Open Source Artificial Intelligence (OSAI) technologies are being rapidly experimented and adopted by all sort of companies. There are perceived benefits of using OSAI such as cost efficiencies, accelerated innovation through global collaboration, enhanced transparency, and the strategic advantage of avoiding vendor lock-in. Companies are also actively participating in open source ecosystems through their employees and sponsorships as a means of accelerating their progress and that AI breakthroughs can come from collaborative effort rather than behind closed doors. Such a phenomenon success of OSAI in the real world application is not without its complexities. Industrial adopters face significant challenges, including cybersecurity issues, intricacies related to intellectual property and regulatory compliance, lack of inhouse knowledge and expertise to take full advantage of OSAI, concerns about the long-term sustainability and support and the ethical issues stemming from data quality and bias problems. These and many other issues with adopting and using OSAI raises important questions that need to be debated. The panel would shed some light on such issues from different perspectives. The key stimulating questions, not the exhausting list, to drive the discussion are:

How should the SOAI be defined clearly and actionably to balance innovation, trust, and competitive interests?

How has OS collaboration accelerated innovation in AI compared to traditional R&D models? What are the examples of community-driven projects outpacing an individual company’s output.

How will the increasing use of AI-powered development tools impact the quality, maintainability, and security of OSAI projects, and what kind of quality assurance approaches are needed?

What are the common security risks of using OSAI and how enterprises should adopt OSAI by considering and addressing potential security risks?

How can OSAI models and training data ensure compliance with emerging laws, and what role can open collaboration play in addressing biases and promoting ethical AI development at scale?

How can organisations assess and decide when to leverage OSAI, proprietary AI, or a hybrid approach, and how can organizations effectively integrate and manage diverse AI technology stacks for optimal business value?