How Data Trust Affects Real People

💭 𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐭𝐡𝐞 𝐩𝐞𝐫𝐬𝐨𝐧 𝐲𝐨𝐮 𝐭𝐫𝐮𝐬𝐭 𝐦𝐨𝐬𝐭 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰 𝐦𝐢𝐠𝐡𝐭 𝐬𝐢𝐭 𝐚𝐜𝐫𝐨𝐬𝐬 𝐟𝐫𝐨𝐦 𝐲𝐨𝐮 - 𝐚𝐧𝐝 𝐢𝐭’𝐬 𝐚 𝐦𝐚𝐜𝐡𝐢𝐧𝐞. We’ve entered an era where privacy no longer means who sees my data - but who truly knows me, and how I allow myself to be known. A senior exec once told me: “𝘚𝘰𝘮𝘦𝘵𝘪𝘮𝘦𝘴 𝘐 𝘧𝘦𝘦𝘭 𝘮𝘺 𝘵𝘦𝘢𝘮 𝘵𝘳𝘶𝘴𝘵𝘴 𝘊𝘩𝘢𝘵𝘎𝘗𝘛 𝘮𝘰𝘳𝘦 𝘵𝘩𝘢𝘯 𝘵𝘩𝘦𝘺 𝘵𝘳𝘶𝘴𝘵 𝘮𝘦.” That sentence says a lot about where we’re heading. 📊 Studies show that 𝟑𝟖% 𝐨𝐟 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 already share sensitive work information with AI tools - often more openly than with colleagues. And if we’re honest, many now discuss personal topics with AI more easily than with their partners at home. Think of a manager who starts every morning with her AI assistant. It helps her prepare for meetings, rewrites complex mails, even suggests how to motivate her team. Over time, it begins to understand her: her tone, her hesitation, her stress patterns. She starts confiding in it. It listens. It learns. It feels safe. Then one day, the company decides to connect all assistants to a central “leadership analytics” dashboard. 𝐒𝐮𝐝𝐝𝐞𝐧𝐥𝐲, 𝐰𝐡𝐚𝐭 𝐛𝐞𝐠𝐚𝐧 𝐚𝐬 𝐚 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐚 𝐜𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭. A mirror she never consented to share. That’s not just data. That’s 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 - and in my view, it must remain 𝐨𝐰𝐧𝐞𝐝 𝐛𝐲 𝐭𝐡𝐞 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥. Protected like a private diary, not monitored like corporate data. That’s the paradox: Every insight that makes a system caring also makes it capable of control. The data may belong to the individual, but the duty of care belongs to the organisation. That’s why the next governance frontier isn’t machine oversight - it’s 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐬𝐭𝐞𝐰𝐚𝐫𝐝𝐬𝐡𝐢𝐩. How do we design boundaries so that human–machine partnerships empower rather than expose? How do leaders ensure their people feel 𝐦𝐨𝐫𝐞 𝐡𝐮𝐦𝐚𝐧, not less, as they work alongside systems that now know them? Because the challenge ahead isn’t just to protect data. It’s to protect 𝐭𝐡𝐞 𝐝𝐢𝐠𝐧𝐢𝐭𝐲 𝐰𝐢𝐭𝐡𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩. #Leadership #DigitalEthics #TrustInTechnology #HumanCentredTransformation #DataGovernance 𝑉𝑖𝑑𝑒𝑜 𝑐𝑟𝑒𝑑𝑖𝑡𝑠 𝑡𝑜 @𝑒𝑝𝑖𝑐_𝑎𝑟𝑡𝑟𝑒𝑠𝑖𝑛

A big part of why I got into cybersecurity, and later, privacy, was out of concern for the effects that digital data and social media could have on the persecution of vulnerable communities. The Holocaust was one especially horrific example of how even analog data collection of persecuted communities can operationalize their genocide. With the emergence of digitalized data, this threat is scaled. The problems we have at hand now are bigger than what I could have imagined when I first entered this space a few years ago. We run the risk of not only hostile governments using our information against us, but of us using information about our race, ethnicities, religious identities and affiliations, political opinions, etc. against each other. When I first entered this space, the nightmare situation that kept me up at night was that information identifying vulnerable populations (based along religious, race, or other lines) might be released en-masse with the intention to cause harm to those communities. This nightmare situation is already coming to fruition - just weeks ago, news broke that #23andme had experienced a data breach affecting circa 1 million Ashkenazi Jewish users, and over 100,000 Chinese users (this article from EFF provides useful resources on what to do if you're concerned you may be affected: https://lnkd.in/dZ3y3Xzv). At the same time, the rise of generative AI leaves ample ground for the spread of increasingly mindbending, reality-resembling, disinformation and fake/manipulated content that contributes to the rise of polarization, online hate, and physical violence against persecuted groups (this article from the Anti-Defamation League describes some examples of how this is taking place in the Israel-Hamas war: https://lnkd.in/dkkxtrNJ). The nexus of these issues, the horrific violence in the Middle East these past few weeks, and the rise of anti-semitism and Islamophobia/anti-Arab sentiment in the US and across the world leaves me concerned for my family, friends, and colleagues - in the region and at home. I'm praying for all those who are affected by this conflict, and urge anyone who this may reach to think critically about what you read online and practice empathy for members of these communities who are struggling in these fearful times.

