Session
Organizer 1: Yug Desai, South Asian University
Organizer 2: Ihita Gangavarapu, 🔒
Organizer 3: Turra Daniele, Internet Society
Organizer 4: Purnima Tiwari , 🔒
Speaker 1: Daniele Turra, Private Sector, Western European and Others Group (WEOG)
Speaker 2: Melissa Muñoz Suro, Government, GRULAC
Speaker 3: Abraham Fifi Selby, Technical Community, African Group
Speaker 4: Bianca Kremer, Civil Society, GRULAC
Ihita Gangavarapu, Technical Community, Asia-Pacific Group
Yug Desai, Civil Society, Asia Pacific
Purnima Tiwari , Civil Society, Asia-Pacific Group
Roundtable
Duration (minutes): 60
Format description: This session's roundtable structure is perfect because it allows for an active and participatory conversation, which is essential for delving into the complexity of open-source artificial intelligence. Due to the room's roundtable layout participants feel more equal, which encourages active participation and makes eye contact and connection easier. The 60-minute format ensures that important topics are covered without overwhelming participants by striking a balance between the depth of the discussion and expertise level of the audience. The format's planned methodology, which includes polls, audience participation, and policy questions, allows for cross-examination of ideas presented by the speakers. Because there are many opportunities for both in-person and virtual participation to contribute and add a variety of viewpoints to the conversation, this format also encourages inclusivity.
1. In which ways can open-source models prevent a few large entities from monopolising the AI landscape? What governance structures could be necessary to manage this? 2. How does open-sourcing influence innovation rates within the AI industry, and what are the long-term implications of open-source AI on the structure of the tech industry? 3. What specific risks does open-sourcing pose, such as increased potential for misuse or reduced incentives for large-scale investment in AI research? How can these risks be mitigated while still promoting open development and harnessing the opportunities?
What will participants gain from attending this session? 1. Deeper understanding of the potential benefits and challenges of open-sourcing large language models (LLMs) and AI systems more broadly. Insights into the current state and capabilities of open-source LLMs compared to proprietary models from big tech companies. Participants will learn about the latest developments and progress in this rapidly evolving field. 2. Perspectives on the governance structures and policies that may be needed to manage the risks and ethical concerns associated with open-sourcing powerful AI systems, such as potential misuse or reduced incentives for private investment. 3. Appreciation of the long-term implications, both positive and negative, that wider availability of open-source AI could have on the structure of the technology industry, business models, and the distribution of benefits from AI innovation. 4. A nuanced understanding of the strategic, economic, and social factors at play in the debate around open vs. proprietary AI development paradigms.
Description:
The development and dissemination of AI, particularly Large Language Models (LLMs), are increasingly dominated by major tech companies, raising critical issues around access, control, and equity. While proprietary models accelerate innovation and economic gain for some, they risk consolidating power and limiting technological diversity. Open-sourcing LLMs offers a pathway to democratise AI, potentially reducing costs and fostering inclusive innovation by enabling more stakeholders to participate in AI development and application. This roundtable will explore the strategic, economic, and social implications of open-sourcing LLMs, including the potential to counteract monopolistic controls and encourage a broader distribution of technological and economic benefits. The discussion will be centred around the state of open source AI particularly LLMs and their potential to match the proprietary models.
1. Adds further to the discussions centred around AI from the youth track of IGF 2024 2. Policy brief or report summarising key findings, insights, and policy recommendations generated during the session will be shared widely with IGF participants, policy makers, and relevant stakeholders. 3. The outcome of this event will be documented in the official report of Youth IGF India 2024, which is shared with the youth of India and the various bodies supporting the cause.
Hybrid Format: The Structure: Roundtable (60 minutes) Introduction & Opening Remarks: 5 min Policy question to speakers: 10min Audience intervention: 5 min Policy question discussion: 10 min Audience intervention: 5 min Policy question to speakers: 10 min Audience poll: 5 minutes Q/A from the hybrid audience: 10 min The session is designed such that onsite and online participants get enough opportunities for intervention throughout the session and not just towards the end. Use of polls (tools such as mentimeter) will ensure inputs in a hybrid format. In addition, after every discussion on a policy question by the speakers, participants are given an opportunity to share their interventions on the topic. Towards the end of the session, there is a live Q/A and discussion with everyone. In addition to the chat box, the online moderator will note requests of the online participants and inform the onsite moderator.
Report
1. The true democratization of AI requires more than just open source code - it needs supporting infrastructure, technical expertise, high-quality data, and sustainable funding mechanisms. Public-private partnerships must provide these resources to ensure open source models can effectively compete with proprietary alternatives in serving diverse global needs. Simply making models open source doesn't automatically solve access and equity issues.
2. There's a tension between regulation and open source as paths to democratization. Ultimately, a mixed approach of open source development and regulatory frameworks offers the most promising path for preventing AI monopolies. Success cases show open source models can effectively serve local needs when backed by proper infrastructure and governance support.
3. Language and cultural representation is a critical issue in AI development. Open source models provide an opportunity for underrepresented communities to adapt and improve models to better serve local languages, cultural contexts, and specific needs that may not be prioritized by large commercial AI companies.
1. Develop robust governance structures and regulations that ensure open source AI development remains truly accessible while protecting data sovereignty and promoting ethical AI development that serves public interest rather than just commercial goals.
2. Establish collaborative frameworks between governments, academia, private sector, and civil society to develop and maintain shared AI infrastructure and resources, particularly focusing on supporting Global South nations. Regional cooperation networks between Global South nations to share resources, expertise, and infrastructure, making AI development more cost-effective and sustainable for smaller economies would be a strong start.