[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2653":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":70},2653,"Artificial intelligence and food insecurity: opportunities, risks, and equity pathways for the Global South","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2730680","Food insecurity affects 691–783 million people globally, with disproportionate impacts on low-income and middle-income countries, marginalized groups, and conflict-affected populations. Artificial intelligence (AI) presents unprecedented opportunities to address food security challenges through precision agriculture, supply chain optimization, and early warning systems, achieving up to 91% accuracy in crop yield prediction and reducing food waste by 22%. Its deployment risks perpetuating existing inequities. Drawing on a systematic literature search of 64 peer-reviewed studies across Scopus, Web of Science, PubMed, AGRIS, and IEEE Xplore (2015–2025), this review examines AI’s double-edged role in food systems through a comprehensive framework encompassing inclusive data practices, equitable governance, participatory design, and local capacity building. Key risks include data colonialism, algorithmic bias favoring industrial agriculture, the digital exclusion of vulnerable communities, and technological dependency that threatens food sovereignty. However, evidence from successful implementations in sub-Saharan Africa demonstrates that inclusive approaches can enhance productivity while promoting gender equality and environmental sustainability. The study concludes that AI’s impact of AI fundamentally depends on the design principles and governance frameworks that prioritize equity. Without inclusive implementation strategies, AI may reinforce hunger gaps; conversely, equity-centered approaches can contribute to resilient, just, and food-secure systems for all populations.","粮食不安全影响着全球6.91亿至7.83亿人，对低收入和中等收入国家、边缘化群体以及受冲突影响人口的影响尤为严重。人工智能（AI）通过精准农业、供应链优化和早期预警系统，为解决粮食安全挑战提供了前所未有的机遇，在作物产量预测中实现了高达91%的准确率，并将粮食浪费减少了22%。然而，其部署也可能固化现有的不平等。本文基于对Scopus、Web of Science、PubMed、AGRIS和IEEE Xplore数据库中64项同行评审研究（2015—2025年）的系统性文献检索，通过一个涵盖包容性数据实践、公平治理、参与式设计和本地能力建设的综合框架，审视了AI在粮食系统中的双刃剑作用。主要风险包括数据殖民主义、偏向工业化农业的算法偏见、对脆弱社区的数字排斥，以及威胁粮食主权的技术依赖。然而，来自撒哈拉以南非洲成功实施的证据表明，包容性方法可以在提高生产力的同时促进性别平等和环境可持续性。研究得出结论，AI的影响从根本上取决于优先考虑公平的设计原则和治理框架。缺乏包容性实施策略，AI可能加剧饥饿差距；反之，以公平为中心的方法可以为所有人群构建具有韧性、公正且粮食安全的体系。",null,"Cogent Food & Agriculture","2026-09-14T00:00:00Z","论文",10,false,83,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,13,8,1,"基于64篇文献的系统综述，系统梳理AI在粮食安全中的双刃剑效应与公平治理路径，数据与框架均有实质增量，对农业信息化政策设计具参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","粮食安全","数字鸿沟","全球南方",0,"10.1080\u002F23311932.2026.2730680",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":61,"card":62,"direction":68,"ingested_from":69},"W7213346271",[36,39,42,45,48,51,54,57,59],{"name":37,"orcid":38},"Ahmed Abdiaziz Alasow","https:\u002F\u002Forcid.org\u002F0000-0002-9888-6131",{"name":40,"orcid":41},"Yusuf Hared Abdi","https:\u002F\u002Forcid.org\u002F0009-0002-7224-2247",{"name":43,"orcid":44},"Abdimalik Ali Warsame","https:\u002F\u002Forcid.org\u002F0000-0001-6130-5607",{"name":46,"orcid":47},"Yakub Burhan Abdullahi","https:\u002F\u002Forcid.org\u002F0009-0001-8535-198X",{"name":49,"orcid":50},"Mohamed Sharif Abdi","https:\u002F\u002Forcid.org\u002F0009-0007-0220-4014",{"name":52,"orcid":53},"Shazia Bashir","https:\u002F\u002Forcid.org\u002F0009-0007-6900-9870",{"name":55,"orcid":56},"Nova Ahmed","https:\u002F\u002Forcid.org\u002F0009-0009-9058-4807",{"name":58,"orcid":9},"Shuaibus Saidu Musa",{"name":60,"orcid":9},"Don Eliseo Lucero-Prisno","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F23311932.2026.2730680?needAccess=true",{"tldr":63,"method":64,"finding":65,"direction":66,"opportunity":67},"系统综述64项研究，分析AI在全球南方粮食安全中的机遇、风险与公平路径。","系统文献综述，检索Scopus、WoS、PubMed、AGRIS、IEEE Xp","AI可提升产量预测与减少浪费，但数据殖民、算法偏见等风险可能加剧不平等。","数字乡村与农业信息化","可实证检验公平导向的AI治理框架在低收入国家小农户中的落地效果与机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:17.199269Z"]