Trust and power in digital aid systems

“𝐂𝐚𝐧 𝐈 𝐭𝐚𝐥𝐤 𝐭𝐨 𝐚 𝐡𝐮𝐦𝐚𝐧, 𝐩𝐥𝐞𝐚𝐬𝐞?” 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐬𝐭𝐢𝐥𝐥 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐦𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐧 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. Not because technology is slow. But because trust is missing. The numbers are clear: 👉 37% of people have never used a digital assistant. 👉 74% prefer a human - even for simple questions. 👉 Only 27% trust digital systems when advice or judgment is needed. That is not an adoption problem. It is a confidence problem. A simple example. You ask a system: “𝐈𝐬 𝐭𝐡𝐢𝐬 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐟𝐨𝐫 𝐦𝐞?” It answers instantly. Sounds confident. Uses perfect language. But it cannot explain why. It cannot say where it might be wrong. And 𝐢𝐭 𝐜𝐚𝐧𝐧𝐨𝐭 𝐭𝐚𝐤𝐞 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲. That is the moment people pull back. Most digital systems work well for: ✅ status checks ✅ simple questions ✅ saving time But they struggle when: ❌ context changes ❌ emotions matter ❌ consequences are real And this is where leadership matters. For years, automation was built to reduce cost. Users experience it as a risk. 𝐒𝐩𝐞𝐞𝐝 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 𝐟𝐞𝐞𝐥𝐬 𝐮𝐧𝐬𝐚𝐟𝐞. Correct answers without empathy feel cold. Decisions without escalation feel dangerous. The next generation of digital systems will not win because they are smarter. They will win because they know: ✔️ when to answer ✔️ when to explain ✔️ and when to bring in a human This is not about replacing people. It is about building systems people can rely on. So here is the real question for leaders: 𝐈𝐟 𝐩𝐞𝐨𝐩𝐥𝐞 𝐝𝐨𝐧’𝐭 𝐭𝐫𝐮𝐬𝐭 𝐲𝐨𝐮𝐫 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐯𝐨𝐢𝐜𝐞, 𝐰𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐲 𝐚𝐛𝐨𝐮𝐭 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐝𝐞𝐬𝐢𝐠𝐧 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲? What builds trust faster today: better answers - or clearer ownership? 𝘛𝘳𝘶𝘴𝘵 𝘪𝘴 𝘭𝘪𝘬𝘦 𝘨𝘭𝘢𝘴𝘴. 𝘌𝘢𝘴𝘺 𝘵𝘰 𝘣𝘳𝘦𝘢𝘬. 𝘏𝘢𝘳𝘥 𝘵𝘰 𝘴𝘩𝘢𝘱𝘦. 𝘗𝘰𝘸𝘦𝘳𝘧𝘶𝘭 𝘸𝘩𝘦𝘯 𝘥𝘰𝘯𝘦 𝘳𝘪𝘨𝘩𝘵. 𝘈𝘳𝘵 𝘣𝘺 𝘚𝘪𝘮𝘰𝘯 𝘉𝘦𝘳𝘨𝘦𝘳.

𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁 - 𝘄𝗵𝗮𝘁 𝗲𝘃𝗲𝗿𝘆 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗸𝗻𝗼𝘄! Most organisations talk about trust as if it were a value. Something cultural. Something soft. Something you earn. That is a mistake. Trust is not a feeling. 𝗧𝗿𝘂𝘀𝘁 𝗶𝘀 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗿𝗼𝗽𝗲𝗿𝘁𝘆. In modern enterprises, especially those deploying AI and autonomous systems, trust does not emerge from intention. It emerges from structure. Design defines what an actor is allowed to decide. Governance constrains those decisions within acceptable bounds. Architecture makes those constraints executable, repeatable, and real. This is not optional. It is load bearing. ‼️ Skip design and there is nothing to govern. ‼️ Skip governance and risk runs unmanaged. ‼️ Skip architecture and rules exist only on slides. This is why so many AI initiatives feel fragile. Policies are written, but cannot be enforced. Controls are discussed, but not encoded. Accountability is assumed, but never anchored in the system itself. What we call “trust” is simply the outcome of decisions being made inside a structure that can explain itself, constrain itself, and survive scale. If AI is acting like an employee, it needs an architecture that defines what it may do, what it must not do, and what happens when reality deviates from assumptions. 𝗧𝗿𝘂𝘀𝘁 𝗶𝘀 𝗻𝗼𝘁 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗮𝗱𝗱 𝗹𝗮𝘁𝗲𝗿. 𝗜𝘁 𝗶𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝘂𝗽 𝗳𝗿𝗼𝗻𝘁. If this resonates, or makes you uncomfortable, that is the point. This is the conversation leaders need to have before autonomy accelerates further. What valuable insights can you add to this conversation?

🚨 𝗧𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 "𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗵𝘂𝗺𝗮𝗻𝗶𝘁𝗮𝗿𝗶𝗮𝗻𝗶𝘀𝗺"—𝗮 𝘄𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗿𝗲𝗳𝘂𝗴𝗲𝗲 𝗮𝗶𝗱. A powerful new piece from 𝗠𝗘𝗥𝗜𝗣 exposes how digital technologies — biometrics, blockchain, predictive analytics, and even humanitarian robots—are reshaping the way aid is delivered to refugees. But at what cost?  💡 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The shift toward "data-driven humanitarianism" is not just about efficiency—it’s about 𝗽𝗼𝘄𝗲𝗿. The same digital systems used to "help" refugees are also fueling 𝗺𝗶𝗹𝗶𝘁𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝘀𝘂𝗿𝘃𝗲𝗶𝗹𝗹𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗳𝗶𝘁-𝗺𝗮𝗸𝗶𝗻𝗴 at their expense.  👉 This is why we must 𝗽𝘂𝘀𝗵 𝗳𝗼𝗿 𝗔𝗜 𝗲𝘁𝗵𝗶𝗰𝘀, 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻𝘀 in humanitarian aid. Refugees are 𝗻𝗼𝘁 𝘁𝗲𝘀𝘁 𝘀𝘂𝗯𝗷𝗲𝗰𝘁𝘀 𝗳𝗼𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹 𝘁𝗲𝗰𝗵—their rights and dignity must come first.  🔹 In Jordan’s 𝗭𝗮’𝗮𝘁𝗮𝗿𝗶 𝗿𝗲𝗳𝘂𝗴𝗲𝗲 𝗰𝗮𝗺𝗽, Syrian refugees must scan their 𝗶𝗿𝗶𝘀𝗲𝘀 just to buy food. Their transactions are logged on a blockchain, tying their survival to digital systems they have little control over.  🔹 The UN’s 𝗯𝗶𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 (BIMS) has now expanded globally, with 𝗼𝘃𝗲𝗿 𝟯𝟳 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗿𝗲𝗳𝘂𝗴𝗲𝗲𝘀 registered. It tracks life events from 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝘁𝗼 𝗱𝗲𝗮𝘁𝗵, raising major concerns about 𝗱𝗮𝘁𝗮 𝗽𝗿𝗶𝘃𝗮𝗰𝘆, 𝘀𝘂𝗿𝘃𝗲𝗶𝗹𝗹𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗰𝗼𝗲𝗿𝗰𝗶𝗼𝗻.  🔹 𝗧𝗲𝗰𝗵 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁𝘀 are capitalizing on humanitarian crises to develop AI-driven predictive models, autonomous aid delivery, and expansive 𝗱𝗮𝘁𝗮 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀—but who benefits? With 𝘄𝗲𝗮𝗸 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗹𝗮𝘄𝘀, anonymized refugee data can be 𝘀𝗼𝗹𝗱, 𝘀𝗵𝗮𝗿𝗲𝗱, 𝗮𝗻𝗱 𝗲𝘅𝗽𝗹𝗼𝗶𝘁𝗲𝗱 long after the crisis ends.  🔹 Meanwhile, 𝗿𝗼𝗯𝗼𝘁𝘀 𝗮𝗻𝗱 𝗔𝗜 are being positioned as the future of humanitarian response, raising troubling questions about 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗯𝗶𝗮𝘀, 𝗰𝗼𝗻𝘀𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴. Are we really comfortable with AI dictating 𝘄𝗵𝗼 𝗴𝗲𝘁𝘀 𝗮𝗶𝗱 𝗮𝗻𝗱 𝘄𝗵𝗼 𝗱𝗼𝗲𝘀𝗻’𝘁?  Check it out for yourself: https://lnkd.in/eG5uEdDC What are your thoughts on AI in humanitarian work? Should digital tools play a larger role, or do they introduce new risks? Let’s discuss! ⬇️ #AIethics #ResponsibleAI #DigitalHumanitarianism

