Building a Responsible and Open
Community for Generative AI
Our Workstreams
The Generative AI Commons is a community-driven open membership initiative with elected chairs and represented by non-profits, academia and industry in a neutral forum. We embrace responsible and trustworthy AI, foster collaboration, promote AI literacy and advocate for our community.
Models, Applications & Data (MAD) Workstream
- GenAI components
- Technical collaborations
- Advancement discussions
- Critical evaluation
- Generative AI adoption
- Reference architectures
- Model Openness Framework
- Best practices and guidelines
- Compliance in code
Education and Outreach Workstream
- Education and training
- Thought leadership
- Educational outreach
- Legislative representation
Responsible AI Workstream
- Responsible AI
- Security, Privacy and Safety
- Informing Policy
- Copyright Issues
- Model and Data Lineage
Workstream Leaders
Anni Lai
Generative AI Commons Chair
Arnaud Le Hors
Generative AI Commons Vice Chair
Nick Chase
MAD Workstream Lead
Sachin Varghese
MAD Workstream Lead
Raghavan Muthuregunathan
MAD Workstream Lead & Education and Outreach Workstream Lead
Ofer Hermoni
Education and Outreach Workstream Lead
Projects
We host open source projects that drive generative AI innovation across industries in an ethically responsible way, from models and datasets to vector stores and AI application frameworks.
We encourage the publishing of data sets, and models including source code with weights and biases released under open-source licenses. We promote transparency and recommend that research papers be informal and discuss possible harms and the means to mitigate them.
We are backed by a global community of AI researchers, engineers, data scientists, ethicists and developers and we provide resources and accessible tools that are designed to foster collaboration and empower developers to innovate and build with generative AI while embracing the tenants of Responsible AI.
Explore ProjectsHost Your Project
Get Involved
The Generative AI Commons, an initiative of the LF AI & Data Foundation, democratizes generative AI by hosting open-source models, datasets, and applications. We publish standards and frameworks to support interoperability and economies of scale.
Collaborating with industry leaders, researchers, and policymakers, we promote Responsible AI principles for open-source AI and open science. Our goal is to ensure ethical, transparent, and accountable AI practices that benefit enterprises, industries, non-profits, academia, and humanity.
We advance AI literacy and Responsible AI through education and training programs, helping the community and public understand the benefits and risks of generative AI.
Get InvolvedMore About Us
