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Humanitarian AI Today

Humanitarian AI Today

著者： Humanitarian AI Today
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Humanitarian AI Today is the leading AI for Good podcast series focusing on humanitarian applications of artificial intelligence. We interview leaders, developers and innovators advancing humanitarian applications of AI from across the tech and humanitarian communities. The series is produced by the Humanitarian AI meetup.com community, linking local groups in Cambridge, San Francisco, Seattle, New York City, Toronto, Montreal, London, Paris, Berlin, Oslo, Geneva, Zurich, Bangalore, Tel Aviv and Tokyo.All rights reserved
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  • Priyank Hirani from Data.org on Scaling Inclusive AI for Social Impact

    Priyank Hirani from Data.org on Scaling Inclusive AI for Social Impact
    2026/06/04
    Priyank Hirani, Vice President of Programs at data.org discusses the India AI Impact Summit and India’s role as a development space and test bed for responsible and inclusive AI applications with Humanitarian AI Today Producer, Brent Phillips. In this insightful conversation, Hirani highlights how India is shifting the global narrative away from frontier model competition toward practical, population-scale AI deployment that addresses real-world challenges in health, agriculture, and finance. By building directly upon a decade of robust digital public infrastructure, the Indian ecosystem accelerates adoption through lean, cost-effective and context-aware innovation. Hirani emphasizes how this unique ecosystem serves as a powerful blueprint for the global majority, championing "South-South" collaborations and responsible and inclusive AI innovation. The episode underscores data.org’s mission as a global catalyst, capacity builder, and convener in the social impact sector. Hirani discusses the organization’s ambitious goal to cultivate one million purpose-driven data professionals by 2032 through its expanding Capacity Accelerator Network. Looking ahead to the future of technology, he shares a compelling vision for "Civic AI” and the creation of persistent, multilingual, and context-aware personal AI agents designed to help citizens frictionlessly navigate complex public systems. The conversation is packed with updates on data.org’s latest initiatives, including regional hackathons and the Finverse, a decision support tool and resource hub designed to help organizations in the Asia Pacific (APAC) region use data and AI to improve the financial health of the individuals and communities they serve. This episode is a must-listen for social impact professionals, researchers, and anyone invested in a more equitable and human-centered AI future.
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    24 分
  • Jarrod Goentzel on MIT’s Humanitarian Supply Chain Lab, AI and System Level Thinking