Your exposed data doesn’t just put you at risk - it can be used to exploit the people you love. Let’s say a bad actor finds just enough of your personal info online. Now they call your grandparent, pretend to be you, muffle their voice a little, and say they’re stranded while traveling. The story sounds urgent, and it feels real to your loved one. And your grandparent - wanting to help - sends as much money as they can spare. It’s a dirty trick. And it works more often than you’d think. This is why protecting your digital footprint isn’t just about safeguarding your own identity. Staying proactive about your privacy protects the people around you who trust you, who care about you, and who might not question the call when they think you’re in trouble. In those cases, your digital privacy isn’t just a nice-to-have. It's an essential part of keeping everyone else safe.

Here's an uncomfortable truth: We're asking the most vulnerable patients to trust us with their most sensitive data while giving them the least control over how we use it. Think about that for a moment. The communities most harmed by healthcare systems are also the ones who could benefit most from truly personalized care. But they're sitting in our waiting rooms, watching us collect their information, make decisions about their bodies, and wondering: "What happens to all this data about me?" We can't keep building personalized healthcare ON TOP of broken trust. We need Privacy-First Experience Hubs that put patients back in control: → Data decisions made WITH communities, not just FOR them → Build cultural intelligence into AI without sacrificing security → Turn legal jargon into plain-language transparency → Give patients ownership, not just access, to their health information Real talk: If your personalization strategy doesn't start with rebuilding trust, you're just creating more barriers for the people who need care most. The question isn't whether we can afford to make this change. It's whether we can afford not to. What would happen if your most vulnerable patients felt the safest in your system? #PatientExperience #HealthEquity #PatientTrust #HealthcareInnovation #SDOH #HealthcareCX #PatientEX

What happens when people start being judged by a single number based on their social status, network, and behavior? I have been intrigued by #SocialCreditScores for the last few years and am very happy to see this passion project published in a 🚨new paper🚨! (link below 👇) It sounds like something out of a dystopian episode of #BlackMirror, but Social Credit Scores exist for millions of people around the world and are becoming increasingly prevalent, even in western societies. Versions of them already exist in the forms of reputation platforms, rating systems, and even algorithmic trust scores used in hiring and fraud profiling. Despite the clear privacy and moral issue, the justification for SCS systems is that transparency leads to trust and encourages interactions. But what if the opposite is true? 🤔 In this paper we explore how the availability of Social Credit Scores impacts #trust, #cooperation, and #perceptions between individuals. Across three experiments, we find that when SCS scores are made available: 1️⃣ Overall trust behavior decreases ⬇️ 2️⃣ Cooperation drops, even among individuals who would have otherwise worked together ⬇️ 3️⃣ And most strikingly, even when they behave identically to others, people with lower scores are judged more harshly and rewarded less 😐 Social Credit Systems promise fairness and efficiency but we find that they can reinforce existing inequalities and bias our perceptions in lasting ways. ⚖️ If you’re interested in how algorithms, reputation, and human psychology intersect, and what it means for policy makers, regulators and industry leaders, follow the link below to read more... Full open-access paper at PLOS here: https://lnkd.in/esUj4ezH Erasmus University Rotterdam Rotterdam School of Management, Erasmus University

Data governance is usually framed as a compliance problem. In reality, it’s a human one. Good data governance is about building trust. We were brought in to build the data platform for a compensation programme handling highly sensitive medical, legal and financial information. The technical requirements were substantial. → Zero trust architecture → Role based access controls → Infrastructure as Code for rapid deployment Case officers needed to make decisions about compensation claims. Those decisions depended entirely on having reliable, complete information. Vulnerable citizens needed to trust that their sensitive data was protected and their claims would be handled with dignity and accuracy. Before the platform existed, data was fragmented. Spreadsheets scattered across teams. Manual reconciliation consuming hours that should have been spent on casework. No single source of truth. What this meant in practice was → Case officers spent time cross-referencing files instead of supporting claimants → Data inconsistencies created delays and uncertainty → Citizens had no visibility of claim status or timelines Building a unified data platform was about giving case officers the reliable foundation they needed to do their jobs effectively. And it was about treating vulnerable people with the dignity they deserve by ensuring their information was handled with care, accuracy and transparency. When you unify case data and eliminate spreadsheet sprawl, you restore trust in a broken system. Good data governance enables people to do meaningful work. That is what matters. What is the human cost of poor data governance in your organisation? #DataGovernance #PublicSector #Trust

