Risks and Benefits of Trust in Tech

I'm being asked A LOT of questions about trust in AI. The danger isn't just too much or too little trust. It's misplaced trust—and that's where real harm happens. I've been developing a simple framework to help make sense of this: The AI Trust Matrix 🔹 Alignment Zone: High Trust + High Trustworthiness, e.g. Nav apps like Waze — trusted, and get better with real-time feedback. 🔹 Danger Zone: High Trust + Low Trustworthiness, e.g. AI-generated influencers — followed… but they don't even exist. 🔹 Friction Zone: Low Trust + High Trustworthiness, e.g. AI in cancer diagnostics — high-potential, not trusted yet. 🔹 Caution Zone: Low Trust + Low Trustworthiness, e.g. Predictive policing — biased tools that unfairly target communities. This matrix helps surface an uncomfortable truth: The most dangerous systems aren't always the least trustworthy — they're the ones we trust too much. #TrustInAI #ResponsibleAI #TrustMatrix #DesignForTrust #TechWithPurpose #AIethics

We’re now putting microchips in pill bottles. But are we also putting trust at risk? Imagine a pill bottle that texts your doctor when you forget a dose. That’s the promise of updated “smart adherence” technology – bottles that track medication use and share data with providers in real time. The idea is compelling: improve outcomes, reduce hospital readmissions, and support patients who struggle with complex regimens. And based on studies, it works. Researchers saw between 91.8 and 100% adherence rates. But here’s my concern: ➔ What happens when we confuse 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝘀𝘂𝗿𝘃𝗲𝗶𝗹𝗹𝗮𝗻𝗰𝗲? ➔ What happens to 𝘁𝗿𝘂𝘀𝘁 in the doctor-patient relationship when data becomes the primary measure of care? ➔ And, how do we protect 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 when tech becomes part of treatment? As someone deeply involved in training the next generation of healthcare leaders, I see both sides. We need innovation. But we also need 𝗲𝘁𝗵𝗶𝗰𝘀, 𝗲𝗺𝗽𝗮𝘁𝗵𝘆, 𝗮𝗻𝗱 𝗲𝗾𝘂𝗶𝘁𝘆 in how we apply it. Technology can help save lives – but only if we keep humanity at the center. How would you feel knowing your medication habits were being tracked?

A client once told me he keeps his iPad out of the room during important conversations. At first, I thought he was being overly cautious. Now? I think he might’ve been onto something the rest of us missed. Apple (the ones who ‘value privacy’ 🤐) just paid millions to settle claims that Siri recorded conversations without consent. Google is facing lawsuits over devices picking up audio when they shouldn’t. And Facebook? They’ve had plenty of issues with how they’ve handled voice data. But this isn’t just about tech companies or their tools. It’s about trust. Trust doesn’t just disappear overnight. It erodes bit by bit—until one day, your customers, employees, or partners stop believing in what you’re building. A recent survey found that over 60% of people believe their devices are listening to them—even when they aren’t activated. Whether perception or reality, that belief is already shaking confidence in the tools we rely on every day. This isn’t just a technology issue. It’s a leadership challenge. I’m a big advocate for AI—its experimentation, its strategic potential, and its operational applications in organizations. I’ve seen organizations use AI to streamline supply chains, enhance customer experiences, and uncover new market opportunities—all while driving meaningful impact. AI offers incredible opportunities to rethink how we work, innovate, and deliver value. But none of that matters without trust. Leaders must balance the excitement of AI’s possibilities with asking the tough questions about ethics, data, and responsibility. The two need to go hand in hand. Innovation and trust. Progress and accountability. Because innovation without trust isn’t progress—it’s a gamble. So yes, push for AI and other innovative technologies in your organization. Experiment, think boldly, and embrace their potential. But don’t skip the hard conversations. Ask yourself: • Do we know what data we’re using, how it’s being used, and why? • Do we have the right people in the room—people who will speak up when decisions might cross the line? • Have we set clear ethical boundaries so we can recognize when lines are being tested? We’ve seen what happens when trust breaks. It’s not just reputations that suffer—teams lose morale, customers look elsewhere, and opportunities for progress disappear. The real challenge isn’t just adopting technology—it’s doing it in a way that strengthens trust. Leaders who get this right will build a competitive advantage. Those who don’t risk losing everything. The pace of innovation is accelerating. What are you doing to make sure your team leads with trust—and doesn’t leave values behind in the rush to move fast? #StrategyToAction Partnering Leadership #partneringleadership Strategic Leadership Ventures #strategy #collaboration #ai #genai #mangement