Power doesn’t disappear in the age of AI—it just gets harder to see. My dear friend Tiffany A. Archer, Esq. is one of the sharpest ethics and behavioral science minds I know, and her latest piece in Leader to Leader is required reading for any leader navigating AI, global teams, and institutional trust. She reminds us that power operates beneath the surface—signaled, challenged, and interpreted differently across cultures and shows how power distance shapes everything from how leaders receive feedback to how organizations handle ethical failures. What struck me most is her warning for the AI era: even ethically designed tools will fail if leaders ignore cultural power dynamics. Authority isn’t just embedded in systems—it’s communicated, perceived, and contested by people. This is leadership work, not technology work. And it’s long overdue. If you care about trust, ethics, and making AI actually work across borders, give this a read—and follow Tiffany’s thinking closely. You can find her article here: https://lnkd.in/gNrSeyxS #leadership #AI #power #teams #trust

The AI Equity Project is coming back for its third round, and I am excited + grateful to say this work is continuing. When AI Equity started 30-ish months ago, the core question was not-so-simple but persistent: How do we know AI in our sector isn’t shaped by/doesn’t shape inequity, power imbalances, and extraction? This project exists to hold that question from the vantage point of nonprofits, fundraisers, community organizations, and philanthropy staff — the voices that are often told what to do with tech when those voices are the ones interacting and engaging communities outside this sector every day. In 2024, the first study focused on orientation and lived experience: ● how are nonprofits actually using or avoiding AI? ● what fears, hopes, and boundaries are showing up in staff and leadership? ● how do gaps appear when AI is introduced into already stretched teams? To me that first round of solid evidence gathering of single, unified, sector-wide snapshot said: curiosity and experimentation sit right next to exhaustion, ambivalence, and a deep worry about putting community stories into opaque systems. In 2025, the second round turned toward systems and structures: ● how are organizations thinking about AI policies, governance, and risk? ● do we have awareness of “responsible AI” when we do not have a data team or innovation budget? ● how are funders, vendors, and infrastructure tools supporting AI adoption? In it, trust, power, capacity, and risk-sharing came through strongly. And once again, AI equity became a topic less as a technology problem and more as a relationships-and-governance problem. With Round 3 this year, I am especially interested in moving the needle in a different + stronger way. The research will still be listening, documenting, and naming patterns. But this year, the focus is on how we translate those insights into more concrete ways for nonprofits and philanthropy to: ● share and redistribute access and understanding around AI, and ● let AI be called upon to reflect values, not just for efficiency or fear of being left behind. I will share more about this year’s focus, opportunities to participate, and ways nonprofits can engage with the next phase of AI Equity in the coming weeks. For now, I wanted to mark this moment publicly, joyfully, intentionally: Round 3 is beginning. This project isn’t easy work by any means. But I am deeply, deeply grateful to everyone who has walked alongside this project so far. Thus, the story continues... #nonprofits #community