Seven years. That's how long a single survey comment carried forward into a donor's story. Buried at the end of my latest survey data, I read this: "I took your survey seven years ago, and had mentioned there my partner was sick. I continue to give because you called to check on me within days after that survey." That moment of care? It wasn't random. It came from the previous survey. The one where this donor shared about her life when asked about future engagements. Someone on the team read that response, picked up the phone, and checked in. Seven years later, here it was again—this time in a new survey—telling us why the donor stayed, why they gave, why they felt connected. And it reminds us: a survey isn't a one-time thing. It's not just: ask → collect → report → done. It's a conversation. A process of trust-building - of listening and then acting. When people see their words turn into action, they respond differently the next time. Because it contributes to that trust. They show up. They share more. They believe the data has a life beyond the form they filled out. Nonprofit friends, can we commit to only use data listening tools as non-extractive bridges? #nonprofits #nonprofitleadership #community

The numbers are staggering: 78% of companies track user data across platforms. But here’s the real issue: Most users don’t know how much of their behavior is being monitored. Most companies treat “consent” as a checkbox, not a commitment. And in a digital-first economy, trust is the most valuable currency. Case in point: A recent global study revealed that while data collection has surged, consumer trust in corporations has declined sharply. The tension is clear:  → Businesses need data to personalize experiences.  → Users want control, transparency, and ethical handling. The leaders who will win in this new era are those who move from:  “How much data can we get?” to “How can we earn lasting trust?” Privacy-first frameworks are emerging: Transparent opt-ins, not hidden clauses. User data vaults controlled by the individual. AI systems that process data without storing sensitive identifiers. The lesson is simple: Companies that build trust-first, track-second will outlast those who treat data like a commodity. So here’s my question for you: Would you rather buy from a company that personalizes aggressively, or one that promises minimal data tracking with full transparency? P.S. Dropping impactful insights that matter in my weekly newsletter every Saturday, 10 AM EST. Don't miss it. Subscribe right here! https://lnkd.in/gcqfGeK4

What happens when you trust the model more than what's happening outside your window? We've been here before. The medical establishment spent decades insisting that dietary fat caused heart disease, despite populations eating traditional high-fat diets showing remarkable cardiovascular health. The model said one thing. Reality kept saying another. But the model had been peer-reviewed, funded, and repeated in every nutrition textbook. So we told millions of people to eat margarine instead of butter, seed oils instead of animal fats. Took fifty years before the data finally overwhelmed the consensus. The cartoon nails something deeper than weather prediction. It's about where we place our epistemic trust. Models are useful tools for understanding complexity. But when the model becomes more real than the phenomenon it's supposed to represent, we've entered a strange territory where observation itself needs permission from theory. The most interesting health breakthroughs over the next decade won't come from better models. They'll come from practitioners willing to notice what's actually working, even when it contradicts what should work according to the framework they were taught.

Just on the first anniversary of EHDS: An interesting paper by Teodora Lalova-Spinks and team on patients’ views on health data sharing in the context of the the European Health Data Space EHDS. It confirms that patients are not the barrier. They see the value of data sharing. But their support depends on trust, and trust is not created through consent forms or regulation alone. It comes from transparency, safeguards, and a clear sense that data use leads to meaningful outcomes. It depends on good governance, especially at times when US big tech don’t give a shit about giving citizens a role un supervising data use. Patients should have a role in data governance, not just agreeing to data use, but shaping how it happens. This puts patient organisations in a very different position than how they are often treated. Not as engagement channels, but as part of the governance infrastructure — as actors that build legitimacy, act as co-governors and translators of patient value into system design. The problem is, we expect them to play that role without giving them the mandate or resources to do so, and we don’t train them on understanding and taking those roles. EHDS will not succeed because it is well designed, has a solid legal framework, provides data standards or runs great platforms. It will succeed if it earns trust and if we have a cultural shift on data sharing and governance. Otherwise it will fail. And that trust will largely depend on whether we finally take the role of patient organisations seriously. 
Thanks Teodora! Open access text: https://lnkd.in/etk4n6JU