microsoft / responsible-ai-toolboxLinks
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
☆1,687Updated 10 months ago
Alternatives and similar repositories for responsible-ai-toolbox
Users that are interested in responsible-ai-toolbox are comparing it to the libraries listed below
Sorting:
- Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world d…☆436Updated 10 months ago
- A Python package to assess and improve fairness of machine learning models.☆2,176Updated last week
- Source code/webpage/demos for the What-If Tool☆984Updated 3 weeks ago
- Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.☆605Updated 2 weeks ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,478Updated 5 months ago
- A toolkit that streamlines and automates the generation of model cards☆441Updated 2 years ago
- Official community-driven Azure Machine Learning examples, tested with GitHub Actions.☆1,957Updated last week
- Interpretability and explainability of data and machine learning models☆1,749Updated 10 months ago
- Bias Auditing & Fair ML Toolkit☆742Updated last week
- Algorithms for explaining machine learning models☆2,602Updated 2 months ago
- OmniXAI: A Library for eXplainable AI☆959Updated last year
- This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and b…☆1,390Updated last month
- XAI - An eXplainability toolbox for machine learning☆1,212Updated 3 weeks ago
- Tensorflow's Fairness Evaluation and Visualization Toolkit☆358Updated 4 months ago
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).☆1,559Updated last month
- Project for open sourcing research efforts on Backward Compatibility in Machine Learning☆75Updated 2 years ago
- Resources for Data Centric AI☆1,131Updated 2 years ago
- ☆35Updated 5 months ago
- Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖☆344Updated 2 years ago
- Human-explainable AI.☆526Updated 3 months ago
- Curated list of open source tooling for data-centric AI on unstructured data.☆735Updated 2 years ago
- The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process …☆524Updated 4 years ago
- A Repository for the public preview of Responsible AI in AML vNext☆10Updated 3 weeks ago
- Synthetic data generators for structured and unstructured text, featuring differentially private learning.☆669Updated 6 months ago
- MLOps examples☆2,053Updated last year
- Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are look…☆443Updated last year
- The Data Cards Playbook helps dataset producers and publishers adopt a people-centered approach to transparency in dataset documentation.☆196Updated last year
- Template for getting started with automated ML Ops on Azure Machine Learning☆130Updated 3 years ago
- 📝 Design doc template & examples for machine learning systems (requirements, methodology, implementation, etc.)☆634Updated 2 years ago
- A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitig…☆2,725Updated last month