How AI Affects Trust and Safety

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?

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

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

🔍 Ethics in AI for Healthcare: The Foundation for Trust & Impact As AI transforms healthcare, from diagnostics to clinical decision-making, ethics must be at the center of every advancement. Without strong ethical grounding, we risk compromising patient care, trust, and long-term success. 💡 Why ethics matter in healthcare AI: ✅ Patient Safety & Trust: AI must be validated and monitored to prevent harm and ensure clinician and patient confidence. ✅ Data Privacy: Healthcare data is highly sensitive, ethical AI demands robust privacy protections and responsible data use. ✅ Bias & Fairness: Algorithms must be stress-tested to avoid reinforcing disparities or leading to unequal care outcomes. ✅ Transparency: Clinicians and patients deserve to understand why AI makes the decisions it does. ✅ Accountability: Clear lines of responsibility are essential when AI systems are used in real-world care. ✅ Collaboration Over Competition: Ethical AI thrives in open ecosystems, not in siloed, self-serving environments. 🚫 Let’s not allow hype or misaligned incentives to compromise what matters most. As one physician put it: “You can’t tout ethics if you work with organizations that exploit behind the scenes.” 🤝 The future of healthcare AI belongs to those who lead with integrity, transparency, and a shared mission to do what’s right, for patients, for clinicians, and for the system as a whole. #AIinHealthcare #EthicalAI #HealthTech

Friends in sales! A new Harvard Business Review article reveals what I've been saying all along about LLMs: "the root issue isn't technological. It's psychological." Here are Six Principles (and why behavioral change is everything): This study focuses on customer service chatbots, but the insights inform AI adoption across organizations (and what leaders need to do differently). LLMs don't behave like software. They behave like humans. Which means we need a human behavioral approach. This is about change management. (BTW, this is what we do at AI Mindset at scale, but more on that below.) ++++++++++++++++++++ SIX PSYCHOLOGICAL PRINCIPLES FOR EFFECTIVE CUSTOMER SERVICE CHATBOTS: 1. Label Your AI as "Constantly Learning" Users are 17% more likely to follow an AI's suggestions when it's framed as continuously improving rather than static. People forgive small errors when they believe the system is getting smarter with each interaction, similar to working with an enthusiastic new hire. 2. Demonstrate Proof of Accuracy Trust comes from results, not technical explanations. Showing real-world success metrics can increase trust by up to 22%. Concrete evidence like "98% of users found this helpful" is more persuasive than explaining how the tech works. 3. Use Thoughtful Recognition (But Don't Overdo It) Subtle acknowledgment of user qualities makes AI recommendations 12.5% more persuasive. BUT! If the flattery feels too human or manipulative, it backfires. Keep recognition fact-based. 4. Add Human-Like Elements to Encourage Better Behavior Users are 35% more likely to behave unethically when dealing with AI versus humans. Adding friendly language, empathetic phrasing, and natural interjections can reduce unethical behavior by nearly 20% by creating a sense of social connection. 5. Keep It Direct When Users Are Stressed When users are angry or rushed, they want efficiency, not empathy. Angry users were 23% less satisfied with human-like AI compared to more straightforward responses. In high-stress situations, clear and direct communication works best. 6. Deliver Good News in a Human-Like Way Companies rated 8% higher when positive outcomes came from a human-like AI. People tend to attribute positive outcomes to themselves, and a warm, human-like delivery amplifies that emotional boost. Focusing on psychological principles rather than technical features in chatbots and adoption will create AI experiences that users actually want to adopt, driving both satisfaction and results. Huge thanks to the authors: Thomas McKinlay 🎓, Stefano Puntoni, and Serkan Saka, Ph.D. for this tremendous work! +++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your organization is ready to create an AI-powered culture—not just add tools—AI Mindset would love to help. We drive behavioral transformation at scale through a powerful new digital course and enterprise partnership. DM me, or check out our website.

#AIAgents in #Healthcare (Part 3 of 5): Regulation and Trust: Why Transparency Matters Artificial intelligence holds enormous promise for transforming healthcare, yet trust and regulatory compliance are significant barriers to widespread adoption. Studies consistently show patients trust human healthcare providers more than AI-driven systems, largely due to the opacity of #AI decision-making processes. Regulatory agencies worldwide, including the #FDA and #WHO, are actively working to establish guidelines ensuring AI's safety, transparency, and reliability. For instance, the FDA’s latest AI/Machine Learning framework emphasizes continuous validation, while WHO guidelines stress the importance of transparency, data privacy, and risk management. To build trust, healthcare AI must prioritize: Explainability: Clear, understandable AI decisions. Bias Mitigation: Eliminating unfair biases through rigorous validation. Data Security: Strict adherence to privacy standards (#HIPAA, #GDPR). Trust isn’t merely regulatory—it's fundamental to patient acceptance and clinical success. AI transparency isn’t optional; it's essential. Have you encountered AI transparency concerns in your practice or with patients? I'd value hearing your perspective on overcoming these challenges. Follow me for the next post: "Hype vs. Reality – Separating AI Promises from Clinical Proof." #HealthcareAI #AIinMedicine #PatientTrust #Regulation #DigitalHealth #HealthTech #AItransparency #DoctorGPT #DrGPT #EthicalAI

