Stanford’s Institute for Human-Centered Artificial Intelligence announced the recipients for its 2019 seed grant awards. Of 143 applications, HAI funded 29 unique and innovative proposals that foster novel interdepartmental or inter-school collaborations between faculty, postdocs, and students, and present new, ambitious and speculative research that will help advance and guide the future of AI.
Michele Barry, Senior Associate Dean for Global Health, will work with eight collaborators on Predicting Malaria Outbreaks: AI to Learn, Classify and Predict Across Diverse Paleo-demographic, Climatic and Genomic Data.
Krish Seetah (Anthropology), Robert Dunbar (Earth System Science), Carlos Bustamante (Biomedical Data Science, Genetics), Giulio De Leo (Biology), Erin Mordecai (Biology), Bright Zhou (Medicine), David Pickel (Classics), Hannah Moots (Anthropology) and Barry are working to predict the impact of malaria for the next 50-100 years. The project incorporates AI tools to recognize patterns in transmission over time by accessing vast, data-rich evidence on climate, land use, and human behavior from historic epidemics, alongside genetic evidence on human demography, and vector and parasite biology.
Malaria threatens 3.5 billion people, 90 percent of whom live in Africa. Despite billions in funding eradication efforts, prevalence is increasing because of resistance to pesticides and drugs, and the lack of a proven vaccine.
Gary Darmstadt, Center for Innovation in Global Health core faculty, and three collaborators will work on Uncovering gender inequalities in East Africa: Using AI to gain insights from media data.
James Zou (Biomedical Data Science), Londa Schiebinger (History), Ann Weber (Pediatrics), Valerie Meausoone (Population Health Sciences) and Darmstadt aim to take advantage of Artificial Intelligence (AI) methods, such as natural language processing combined with machine learning, to gain insights into the ways different gender groups are perceived in East-African media. Specifically, they aim to start a database of word embeddings for gendered terms trained on publicly available media data, focusing on three former British colonies: Kenya, Uganda and Tanzania. They hypothesize that quantified gender biases will show country-specific differences in gender stereotypes that codify attitudes and behaviors.
Gender inequality intersects with discrimination by race, age, social class in ways that affect the health and wellbeing of people around the world. Analysis of media data from the U.S. has revealed under-recognized gender, racial and ethnic-related biases in the public sphere, demonstrating how language can reflect social, political and institutional environments.
Please read about all the grant award winners at the HAI website.