The Role of Trust in Innovation Acceptance

🤖 Exciting advances in AI are transforming clinical decision support, but how do companies in the space make sure their solutions are trustworthy, effective, and truly supportive of clinical needs? Rhett Alden shared a compelling presentation last week at #himss24 outlining 3 key principles that align with my advice to clients on responsible AI deployment: Trust, Content Quality & Provenance, and Validation. 🚀🔍 1️⃣ 𝐓𝐫𝐮𝐬𝐭: 𝐓𝐡𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 🛡️ Trust is not just a principle; it's the bedrock of effective AI in healthcare. To build this trust, companies should: 👉🏻Leverage domain-specific LLMs to understand and interpret medical nuances accurately. 👉🏻Build a foundation rooted in Responsible AI and Quality Management Systems (QMS) Principles to ensure solutions are ethically developed and deployed. 👉🏻Implement Clinical Safety Frameworks safeguards against unintended consequences, protecting both patients and practitioners. 2️⃣ 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 & 𝐏𝐫𝐨𝐯𝐞𝐧𝐚𝐧𝐜𝐞: 𝐓𝐡𝐞 𝐁𝐚𝐜𝐤𝐛𝐨𝐧𝐞 📚 Data provenance refers to the lineage or origin of a piece of data and where it has moved from to where it is presently. This is crucial for transparency and trustworthiness in clinical decision-making, so: 👉🏻The information feeding into AI systems must be copyright secure, ensuring all data is ethically sourced and legally compliant. 👉🏻Utilize Retrieval Augmented Generation for up-to-date, accurate, and contextually relevant information. 3️⃣ 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: 𝐓𝐡𝐞 𝐎𝐧𝐠𝐨𝐢𝐧𝐠 𝐂𝐨𝐦𝐦𝐢𝐭𝐦𝐞𝐧𝐭 🔬 For AI solutions to remain relevant and reliable, continuous validation is key: 👉🏻Automated and Clinician SME (Subject Matter Expert) evaluation ensures the AI's recommendations are clinically sound and practical. 👉🏻Real-world monitoring and CAPA (Corrective and Preventive Action) mechanisms ensure solutions adapt to new data, evolving standards, and emerging clinical practices. I'd love to hear your thoughts on these principles or any others you believe are crucial for the successful integration of AI into healthcare. 🌐💡 #AIinHealthcare #ClinicalDecisionSupport #DigitalHealth #HealthcareInnovation #TrustInAI #QualityInHealthcare Sam Pinson Reema Taneja

Lately, I’ve been asked an interesting question about AI adoption. Specifically, how should an organization implement AI tools without instilling fear, but instead align with the organization’s mission and vision? Whenever I discuss AI adoption, two thoughts always come to my mind immediately: 1️⃣ Transparency in AI Usage: As executives, we must be transparent about our future use of AI within our business. Transparency aligns with our commitment to ethical practices and strengthens employees’ trust. It’s important to demystify the use of AI, ensuring everyone understands its applications and benefits. Together, we can build a foundation of trust that forms the bedrock of our AI journey. 2️⃣ Employee Inclusion in Decision-Making: Our greatest asset is our people. In the AI adoption era, including employees in decision-making is critical to building trust. They bring invaluable insights, diverse perspectives, and a deep understanding of our operations. By involving our teams, we tap into a wellspring of collective intelligence that propels us ahead. Let’s foster a culture where everyone feels empowered to contribute to the decisions that shape our AI strategy. By combining transparency in AI usage with inclusive decision-making, we are not just adopting technology; we are building a culture of innovation, trust, and shared success. The convergence of transparency and employee inclusion isn’t just a strategy; it’s an organizational mindset.

The ACM, Association for Computing Machinery's Technology Policy Council has just published an easy-to-understand 4-page Technology Brief on Trusted AI. The tech brief series is specifically designed for consumption by policy makers and people in business. It is available as a free download from the ACM Digital Library. From the Tech Brief: POLICY IMPLICATIONS ➡️ Public trust of AI is essential for trust in the institutions that deploy these technologies. ➡️ Extensive research on technical mechanisms for promoting and measuring trustworthiness of AI has done little to increase public trust in it. ➡️ Policy makers must understand, prioritize, and reflect the importance of earning the public’s trust in emerging AI regulations and standards. TRUSTED AI: BY THE NUMBERS 306 - Number of academic papers in the arXiv online repository on the general topic of responsible, trustworthy, and ethical AI since introduction of the EU AI Act in 2021. 413 - Number of arXiv papers over same time period on technical means to promote and measure AI trustworthiness. 190 - Number of bills introduced by states in the U.S. to regulate AI in the first three quarters of 2023.1 74 - Percentage of Americans very/somewhat concerned that AI could make important life decisions for them. 53 - Percentage of Britons with no faith in any organization using algorithms to make judgments about them. 61 - Percentage of people globally who report not trusting AI. 71 - Percentage of people globally who expect AI to be regulated. 33 - Percentage of people globally who lack confidence in government and business to develop, use, and regulate AI. Toward Trusted AI "The pursuit of supporting mechanisms and objective trustworthiness metrics, while understandable from accountability and compliance perspectives, may contribute little to the goal of engendering broad trust of AI. For any given AI system, there will be competing views on what would make it trustworthy. Fortunately, earning the trust of various stakeholders does not require that technologists discover and implement a perfect approach. A good faith effort to engage with affected parties toward a more comprehensive understanding of the implications of the design and deployment choices being made, and toward more optimal ways of negotiating these choices, could be very beneficial." https://lnkd.in/ejN9kAmB

