Open Source AI Tools
Discover and explore the most popular open source AI and machine learning tools from GitHub
tf-explain
sicara
ExplainabilityFreeAPI
Interpretability Methods for tf.keras models with Tensorflow 2.x
Python
1.0K
110
0
47
2019
What-If Tool
Transparency
Source code/webpage/demos for the What-If Tool
HTML
1.0K
184
0
0
differential-privacy-library
IBM
Privacy & SecurityFreeAPI
Diffprivlib: The IBM Differential Privacy Library
Python
903
206
0
10
2019
lofo-importance
aerdem4
ExplainabilityFreeAPI
Leave One Feature Out Importance
Python
856
84
0
3
2019
aequitas
dssg
BiasFreeAPI
Bias Auditing & Fair ML Toolkit
Python
775
127
0
55
2018
DeepExplain
marcoancona
ExplainabilityFreeAPI
A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)
Python
760
136
0
33
2017
federated
google
Privacy & SecurityFreeAPI
A collection of Google research projects related to Federated Learning and Federated Analytics.
Python
742
209
0
17
2020
tcav
tensorflow
ExplainabilityFreeAPI
Code for the TCAV ML interpretability project
Jupyter Notebook
650
152
0
17
2018
Integrated-Gradients
ankurtaly
ExplainabilityFreeAPI
Attributing predictions made by the Inception network using the Integrated Gradients method
Jupyter Notebook
645
109
0
16
2017
securify2
eth-sri
Privacy & SecurityFreeAPI
Securify v2.0
Solidity
629
141
0
36
2018
PyDP
OpenMined
Privacy & SecurityFreeAPI
The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
Python
541
142
0
57
2020
interpret-community
interpretml
ExplainabilityFreeAPI
Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world datasets and workflows.
Python
439
88
0
26
2019
With Thanks to Our Contributors & Supporters
Building a responsible AI marketplace is a community effort. We're grateful to the developers, researchers, organizations, evaluators, and individuals whose work helps make AI more accessible, transparent, and responsible through the framework of 6 pillars:
TransparencyPrivacy & SecurityFairnessExplainabilityEthicalBias
Database Contributors
Researchers and developers whose data collection efforts help build the foundation of AI tools.
Responsible AI Marketplace Open-source AI Tools Database
Cornell University
Advisor: Martin Wells
Contributors: Houlin Chen, Xinyu Zhu, Yinghan Xia, Kate Chanpong, Zixuan Huangfu
Research & Evaluation Contributors
Responsible AI depends on meaningful evaluation, testing, research, and transparency.
Xiaochen Zhang/ AI 2030Daniela Muhaj/ AI 2030 / Georgetown Univ. / MIT CSAILAnya Wang Crane/ AI 2030David Shedd/ AI 2030Ravneet Kaur/ AI 2030Naga Lalitha Sree Thatavarthi· Web DesignIyad Cherifi/ University of Surrey· Web R&DXiaoyi Mark Sun/ Imperial College· Tech Stack, Web R&DCharles Cai/ Socialogix / University of Surrey· Tech Architecture, R&D
Infrastructure
Credits, resources and support.
Amazon AWSCloudflareGitHub
Built with Open Source
Core technologies powering the marketplace.
ReactNode.jsStrapiPostgreSQLTailwind CSS
Community Contributors
Our marketplace is strengthened by the people who review, test, evaluate, document, and share their experiences with AI tools. Thank you to everyone who contributes their time, expertise, and perspective.
Database Citation
If you use this database in your research, please use the following BibTeX entry.
@misc{ai2030-marketplace,
title={AI2030 Responsible AI Open-Source Tools and Commercial Tools Database / Marketplace},
author={AI2030 and Community Contributors},
year={2025--2026},
url={https://marketplace.ai2030.org}
}A Note on Independence — Recognition on this page does not constitute an endorsement of a tool, organization, model, or viewpoint. Commercial relationships and sponsorships are disclosed where applicable. Marketplace listings, evaluations, and editorial materials are governed by our published standards and policies.