In AI WE Trust? "Trust is not about the specific technologies developed or deployed, it’s about the decisions that leaders make."- World Economic Forum In a McKinsey & Company survey 55 percent of executives experienced an incident in which active AI (for example, in use in an application) produced outputs that were biased, incorrect, or did not reflect the organization’s values. Only a little over half of these AI errors were publicized. These AI mishaps, too, frequently resulted in consequences, most often employees’ loss of confidence in using AI (38 percent of the time) and financial losses (37 percent). The recent World Economic Forum report on "Earning Digital Trust in the Fourth Industrial Revolution" highlights the importance of building digital trust between consumers, businesses, and technology. As our lives become increasingly digital, establishing trust is crucial. 📍 Digital technologies like AI and automation are rapidly advancing. While they provide many benefits, they also come with new risks around data privacy, security, and ethics. 📍 Consumers want more transparency from companies on how their data is used and how AI systems make decisions. Lack of trust will make consumers hesitate to adopt new technologies. 📍 Multistakeholder collaboration is needed to shape the development of emerging tech responsibly. Companies, governments, and civil society must work together. The report makes it clear - earning digital trust is vital for realizing the full potential of technology to improve our lives. Companies that prioritize transparency, security, and ethics will gain people's confidence and loyalty. Building digital trust requires a concerted effort across industries. But each of us can also do our part as individuals. 🚨👾We can educate ourselves on technology, advocate for ethical practices, and make informed choices about what products and services we use. Small actions today will help shape a future powered by technology that we can trust. What are your thoughts on building digital trust? How can we work together to earn trust in the digital age? I'd love to hear your perspectives. #digitaltrust #AI #privacy #cybersecurity #ethicalAI #dataprotection

Recent insights from the Capgemini Research Institute highlight a compelling trend: 73% of consumers trust #GenerativeAI, with significant reliance noted in financial, medical, and personal advisory sectors. Yet, while the promise of AI in enhancing business operations is undeniable (a reported average return of $3.5 for every $1 invested), consumer awareness of risks like fake news and phishing remains surprisingly low. Trust in AI cannot be taken lightly. As my colleague Niraj Parihar highlights in his recent blog for #WEF24, as we continue accelerate adoption of AI technologies, it will be crucial that ethical frameworks, regulation, and transparency are established to ensure responsible AI use.

Building Trustworthy Generative AI In discussions of generative AI, one type of risk is commonly noted: hallucination. Generative AI models are designed to create data that looks like real data, but that doesn’t mean outputs are always true. Sometimes, the model takes a wrong turn. While generative AI hallucinations can be a hurdle significantly impacting user trust, they can be mitigated by considering it as a model reliability issue. But then, it is not the only (nor even the most significant) risk. Taking a broader view, we can use the trust domains in #Deloitte’s Trustworthy AITM framework to explore the types of risks with which organizations may contend when deploying generative AI. Trustworthy AI (of any variety) is: fair and impartial; robust and reliable; transparent and explainable; safe and secure; accountable and responsible; and respectful of privacy. While not every trust domain is relevant to every model and deployment, the framework helps illuminate generative AI risks that deserve greater concern and treatment. Bottomline - Effective, enterprise-wide model governance is not something that can be dismissed until negative consequences emerge, nor is it sufficient to take a “wait and see” approach as government rulemaking on generative AI evolves. Instead, given the potential consequences, businesses face a need to account for generative AI risks today and those yet to emerge as the technology matures. Source - https://lnkd.in/g4588dpr #Ai #generativeai #technology