Welcome to the AI Alliance, a community of technology creators, developers, and adopters collaborating to advance safe, responsible AI rooted in open innovation.
The AI Alliance is focused on accelerating and disseminating open innovation across the AI technology landscape to improve foundational capabilities, safety, security and trust in AI, and to responsibly maximize benefits to people and society everywhere.
This is the GitHub organization for Alliance projects.
The projects in this organization are organized into focus areas:
How do we know that applications built with AI are trustworthy, that they perform as required, in particular that they are safe, free of harmful outputs?
| Links | Description |
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| Trust and Safety Evaluations Initiative | |
TSEI seeks to define the global taxonomy of evaluations (from safety to performance to efficacy), catalog available implementations of them, and provide a reference stack of industry-standard tools to run the evaluations. Projects:
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| Ranking AI Safety Priorities by Domain | |
| What are the most important safety concerns for your specific domain and use cases? This project explores these questions in several industries, healthcare, finance, education, and legal, with more to come. | |
| The AI Trust and Safety User Guide | |
| Introduction to T&S with guidance from diverse experts. | |
Real-world use of AI involves more than just models. What application patterns best complement the strengths and weaknesses of models? Are there domain-specific considerations?
| Links | Description |
|---|---|
| Gofannon | |
| A repository of functions consumable by other agent frameworks. | |
| Proscenium | |
| A lightweight, composable library and several demo applications. | |
| The Living Guide to Applying AI | |
| Tips from experts on using AI for various applications, including popular design patterns. | |
| AI Application Testing for Developers | |
| If you are a software developer, you are accustomed to writing deterministic tests. What do you do when generative models aren't deterministic? | |
While NVIDIA GPUs are the dominant AI accelerators, alternatives are useful for different price vs. performance trade offs, including deployments to edge devices, like phones. How do we ensure that the software stack we use supports different accelerator options?
| Links | Description |
|---|---|
| The AI Accelerator Software Ecosystem Guide | |
| A guide to the most common AI accelerators and the software stacks they use to integrate with tools you know, like PyTorch. | |
Datasets with clear license for use, backed by unambiguous provenance and governance controls, are needed to train and tune models. A variety of models are needed, not just for English text, but multilingual, multimodal, and domain specific, like models for molecular discovery, geospatial, and time series.
| Links | Description |
|---|---|
| The Open, Trusted Data Initiative | |
Open data has clear license for use, across a wide range of topic areas, with clear provenance and governance. OTDI seeks to clarify the criteria for openness and catalog the world’s datasets that meet the criteria. Projects:
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| The Open, Trusted Model Initiative | |
| Facilitate the creation of open models in diverse science and technology domains, and for applications like time series, drug discovery, etc. | |
Advocacy is about educating the public, policy officials, and others about the benefits of openness for AI, as well as the implications for safety and reliability.
| Links | Description |
|---|---|
| AAAI 25: Workshop on Open-Source AI for Mainstream Use | |
| A workshop at AAAI 25 that explored practical challenges using AI. | |
- The AI Alliance GitHub Organization
- Contributing to the AI Alliance community.
- The microsite template: The template used for Alliance projects, including all the websites listed above. See the README-template.md for instructions.
- The AI Alliance website: About the AI Alliance, our goals and initiatives.
- Learn more about getting involved.