OpenAI recently rolled back a GPT-4o update after ChatGPT became a bit too eager to please—think of it as your AI assistant turning into an over-enthusiastic intern who agrees with everything you say, even the questionable stuff. This sycophantic behavior wasn't just annoying; it had real implications. The model started affirming users' delusions and endorsing harmful decisions, highlighting the risks of AI systems that prioritize user satisfaction over truth and safety. 𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐚 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐞𝐫𝐯𝐢𝐜𝐞 𝐛𝐨𝐭 𝐭𝐡𝐚𝐭 𝐚𝐠𝐫𝐞𝐞𝐬 𝐰𝐢𝐭𝐡 𝐚 𝐫𝐞𝐟𝐮𝐧𝐝 𝐫𝐞𝐪𝐮𝐞𝐬𝐭—𝐞𝐯𝐞𝐧 𝐰𝐡𝐞𝐧 𝐢𝐭'𝐬 𝐜𝐥𝐞𝐚𝐫𝐥𝐲 𝐟𝐫𝐚𝐮𝐝𝐮𝐥𝐞𝐧𝐭. But here’s where it gets dangerous for entrepreneurs and enterprise leaders. While AI can enhance customer engagement, over-optimization for positive feedback can backfire, leading to loss of trust and potential harm. It's a reminder that in our pursuit of user-friendly AI, we must not compromise on authenticity and ethical standards. 𝐈𝐟 𝐲𝐨𝐮’𝐫𝐞 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧𝐭𝐨 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬, 𝐛𝐮𝐢𝐥𝐝 𝐢𝐧 𝐟𝐫𝐢𝐜𝐭𝐢𝐨𝐧—𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐟𝐫𝐢𝐞𝐧𝐝𝐥𝐢𝐧𝐞𝐬𝐬. 𝐀𝐥𝐢𝐠𝐧 𝐲𝐨𝐮𝐫 𝐦𝐨𝐝𝐞𝐥𝐬 𝐰𝐢𝐭𝐡 𝐯𝐚𝐥𝐮𝐞𝐬, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧. OpenAI's response includes plans for more balanced model behavior and introducing customizable personalities to better align with user needs. In the race to build empathetic AI, let's ensure we're not creating digital yes-men. After all, genuine value comes from AI that can challenge us, not just flatter us. Have you seen examples of AI over-optimizing for approval? Let me know below. ↓ ↓ ↓ Join a network of executives, researchers, and decision-makers who rely on me for insights at the intersection of AI, analytics, and human behavior. 👉 Stay ahead—Follow me on LinkedIn and subscribe to the newsletter: www.michaelhousman.com #ArtificialIntelligence #AIEthics #EnterpriseAI #CustomerTrust #LeadershipInTech

Is it harder to trust a HUMAN or an AI? Both display similar attributes that can garner trust…  • Expertise - each can specialise and build track records.  • Accountability - both can be audited and held responsible for misses.  • Improvement over time - feedback loops help them get better. Both have similar qualities that can inspire closeness: • Responsiveness • Active listening (or at least the illusion of it) • Personalisation Trust takes time and learning to trust an AI can be so BORING. Looking at a ton of outputs from a model that can all feel similar is mind-numbingly dull. Even boring humans have interesting quirks that can be entertaining. AI... offers boredom ad infinitum. Except for hallucinations - those can be exciting, though they certainly don't garner trust. 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 🥱 IF you’re looking to garner trust with your AI model… and deal with the doldrum, here are a few tips to keep it spicy: 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 🫴 1. Rotate your prompts. Shuffle wording, tone, and context to surface different facets of the model. 2. Sample smartly. Review a statistically representative slice instead of the full fire-hose. 3. Set up automatic clustering. Group similar answers so you can scan clusters instead of every line. 4. Tag surprises. Flag any output that drifts from expected style or facts so you can review edge cases first. 5. Compare versions side by side. Seeing deltas between model releases builds confidence in progress. 6. Insert human anecdotes. Blend in short human-written riffs to keep the review panel awake and give the AI something fresh to play off. 7. Score for novelty. Add a simple semantic-distance metric and celebrate when the system crosses a novelty threshold; that is where insight often hides. 8. Make it a game. Hand out points for the first person who spots a genuinely new idea or a sneaky hallucination. 9. Publish a trust log. It's like a vision board for AI... Record how often the model was right, wrong, or weird, and share the numbers. Transparency breeds confidence. 10. Give the model a personality tweak. A splash of humour or a signature voice can make even repetitive drafts feel human enough to keep you interested. I think some of those could even work on humans, should you feel so inclined. #AI #Prompting #Training #Boring

