Machine learning in health has made impressive progress in recent years, powered by an increasing availability of health-related data and high-capacity models. While many models in health now perform at, or above, humans in a range of tasks across the human lifespan, models also learn societal biases and may replicate or expand them. In this talk, Dr. Marzyeh Ghassemi will focus on the need for machine learning researchers and model developers to create robust models that can be ethically deployed in health settings, and beyond. Dr. Ghassemi's talk will span issues in data collection, outcome definition, algorithm development, and deployment considerations.

This talk talk has a single objective: to advocate for machine learning infused with social purpose. Social purpose here is an invitation to deepen our inquiries as investigators and inventors into the relationships between machine learning, our planet, and each other. In this way, social purpose transforms our field of machine learning: into something that is both technical and social. And my belief is that machine learning with social purpose will provide the passion and momentum for the contributions that are needed in overcoming the myriad of global challenges and in achieving our global goals. To make this all concrete, the talk will have three parts: machine learning for the Earth systems, sociotechnical AI, and strengthening global communities. And we’ll cover topics on generative models; evaluations and experts; healthcare and climate; fairness, ethics and safety; and bias and global inclusion. By the end, I hope we’ll have set the scene for a rich discussion on our responsibility and agency as researchers, and new ways of driving machine learning with social purpose.

Jennifer A. Doudna, Innovative Genomics Institute, Howard Hughes Medical Institute and University of California Berkeley & UCSF/Gladstone Institutes Machine learning will have profound impacts on biological research in ways that are just beginning to be imagined. The intersection of ML and CRISPR provides exciting examples of the opportunities and challenges in fields ranging from healthcare to climate change. CRISPR-Cas programmable proteins can edit specific DNA sequences in cells and organisms, generating new biological insights as well as approved therapeutics and improved crops. I will discuss how ML may accelerate and perhaps fundamentally alter our use of CRISPR genome editing in both humans and microbes.

Proxy objectives are a fundamental concept in machine learning. That is, there's a true objective that we care about, but it's hard to compute or estimate, so instead we construct a locally-valid approximation and optimize that. I will examine reinforcement from human feedback with this lens, as a chain of approximations, each of which can widen the gap between the desired and achieved result.