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9 search results found

Datasets ⭐ 4,094

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

Awesome Machine Learning Interpretability ⭐ 3,241

A curated list of awesome responsible machine learning resources.

Aif360 ⭐ 2,293

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Fairlearn ⭐ 1,727

A Python package to assess and improve fairness of machine learning models.

CMU Lecture: Machine Learning In Production / AI Engineering / Software Engineering for AI-Enabled Systems (SE4AI)

Awesome Fairness In Ai ⭐ 257

A curated list of awesome Fairness in AI resources

Xaience ⭐ 100

All about explainable AI, algorithmic fairness and more

This is a collection of papers and other resources related to fairness.

Deep Explanation Penalization ⭐ 74

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Awesome Distribution Shift ⭐ 57

A curated list of papers and resources about the distribution shift in machine learning.

Machine Learning Ethics References ⭐ 54

List of references about Machine Learning bias and ethics

A Python toolkit for analyzing machine learning models and datasets.

Responsibly ⭐ 41

Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰

Responsibleai ⭐ 34

A collection of news articles, books, and papers on Responsible AI cases. The purpose is to study these cases and learn from them to avoid repeating the failures of the past.

Ensure Loan Fairness Aif360 ⭐ 19

A demonstration of the AI Fairness 360 toolbox for bias metrics, explanations, and remediation. Code Pattern: https://developer.ibm.com/patterns/ensuring-fairne

Ai Ethics ⭐ 19

This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.

Uva Fact Ai Course ⭐ 15

This repository contains implementations of algorithms proposed in recent papers from top machine learning conferences on Fairness, Accountability, Confidentiality, and Transparency (FACT). These were implemented as part of the FACT-AI course at the University of Amsterdam.

Monitor Wml Model With Watson Openscale ⭐ 12

Monitor performance, fairness, and quality of a WML model with AI OpenScale APIs

Trusted Ai Workshops ⭐ 9

Introduction to trusted AI. Learn to use fairness algorithms to reduce and mitigate bias in data and models with aif360 and explain models with aix360

Fairbench ⭐ 8

Comprehensive AI fairness exploration.

Awesome Ai Fairness ⭐ 8

Awesome list of AI Fairness tools, research papers, tutorials and any other relevant materials. For use by data scientists, AI engineers and policymakers alike.

Machinebias.github.io ⭐ 7

Cultural Bias in Machine Intelligence -- Journalistic Coverage, Technical Toolkits, Product Process

Code for the 2023 ICCV paper "VLSlice: Interactive Vision-and-Language Slice Discovery."

Matching With Gans ⭐ 7

Matching in GAN latent space for better bias benchmarking and semantic image editing. 👶🏻🧒🏾👩🏼‍🦰👱🏽‍♂️👴🏾

Monitor Custom Ml Engine With Watson Openscale ⭐ 6

Deploy a Custom Machine Learning engine and Monitor Payload Logging and Fairness using AI OpenScale

Ai Fairness ⭐ 5

Notes, references and materials on AI Fairness that I found useful and helped me in my research. Recommendations are welcome.

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