The Importance of Trust in Autonomous Operations

A lesson from self-driving cars… Healthcare's AI conversation remains dangerously incomplete. While organizations obsess over provider adoption, we're neglecting the foundational element that will determine success or failure: trust. Joel Gordon, CMIO at UW Health, crystallized this at a Reuters conference, warning that a single high-profile AI error could devastate public confidence sector-wide. His point echoes decades of healthcare innovation: trust isn't given—it's earned through deliberate action. History and other industries can be instructive here. I was hoping by now we’d have fully autonomous self-driving vehicles (so my kids wouldn’t need a real driver’s license!), but early high-profile accidents and driver fatalities damaged consumer confidence. And while it’s picking up steam again, but we lost some good years as public trust needed to be regained. We cannot repeat this mistake with healthcare AI—it’s just too valuable and can do so much good for our patients, workforce, and our deeply inefficient health systems. As I've argued in my prior work, trust and humanity must anchor care delivery. AI that undermines these foundations will fail regardless of technical brilliance. Healthcare already battles trust deficits—vaccine hesitancy, treatment non-adherence—that cost lives and resources. AI without governance risks exponentially amplifying these challenges. We need systematic approaches addressing three areas:   Transparency in AI decision-making, with clear explanations of algorithmic conclusions. WHO principles emphasize AI must serve public benefit, requiring accountability mechanisms that patients and providers understand.   Equity-centered deployment that addresses rather than exacerbates disparities. There is no quality in healthcare without equity—a principle critical to AI deployment at scale.   Proactive error management treating mistakes as learning opportunities, not failures to hide. Improvement science teaches that error transparency builds trust when handled appropriately. As developers and entrepreneurs, we need to treat trust-building as seriously as technical validation. The question isn't whether healthcare AI will face its first major error—it's whether we'll have sufficient trust infrastructure to survive and learn from that inevitable moment. Organizations investing now in transparent governance will capture AI's potential. Those that don't risk the fate of other promising innovations that failed to earn public confidence. #Trust #HealthcareAI #AIAdoption #HealthTech #GenerativeAI #AIMedicine https://lnkd.in/eEnVguju

𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐦𝐞𝐚𝐧 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐰𝐞 𝐜𝐚𝐧 𝐭𝐫𝐮𝐥𝐲 𝐭𝐫𝐮𝐬𝐭? Long before generative and agentic AI made headlines, early warning signs were already showing us what happens when technology advances without the right guardrails. These moments continue to shape the way we build responsible AI today. Well-intentioned AI systems can cause unintended harm. A few examples that have stayed with me: 🤝🏼 An AI model built to detect welfare fraud ran with less than 10% accuracy—leading to false accusations against vulnerable families. 💼 A recruiting algorithm designed to find “star performers” learned to penalize resumes that didn’t fit narrow, Ivy League-shaped data—including anyone who had led a Girl Scout troop. 🚨 In Spain, a predictive model meant to assess domestic abuse risk failed to act in critical cases. Only after many women died while flagged as low-risk was the model finally audited. All of these examples happened before today’s generative and agentic AI—and they remind us why trust, transparency, and accountability must be designed into AI from the start. Tech isn’t neutral—𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐚𝐤𝐞 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞. And we have a responsibility to shape AI that serves society with integrity. 🤔What is the kind of relationship that you wish to have with AI? #TrustworthyAI #ResponsibleAI

The AI gave a clear diagnosis. The doctor trusted it. The only problem? The AI was wrong. A year ago, I was called in to consult for a global healthcare company. They had implemented an AI diagnostic system to help doctors analyze thousands of patient records rapidly. The promise? Faster disease detection, better healthcare. Then came the wake-up call. The AI flagged a case with a high probability of a rare autoimmune disorder. The doctor, trusting the system, recommended an aggressive treatment plan. But something felt off. When I was brought in to review, we discovered the AI had misinterpreted an MRI anomaly. The patient had an entirely different condition—one that didn’t require aggressive treatment. A near-miss that could have had serious consequences. As AI becomes more integrated into decision-making, here are three critical principles for responsible implementation: - Set Clear Boundaries Define where AI assistance ends and human decision-making begins. Establish accountability protocols to avoid blind trust. - Build Trust Gradually Start with low-risk implementations. Validate critical AI outputs with human intervention. Track and learn from every near-miss. - Keep Human Oversight AI should support experts, not replace them. Regular audits and feedback loops strengthen both efficiency and safety. At the end of the day, it’s not about choosing AI 𝘰𝘳 human expertise. It’s about building systems where both work together—responsibly. 💬 What’s your take on AI accountability? How are you building trust in it?

✅ Day 5 → Guardrails, Governance, and Why Trust Is Everything As AI agents become more capable, setting clear boundaries becomes even more important. Guardrails define the limits of what an AI agent is allowed to do. They ensure it only accesses the right data, takes on the right tasks, and knows when a human needs to step in. Governance is the system that supports those guardrails. It answers key questions:  ✅Who built the agent?  ✅Where does it get its information?  ✅Can we trace its actions and understand how it reached a decision? Without governance, AI becomes a black box and in business, black boxes don’t scale. Take a simple use case: an AI agent that sends customer emails. Guardrails would prevent it from responding to legal complaints or escalating billing errors without human review. Governance ensures that every email is logged, and you can explain how and why it was sent. Trust is the multiplier. Without it, AI adoption stalls. With it, AI becomes a true partner in scaling smart, safe, and responsible systems. And that trust isn’t something you patch on later, you build it in from the start. #AgenticAI

Just came across an insightful piece by Diginomica discussing the significance of trust in data for AI adoption, based on TELUS Digital's recent survey findings. https://lnkd.in/gq8tfnfe In a nutshell, TELUS Digital's June surveys reveal compelling insights: - 87% of U.S. adults emphasize the importance of companies being transparent about their AI training data sources, showing a notable increase from 75% in 2023. - The introduction of human oversight significantly boosts public confidence in AI, particularly in critical domains like healthcare, where confidence levels surged from 35% to 61%. While trust emerges as a pivotal factor, diginomica raises pertinent queries: What forms of trust are essential? Whose trust is at stake? How can organizations effectively earn trust? It's not enough to merely assert the criticality of trust. The real challenge lies in deconstructing it—delving into ethics, governance, data integrity, transparent resourcing, and the integration of human oversight in practical AI systems. In my view, leaders must transcend mere rhetoric and establish intricate frameworks that amalgamate expert curation, bias mitigation strategies, third-party evaluations, and active stakeholder involvement. A holistic approach is key to cultivating trust in AI applications.