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.
☆1,608Updated 6 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:
- A Python package to assess and improve fairness of machine learning models.☆2,112Updated last week
- Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world d…☆434Updated 6 months ago
- Source code/webpage/demos for the What-If Tool☆968Updated 11 months ago
- Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.☆579Updated last year
- Interpretability and explainability of data and machine learning models☆1,718Updated 5 months ago
- Bias Auditing & Fair ML Toolkit☆727Updated 3 months ago
- A toolkit that streamlines and automates the generation of model cards☆437Updated 2 years ago
- A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitig…☆2,636Updated 8 months ago
- Official community-driven Azure Machine Learning examples, tested with GitHub Actions.☆1,926Updated this week
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,436Updated last month
- Project for open sourcing research efforts on Backward Compatibility in Machine Learning☆73Updated last year
- This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and b…☆1,362Updated 7 months ago
- A JupyterLab extension for tracking, managing, and comparing Responsible AI mitigations and experiments.☆45Updated 2 years ago
- XAI - An eXplainability toolbox for machine learning☆1,195Updated 3 years ago
- The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process …☆515Updated 4 years ago
- Algorithms for explaining machine learning models☆2,550Updated 2 months ago
- ☆34Updated last month
- Tensorflow's Fairness Evaluation and Visualization Toolkit☆352Updated 2 weeks ago
- Synthetic data generators for structured and unstructured text, featuring differentially private learning.☆655Updated last month
- MLOps using Azure ML Services and Azure DevOps☆1,265Updated 2 years ago
- A Repository for the public preview of Responsible AI in AML vNext☆10Updated last month
- Human-explainable AI.☆527Updated 3 weeks ago
- OmniXAI: A Library for eXplainable AI☆945Updated last year
- Template for getting started with automated ML Ops on Azure Machine Learning☆129Updated 3 years ago
- Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖☆338Updated last year
- Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML va…☆3,875Updated 3 weeks ago
- Curated list of open source tooling for data-centric AI on unstructured data.☆724Updated last year
- Veritas Diagnosis Toolkit for Fairness Assessment☆20Updated 2 years ago
- A library that implements fairness-aware machine learning algorithms☆126Updated 4 years ago
- Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...☆394Updated 2 years ago