What generates trust in an AI system? Knowing that it will do what you intend, predictably and safely. But what might cause you to distrust it? There are five foundational reasons that AI systems can violate your trust: (1) We don’t understand how an AI system works. How it functions is either non-transparent or is not explainable. (2) The design and/or use is inherently risky. There is a non-trivial likelihood the system will produce harm or negatively impact other systems or users. (3) The system produces erratic behavior. You understand how the system works but it sometimes produces weird or unexpected outputs. (4) The system develops emergent capabilities. It’s acquiring the ability to perform functions that it wasn’t explicitly trained to do. (5) The system’s training data itself is untrustworthy. The data contains bias or is insufficiently representative of the target demographic. Like risk itself, any one of these conditions is not automatically disqualifying. You can apply different controls and get to a level of "residual trust" that is acceptable and appropriate given the use case. But thinking about trust in these buckets is helpful in determining what controls might be required early in your governance process.

We keep talking about model accuracy. But the real currency in AI systems is trust. Not just “do I trust the model output?” But: • Do I trust the data pipeline that fed it? • Do I trust the agent’s behavior across edge cases? • Do I trust the humans who labeled the training data? • Do I trust the update cycle not to break downstream dependencies? • Do I trust the org to intervene when things go wrong? In the enterprise, trust isn’t a feeling. It’s a systems property. It lives in audit logs, versioning protocols, human-in-the-loop workflows, escalation playbooks, and update governance. But here’s the challenge: Most AI systems today don’t earn trust. They borrow it. They inherit it from the badge of a brand, the gloss of a UI, the silence of users who don’t know how to question a prediction. Until trust fails. • When the AI outputs toxic content. • When an autonomous agent nukes an inbox or ignores a critical SLA. • When a board discovers that explainability was just a PowerPoint slide. Then you realize: Trust wasn’t designed into the system. It was implied. Assumed. Deferred. Good AI engineering isn’t just about “shipping the model.” It’s about engineering trust boundaries that don’t collapse under pressure. And that means: → Failover, not just fine-tuning. → Safeguards, not just sandboxing. → Explainability that holds up in court, not just demos. → Escalation paths designed like critical infrastructure, not Jira tickets. We don’t need to fear AI. We need to design for trust like we’re designing for failure. Because we are. Where are you seeing trust gaps in your AI stack today? Let’s move the conversation beyond prompts and toward architecture.

Healthcare—a sector where innovation rapidly translates to real-world impact—is undergoing one of the most profound AI-driven transformations. The breakthroughs we help deliver are reshaping patient care, experiences, and outcomes, and underscore the deep purpose and sense of responsibility we bring to our work. I recently read through a report from the World Economic Forum and Boston Consulting Group (BCG) – “Earning Trust for AI in Health: A Collaborative Path Forward” – which outlines a cross-industry framework to build trust with AI and underlines a stark reality for us: without transparency and responsibility, we cannot capitalize on the promise of AI to improve healthcare. There are exciting breakthroughs in the industry happening every day. With the potential to improve and streamline patient care, implementing AI tools requires that the data and information that these tools provide is credible and reliable. At Pfizer we put responsible AI into action with our Responsible AI program, including a proprietary internal toolkit that allows colleagues to easily and consistently implement best practices for responsible AI in their work. Responsibility also played a crucial role in our recently launched Generative AI tool, #HealthAnswersbyPfizer, which utilizes trusted, independent third-party sources so that consumers can access relevant health and wellness information that is up to date. As we apply AI in the real world, these conversations around trust and ethics are paramount. It is our responsibility to not only lead the advancements that will improve the industry, but to also lead the movement in responsible, ethical AI that advances and protects us, not hinders or harms us. This will encourage the adoption of tools that can lead to healthier lives, lower costs, and a brighter future. To read more about the WEF/BCG report: https://bit.ly/406b0AS

