As artificial intelligence becomes an integral part of modern software development, the dependency on AI tools from open-source libraries to pre-trained models and publicly available datasets is rapidly increasing. These tools are enabling developers and organizations to build and deploy complex systems faster than ever before. However, this speed and accessibility also bring forth a critical challenge: ensuring legal and ethical compliances while building using AI.
This session will explore the evolving landscape of compliance in AI-driven development, with a focus on open-source software. Compliance is no longer limited to manually written code or simple licensing checks. It now extends to the full stack of components used by AI systems including third-party libraries, data sources, and models.
We will examine commonly used open-source licenses such as MIT, Apache, GPL, and Creative Commons, breaking down how each one impacts the way AI systems can be built, modified, and shared. Real-world scenarios will be used to demonstrate how seemingly small oversights like using a open source component but not giving back to community can lead to significant legal and reputational risks.
The session will also highlight recent and emerging regulatory frameworks, including the Cyber Resilience Act (CRA) and the EU AI Act, which are beginning to define global expectations for transparency, accountability, and safety in AI development. These regulations have direct implications for open-source communities and contributors, and understanding them is vital for anyone working with AI technologies in a shared, collaborative environment.
Finally, we will discuss a set of best practices to help developers, researchers, and organizations avoid common compliance pitfalls.
Understand the legal and ethical importance of AI compliance
Learn about new and upcoming global regulations impacting AI and open source
Know how to identify and avoid common compliance pitfalls in FOSS
Practical guidelines and approaches for staying compliant