    Jarrod Goentzel on MIT’s Humanitarian Supply Chain Lab, AI and System Level Thinking
    2026/05/29
    In this Humanitarian AI Today Voices flashpod, Eric Talbert, Co-founder of MedCycle Network guest hosts an interview with Jarrod Goentzel, founder and director of the MIT Humanitarian Supply Chain Lab in the MIT Center for Transportation & Logistics. This interview dives into the evolution and modern practices of the MIT Humanitarian Supply Chain Lab for humanitarian professionals looking to optimize crisis response through system-level thinking and technology. The discussion traces the lab’s journey from its origins during the 2004 Indian Ocean tsunami to its needs-assessment work and market-resilience studies to it’s general shift away from reactive, event-specific planning toward building structural, "system-level" understandings of supply chains and how organizations can better anticipate bottlenecks and coordinate with the private sector. For humanitarian professionals, the interview offers a grounded, pragmatic perspective on integrating artificial intelligence into crisis response. Goentzel explicitly addresses the limitations of relying solely on automated systems, noting that AI inherently struggles with data gaps, as it is bounded by what is publicly available and cannot easily synthesize entirely unique disaster contexts on its own. To overcome this, the MIT Humanitarian Supply Chain Lab utilizes AI as an initial data-gathering and pattern-matching catalyst, which is then verified through a human-in-the-loop framework. The lab deploys a network of real-time ground-truthers who are trusted professionals embedded within the supply chain who validate the AI's outputs. This hybrid model ensures that automated data collection never compromises the absolute operational integrity required when delivering life-saving aid to vulnerable populations. The interview touches upon "polycentric governance," which is the concept of humans organically cooperating to manage common resources during crises. The lab models supply chains as complex adaptive systems and conducts "Blue Sky Studies”which are highly detailed structural mapping done when there is no active emergency to locate vulnerabilities before disaster strikes. A prime example of this is the lab’s SCAN (Supply Chain Analysis Network) mapping, which evaluated infrastructural bottlenecks in transportation and fuel pipelines. Looking toward the future of humanitarian tech, the conversation highlights cutting-edge applications of predictive modeling and advanced AI training. For AI developers, Goentzel offer’s a futuristic vision for disaster AI: rather than letting a machine application start from scratch during an active crisis, the lab is actively researching ways to pre-embed AI with complex supply chain network science and system dynamics. By providing the machine with a sophisticated baseline of structural interdependencies beforehand, the AI can immediately interpret real-time news and data influxes with extreme speed. This effectively frees up human humanitarian leaders to step away from the information onslaught and focus entirely on creating the rapid physical and collaborative connections needed to save lives. The MIT Humanitarian Supply Chain Lab offers resources and educational platforms to connect researchers, technology experts, and ground-level aid workers. Goentzel invites listeners to join the lab’s humanitarian supply chain community and take advantage of free online course developed by the lab, like the lab’s free Humanitarian Logistics course through MITx: https://www.edx.org/learn/business-administration/massachusetts-institute-of-technology-humanitarian-logistics An article on the Lab's supply chain resilience work can be found here: https://ctl.mit.edu/sites/default/files/documents/scmr-innovation-strategies-september-2025.pdf To learn more about Eric Talbert’s work and the MedCycle Network, check out his interview on the Grow Healthy, Help People Podcast: https://youtu.be/w495cOVVajw?si=EMZLr-zZXAWM93Oq
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    33 分
  • Ruben Lozano Aguilera from Ai2 on Asta's AutoDiscovery Tool for Scientific Discovery

    Ruben Lozano Aguilera from Ai2 on Asta's AutoDiscovery Tool for Scientific Discovery
    2026/05/12
    Rubén Lozano-Aguilera, Product Lead for Asta at the Allen Institute for AI (Ai2), explores the transformative potential of agentic AI in scientific research with Humanitarian AI Today guest host Lindsey Moore, Founder of DevelopMetrics. Rubén introduces AutoDiscovery, a powerful new tool developed by his team that moves beyond traditional query-based analysis to autonomously generate and test scientific hypotheses. This shift from manual data processing to autonomous discovery offers a powerful force multiplier for researchers, helping them surface blind spots and hidden patterns that traditional methods often overlook in fields ranging from melanoma research to marine ecology. For humanitarian and development organizations, Ai2's work represents a vital new advancement in what Rubén calls "shared AI infrastructure." Ai2's deep commitment to the open-source movement, providing open models, checkpoints, code, and training data, ensures transparency and accessibility for all. This approach is particularly impactful for organizations operating in resource-constrained environments, as it allows them to leverage state-of-the-art predictive analytics without the high costs or "black box" risks associated with proprietary systems. By democratizing access to high-level research tools, Ai2 enables any researcher or developer to maintain data ownership while utilizing sophisticated AI to solve the world's most pressing problems. The conversation next turned to the deeper philosophical stakes of automating scientific discovery itself. Drawing on his graduate research in AI ethics at Cambridge, Rubén separates what philosophers of science call the "context of discovery”, how a hypothesis is generated, from the "context of justification," how it is tested and validated. The worry is deskilling: as scientists offload hypothesis generation to AI, will they lose the instincts needed to catch when the machine is wrong? His answer centers on cultivating "meta-AI skills", the practiced ability to evaluate AI outputs critically. That raises its own problems: how do those skills get built, and are they really the same kind of skill as the hypothesis-generating instincts they would replace? Ai2 is actively studying this by examining how tools like AutoDiscovery affect students and early-career researchers. For humanitarian and development professionals navigating an era of shrinking research budgets and growing AI adoption, these added points raise essential questions about keeping human judgment at the center of discovery.
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    25 分
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