Out now! The Deloitte AI Institute has launched the Q2 pulse report of the State of #GenerativeAI in the Enterprise series today. The report titled Getting real with Generative AI captures a new perspective of this transformative time in #GenAI from nearly 2,000 Director to C-suite respondents and the results are worth the read.    Our attitudes towards #GenerativeAI are evolving but fear behind what it means to “trust” the technology remains the same. A short clip of findings on the topic of trust include: Lack of trust remains a major barrier to large-scale Generative AI adoption and deployment. Two key aspects of trust we observed are: (1) trust in the quality and reliability of Generative AI’s output and (2) trust from workers that the technology will make their jobs easier without replacing them. Organizations that reported “high” or “very high” levels of expertise recognize the importance of building trust in Generative AI across numerous dimensions (e.g., input / output quality, transparency, worker empathy) and are implementing processes to improve it to a much greater extent than are other organizations.   We all know 2024 is a critical year for generative AI— and our latest report helps you to stay on top of the latest trends, challenges, and impacts. Look forward to hearing your thoughts!

🚫 Viewers didn't hear the term "AI" at yesterday’s Apple event – Tim Cook insists on putting the product first, and I agree! Five tips for keeping the product, and the user first when developing solutions that leverage #AI: 👩🏫 Deeply empathize: Understand user needs and then determine whether AI can help solve your users' problems, and not create new ones. 🤖 Design with intent: Ensure that AI seamlessly integrates into the user journey, enhancing interactions and providing value. 🔎 Transparency builds trust: be transparent about the AI’s role. Users should know when they’re interacting with AI and when they’re not. 💡 Keep it simple: While AI can be complex behind the scenes, aim for simplicity in the user interface 📈 Continuous improvement: Regularly gather user feedback and iterate on AI capabilities to enhance their utility and relevance. The goal should be to create AI-enabled products that are intuitive, create efficiencies, and feel personal. By keeping the product and user experience at the forefront you’ll create exceptional products. 💙 What are your tips for building product-first, AI enabled solutions? Would love thoughts in the comments! Miss the apple event? Check it a recap in the link in the comments 👇 #aiforgood #ProductDesign #Innovation

As artificial intelligence becomes more prevalent, there is a growing need to assess how much we can trust AI systems. The article argues that every AI model should have something akin to a FICO credit score that rates its trustworthiness. Some key reasons why AI trust scores are important: - AI systems make mistakes and have biases, so understanding the limitations of a system is critical before deploying it. A trust score would help identify risky AI models. - Different users have different trust requirements - a model safe for low-risk applications may not be appropriate for high-risk ones. Trust scores would enable better matching of AI to use cases. - Trust decays over time as data changes. Regular evaluation and updated trust ratings will help identify when a model is no longer fit for purpose. - Trust scores allow easier comparison of AI systems to select the most appropriate one. - Transparency over how scores are calculated allows users to make informed choices about AI adoption. In summary, AI trust scores empower users to make smarter decisions on how and where to use AI models safely and effectively. Just as FICO scores help assess credit risk, AI trust scores are needed to assess risks of unfairness, inaccuracy and harm. #AI #TRUST #DATA #FICO

AI Trust & Safety reminds me a lot of cybersecurity a decade ago. Previously, when I was a cybersecurity engineer, we could see the major players adopting CS best practices early. As the awareness of bad actors became more prevalent, the adoption of a CS posture snowballed and almost every industry connected to the web now has some cybersecurity measures in place (fingers crossed). As AI applications proliferate, the demand for Trust & Safety roles increases. Just how significant is this growth? We see the rise in global conferences like the Credo AI's Summit, led by CEO & founder Navrina Singh, ACM, Association for Computing Machinery's FAccT, and TrustCon, put on by the Trust & Safety Professional Association. Trust & Safety teams ensure tech platforms are ethical and fair, crucial for standards and user protection. Embedded in Legal, Product, or Operations, they address harmful risks and align with responsible AI practices. As AI's influence grows, these teams are key. Companies like Facebook, Google, and Amazon have boosted their Trust & Safety efforts and with AI integration deepening, prioritizing Trust & Safety is vital for ethical tech navigation. We also see a rising trend in Trust & Safety teams at well-funded AI startups, mainly because their end customers want to know that the potential harms are being addressed in a meaningful way. Here are some ways to collaborate with Trust & Safety:  — Engage Early: Include Trust & Safety from the start in product development for built-in ethics. — Regular Training: Educate all teams on Trust & Safety's role and how to contribute.  — Open Communication: Promote a culture where Trust & Safety feedback is encouraged and acted upon. #TrustAndSafety #AI #TechEthics #AIStartups