As part of our Deep Dive:AI, the Open Source Initiative (OSI) gathered a diverse collection of leaders to collaborate in drafting a definition for “Open Source AI.”
Speakers from law, academia, NGOs, enterprise, and the Open Source community shared their thoughts on pressing issues and offered potential solutions in our development and use of AI systems.
Watch the recordings of the Deep Dive webinars
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Challenges welcoming AI in openly-developed open source projects
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The Ideology of FOSS and AI: What “Open” means relating to platforms and black box systems
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Federated Learning: A Paradigm Shift for Secure and Private Data Analysis
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Perspectives on Open Source Regulation in the upcoming EU AI Act
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Fairness & Responsibility in LLM-based Recommendation Systems: Ensuring Ethical Use of AI Technology
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Copyright — Right Answer for Open Source Code, Wrong Answer for Open Source AI?
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Should we use open source licenses for ML/AI models?
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Operationalising the SAFE-D principles for Open Source AI
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Open source AI between enablement, transparency and reproducibility
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Opening up ChatGPT: a case study in operationalizing openness in AI
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Data privacy in AI
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Covering your bases with IP Indemnity
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Preempting the Risks of Generative AI: Responsible Best Practices for Open-Source AI Initiatives
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Data Cooperatives and Open Source AI
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Should OpenRAIL licenses be considered OS AI Licenses?
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Commons-based data governance
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