Recent trends in AI research reflect a shift from purely technological advancements to the creation of AI solutions with significant societal impact. The AI community is increasingly focused on harnessing AI to support crucial global objectives including enhancing education quality, alleviating poverty, and combating climate change. Despite these noble intentions, the actual impact of AI for Good initiatives remains modest in regard to its potential.
AI research is increasingly targeting impactful applications, yet pure research areas such as algorithmic fairness, robust machine learning, and explainable AI also receive significant focus. Establishing clusters for collaboration on these foundational topics, alongside applied domains like healthcare diagnostics and climate prediction for instance, could align and amplify our efforts. Bridging the gap between theoretical AI advancements and tangible community benefits is critical for ensuring that innovations are both relevant and respectful. Encouraging research in fields like resource distribution analytics and AI-driven energy solutions for example can pave the way for sustainable, scalable social impact. Furthermore, overcoming data challenges will catalyze the transition of AI research from theory to transformative practice.
Collaboration among academia, technology corporations, nonprofits, and government entities is crucial for the Public Good. However, these sectors often operate in isolation, driven by divergent goals, leading to AI for Social Good projects that, while commendable, lack integration with the communities they aim to assist, undermining sustainability.
The path to integrating AI with social good initiatives is complex, requiring not just technological innovation but also an inclusive and coordinated approach that respects and involves local communities. This workshop aims to explore these challenges and collaboratively forge a roadmap for AI research that is directly impactful and sustainable.
During this 1-day discussion and interactive workshop led by multidisciplinary and multilateral speakers participants will:
Learn from the state of the art, opportunities, and obstacles in AI design for public good in practice.
Discuss and design frameworks and incentives towards a collaborative approach to AI for Good from research to field applications.
Define next steps and specific actions to accelerate the development and implementation of joint impactful, scalable and sustainable AI research for public good projects.
