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

被引:0
|
作者
Sammer, Marcus [1 ]
Seong, Kijin [2 ]
Olvera, Norma [1 ]
Gronseth, Susie L. [1 ]
Anderson-Fletcher, Elizabeth [1 ]
Jiao, Junfeng [2 ]
Reese, Alison [3 ]
Kakadiaris, Ioannis A. [1 ]
机构
[1] Univ Houston, Houston, TX 77204 USA
[2] Univ Texas Austin, Austin, TX 78712 USA
[3] Tackle Hunger, Bee Cave, TX 78738 USA
基金
美国国家科学基金会;
关键词
Healthy food access; AI solutions for food charities; Food charity ecosystem; Non-profit blockchain application;
D O I
10.1007/s44196-024-00656-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem's unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED's integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.
引用
收藏
页数:16
相关论文
共 50 条
  • [11] Weight Loss with an AI-Powered Digital Platform for Lifestyle Intervention
    Sarfraz Khokhar
    John Holden
    Catherine Toomer
    Angelo Del Parigi
    Obesity Surgery, 2024, 34 : 1810 - 1818
  • [12] AI-POWERED FLOOD MAPATHON
    Chen, Kaiqiang
    Lu, Xue
    Shen, Taowei
    Liu, Xiaoyu
    Chen, Jialiang
    Wang, Zhirui
    Sun, Xian
    Haensch, Ronny
    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024, 2024, : 3841 - 3844
  • [13] AI-powered neural implants
    N. A. Sudharson
    M. Joseph
    N. Kurian
    K. G. Varghese
    S. Wadhwa
    H. A. Thomas
    British Dental Journal, 2023, 234 : 359 - 360
  • [14] AI-powered aptamer generation
    Majid Khabbazian
    Hosna Jabbari
    Nature Computational Science, 2022, 2 : 356 - 357
  • [15] On the Engineering of AI-Powered Systems
    Kusmenko, Evgeny
    Pavlitskaya, Svetlana
    Rumpe, Bernhard
    Stueber, Sebastian
    2019 34TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING WORKSHOPS (ASEW 2019), 2019, : 126 - 133
  • [16] AI-powered aptamer generation
    Khabbazian, Majid
    Jabbari, Hosna
    NATURE COMPUTATIONAL SCIENCE, 2022, 2 (06): : 356 - 357
  • [17] AI-Powered Research Assistants
    Ojala, Marydee
    Computers in Libraries, 2023, 43 (12) : 43 - 44
  • [18] Complex business ecosystem intelligence using AI-powered visual analytics
    Basole, Rahul C.
    Park, Hyunwoo
    Seuss, David
    DECISION SUPPORT SYSTEMS, 2024, 178
  • [19] Enhancing Software Modeling Learning with AI-Powered ScaffoldingEnhancing Software Modeling Learning with AI-Powered Scaffolding
    Ardimento, Pasquale
    Bernardi, Mario Luca
    Cimitile, Marta
    Scalera, Michele
    ACM/IEEE 27TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS: COMPANION PROCEEDINGS, MODELS 2024, 2024, : 103 - 106
  • [20] SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis
    Aboulmira, Amina
    Hrimech, Hamid
    Lachgar, Mohamed
    Camara, Aboudramane
    Elbahja, Charafeddine
    Elmansouri, Amine
    Hassini, Yassine
    SYSTEMS AND SOFT COMPUTING, 2024, 6