAN + fairness metrics.
Lightweight, transparent ML fairness report-card generator: demographic parity, equal opportunity, disparate impact — computed directly in pandas/numpy, graded A-F, explained in plain English.
Open source React app (and standalone HTML & Excel versions (Excel cannot perform Fisher's Exact)) for testing AI systems for bias and disparate impact using the Anthropic Claude API. Fisher's Exact, Chi-Square, and Z-test. Based on NIST AI RMF 1.0, NIST SP 1270, EEOC Technical Assistance on AI and Title VII (May 2023)
Tools to assess fairness and mitigate unfairness in sociolinguistic auto-coding
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