BRIDGING THE GAP FOR ALICE: CHARITABLE ORGANIZATIONS ACTING AMIDRISING INFLATION AND FURTHER SOLUTIONS USING ARTIFICIAL INTELLIGENCE
Nikhil Chepuri, Rishika Porandla
June 18, 2025
ISBN: 979-8-89480-841-3
Nationwide, nearly 37.9 million U.S. households fall into the Asset Limited, Income Constrained, Employed (ALICE) population which consists of households making incomes above the Federal Poverty Line (FPL) but below the financially-secure ALICE Household Survival Budget, rendering them ineligible for numerous public assistance programs due to income thresholds. To bridge this gap, nonprofit organizations such as United Way and Feeding America are providing ALICE-tailored assistance programs. Through data collected from ALICE reports and U.S. Census data, comprehensive analysis has been conducted using the U.S. Bureau of Labor Statistics Data Retrieval Tools and the Federal Reserve Bank of Atlanta’s Policy Rules Database to provide insight into the complications associated with the ALICE population. Further, this study conducts extensive analysis on data extracted from the “Integrated Public Use Microdata Series - Current Population Survey (IPUMS CPS) data repository using Microsoft Excel’s “Pivottable” to quantify the impact of nonprofit organizations through the Household rasch food security score (FSRASCH) metric. As a result of these methods, a large gap – in terms of ALICE households benefitted – is evident, leading to a conclusion: inadequate access to information regarding assistance programs. To address this large portion of ALICE households nationwide without assistance, AskALICE, an AI chatbot, is proposed, aiming to provide an easily-accessible, centralized, simple solution to those who lack access to information regarding these programs. By leveraging various data tables, assistance program information, and machine learning capabilities, it will accurately respond to user queries and, after evaluating specific criteria, identifies the assistance programs the user is eligible for. Further, a working prototype was built using Yellow.ai’s Orchestrator LLM to serve as a proof-of-concept.This study sheds light to the millions of ALICE households nationwide left without assistance from public programs and offers practical solutions with significant possible implications to enhance economic stability.
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