Californians who receive food assistance come from all backgrounds, but many share a similar story: they were barely getting by financially when they were tipped into crisis by an unexpected expense or loss of income. This site shares their stories.
A case study explaining how a predictive, data-driven machine-learning model was developed to detect unauthorized cash benefit withdrawals more quickly and accurately in California.
This presentation shares user experience research on the challenges, priorities, and opportunities for improving the journey of Bay Area residents seeking affordable housing.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
