During the Covid-19 pandemic, in spite of the impressive advances in machine learning in recent decades, the successes of this field were modest at best. Much work remains, for both machine learning and global health researchers, to deliver true progress in global health. This workshop will start a lasting and consistent effort to close the gap between advances in machine learning, practitioners and policy makers working in public health globally. It will focus on difficult public health problems and relevant machine learning and statistical methods.

We will use this opportunity to bring together researchers from different communities to share new ideas and past experiences. We will facilitate rapid communication of the latest methodological developments in machine learning to parties who are in positions to use them and establish feedback loops for assessing the applicability and relevance of methods that are available and gaps that exist. It will be a unique opportunity to challenge both research communities and demonstrate important, policy-relevant applications of sophisticated methods at one of the most prestigious annual machine learning conferences.

This will be the first ever machine learning conference workshop on the topic ``Machine Learning & Global Health’’, sponsored by the Machine Learning & Global Health Network. By showcasing key applied challenges, along with recent theoretical advances, we hope to foster connections and prompt fruitful discussion. We will invite researchers to submit extended abstracts for contributed talks and posters along the themes of:

• What lessons can we learn from the COVID-19 pandemic?
• What sorts of questions in global health can machine learning be useful for? What sorts of questions in global health is machine learning unlikely to be useful for?
• The current limitations in the application of machine learning to solving global health problems and possible solutions to these limitations.
• How can we leverage machine learning in order to: promote public health worldwide; be proactive against future pandemics; understand and address inequalities in health.
• What types of data and data sharing practices would enable better machine learning and global health?