AI4 Society Seminar: Alison Koenecke

"Algorithmic Decisions in the SNAP Benefits Pipeline"

America’s Supplemental Nutrition Assistance Program (SNAP), formerly known as food stamps, helps low-income households buy nutritious food. Social workers provide pivotal support at many points of the SNAP pipeline: from spreading awareness of the program, to helping applicants determine eligibility and fill out forms, to advising on changes to benefits. In this talk, I describe two projects that ask how algorithms can play a role in easing social worker burdens without perpetuating algorithmic biases. First, we study biases in online advertising for SNAP benefits, arising from the cost differentials between English-speaking and Spanish-speaking ad recipients. We propose a methodological framework for advertisers to determine a demographically equitable allocation for ads, and find broad consensus across political identities for some degree of equity over pure efficiency in this context. Second, we study the efficiency-bias tradeoffs of a chatbot for assisting social workers in answering SNAP eligibility questions. In a randomized experiment varying the quality of LLM-generated responses, we find that social workers with access to LLM suggestions perform significantly better than those without; but, while human accuracy increases with LLM response accuracy, improvement plateaus — likely due to an LLM-distrust effect. Taken together, these studies can inform government service providers on procurement strategies in the AI age while ameliorating administrative burdens and biases.