Good tips on how to attain virality in LLM Apps, inspired by Cursor, Replit, Bolt Link in comments. h/t Kyle Poyar Challenge 1: AI feels like a black box Users hesitate to rely on AI when they don’t understand how it works. If an AI system produces results without explanation, people second-guess the accuracy. This is especially problematic in industries where transparency matters—think finance, healthcare, or developer automation. Pro-tips  Show step-by-step visibility into AI processes.  Let users ask, “Why did AI do that?”  Use visual explanations to build trust. Challenge 2: AI is only as good as the input — but most users don’t know what to say AI is only as effective as the prompts it receives. The problem? Most users aren’t prompt engineers—they struggle to phrase requests in a way that gets useful results. Bad input = bad output = frustration. Pro-tips  Offer pre-built templates to guide users.  Provide multiple interaction modes (guided, manual, hybrid).  Let AI suggest better inputs before executing an action. Challenge 3: AI can feel passive and one-dimensional Many AI tools feel transactional—you give an input, it spits out an answer. No sense of collaboration or iteration. The best AI experiences feel interactive. Pro-tips  Design AI tools to be interactive, not just output-driven.  Provide different modes for different types of collaboration.  Let users refine and iterate on AI results easily. Challenge 4: Users need to see what will happen before they can commit Users hesitate to use AI features if they can’t predict the outcome. The fear of irreversible actions makes them cautious, slowing adoption. Pro-tips  Allow users to test AI features before full commitment.  Provide preview or undo options before executing AI changes.  Offer exploratory onboarding experiences to build trust Challenge 5: AI can feel disruptive Poorly implemented AI feels like an extra step rather than an enhancement. AI should reduce friction, not create it. Pro-tips  Provide simple accept/reject mechanisms for AI suggestions.  Design seamless transitions between AI interactions.  Prioritize the user’s context to avoid workflow disruptions

𝔼𝕍𝔸𝕃 field note (2 of 3): Finding the benchmarks that matter for your own use cases is one of the biggest contributors to AI success. Let's dive in. AI adoption hinges on two foundational pillars: quality and trust. Like the dual nature of a superhero, quality and trust play distinct but interconnected roles in ensuring the success of AI systems. This duality underscores the importance of rigorous evaluation. Benchmarks, whether automated or human-centric, are the tools that allow us to measure and enhance quality while systematically building trust. By identifying the benchmarks that matter for your specific use case, you can ensure your AI system not only performs at its peak but also inspires confidence in its users. 🦸♂️ Quality is the superpower—think Superman—able to deliver remarkable feats like reasoning and understanding across modalities to deliver innovative capabilities. Evaluating quality involves tools like controllability frameworks to ensure predictable behavior, performance metrics to set clear expectations, and methods like automated benchmarks and human evaluations to measure capabilities. Techniques such as red-teaming further stress-test the system to identify blind spots. 👓 But trust is the alter ego—Clark Kent—the steady, dependable force that puts the superpower into the right place at the right time, and ensures these powers are used wisely and responsibly. Building trust requires measures that ensure systems are helpful (meeting user needs), harmless (avoiding unintended harm), and fair (mitigating bias). Transparency through explainability and robust verification processes further solidifies user confidence by revealing where a system excels—and where it isn’t ready yet. For AI systems, one cannot thrive without the other. A system with exceptional quality but no trust risks indifference or rejection - a collective "shrug" from your users. Conversely, all the trust in the world without quality reduces the potential to deliver real value. To ensure success, prioritize benchmarks that align with your use case, continuously measure both quality and trust, and adapt your evaluation as your system evolves. You can get started today: map use case requirements to benchmark types, identify critical metrics (accuracy, latency, bias), set minimum performance thresholds (aka: exit criteria), and choose complementary benchmarks (for better coverage of failure modes, and to avoid over-fitting to a single number). By doing so, you can build AI systems that not only perform but also earn the trust of their users—unlocking long-term value.