• Skip to primary navigation
  • Skip to main content
UT Shield
Urban Information Lab at UT Austin
  • About
    • The Director
    • Mission
  • News & Events
  • Projects
    • Deserts
      • Austin Housing Analysis
      • Austin AI Housing Analysis
      • Transit Deserts
      • Hospital Deserts
      • Community Hub for Smart Mobility (CHSM)
    • Health
      • Urban Health Risk Mapping
      • [COVID-19] VMT Impacts
      • [COVID-19] Epidemic Risk Index
      • Texas Entrepreneurship
      • Optimizing Ambulance Allocation and Routing During Extreme Events
    • Digital twin
      • Smart City Data Integration
      • National Housing Data Portal
      • Active Fire Incident Map
    • Miscellaneous
      • AI Image Generation for Architecture Design
      • Convergent, Responsible, and Ethical AI Training Experience (CREATE Roboticists)
  • Team
  • Contact Us

October 18, 2024, Filed Under: Projects

AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem

Abstract: This paper introduces AI-FEED, a web-based platform (ai-feed.ai) designed to tackle food and nutrition insecurity within the food charity ecosystem. Using AI and blockchain technology, AI-FEED improves access to nutritious food and optimizes resource allocation, aiming to reduce food waste and enhance community health. It was developed through stakeholder interviews and features four modules catering to food charities, donors, clients, and community leaders. These modules provide educational content, streamline donations, and deliver real-time information. The platform facilitates data sharing and emphasizes ethical considerations and sustainability. AI-FEED showcases the power of interdisciplinary collaboration in addressing societal challenges.

The cover image is sourced from Pexels and is free of copyright issues.

For more information, please visit: https://link.springer.com/article/10.1007/s44196-024-00656-9

Share this:

UT Home | Emergency Information | Site Policies | Web Accessibility | Web Privacy | Adobe Reader

© The University of Texas at Austin 2025