The Role of Transparency in AI Claims

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

Just had an fascinating interaction with ŌURA support that highlights a critical lesson about AI and customer trust... I reached out about a lost ring and received what appeared to be a wonderfully empathetic response: "I'm truly sorry to hear that you've lost your Oura ring. I understand how disappointing this must be for you..." The tone was perfect. Human. Compassionate. Then came the plot twist at the end: "This response was generated by Finn, Oura's Virtual assistant." Here's why this matters for anyone building AI into their customer experience: The response itself wasn't the problem. It was actually quite good. The problem was the setup - it felt like being led to believe you're talking to Sarah from customer support, only to discover it's AI after you've opened up about your situation. It's a bit like someone wearing a convincing mask through an entire conversation, then dramatically pulling it off at the end. Even if the conversation was great, you still feel... weird about it. So when they sent me their customer satisfaction survey, I decided to have some fun. I used ChatGPT to write my responses and signed it off, "This response was generated by ChatGPT, Nate's Virtual assistant." But there's a serious point here: Transparency about AI usage isn't just an ethical choice - it's a strategic one. When customers discover they haven't been talking to the human they thought they were, it erodes trust. And trust, once lost, is incredibly expensive to rebuild. The lesson? If you're using AI in customer service: - Be upfront about it from the start - Let customers know they're talking to AI before the conversation, not after - Keep the empathy (AI can be both transparent AND compassionate) Your customers will appreciate the honesty, and you'll build stronger relationships because of it. PS - I love my ŌURA ring and previously they went above and beyond replacing a defective ring at no cost to me.

🩺 “The scan looks normal,” the AI system says. The doctor hesitates. Will the clinician trust the algorithm? And perhaps most importantly—should they? We are entering an era where artificial intelligence will be woven into the fabric of healthcare decisions, from triaging patients to predicting disease progression. The potential is breathtaking: earlier diagnoses, more efficient care, personalized treatment plans. But so are the risks: opaque decision-making, inequitable outcomes, and the erosion of the sacred trust between patient and provider. The challenge is no longer just about building better AI. It’s about building better ways to decide if—and how—we should use it. That’s where the FAIR-AI framework comes in. Developed through literature reviews, stakeholder interviews, and expert workshops, it offers healthcare systems a practical, repeatable, and transparent process to: 👍 Assess risk before implementation, distinguishing low, moderate, and high-stakes tools. 👍 Engage diverse voices, including patients, to evaluate equity, ethics, and usefulness. 👍 Monitor continuously, ensuring tools stay aligned with their intended use and don’t drift into harm. 👍 Foster transparency, with plain-language “AI labels” that demystify how tools work. FAIR-AI treats governance not as a barrier to innovation, but as the foundation for trust—recognizing that in medicine, the measure of success isn’t how quickly we adopt technology, but how wisely we do it. Because at the end of the day, healthcare isn’t about technology. It’s about people. And people deserve both the best we can build—and the safeguards to use it well. #ResponsibleAI #HealthcareInnovation #DigitalHealth #PatientSafety #TrustInAI #HealthEquity #EthicsInAI #FAIRAI #AIGovernance #HealthTech

The California AG issues a useful legal advisory notice on complying with existing and new laws in the state when developing and using AI systems. Here are my thoughts. 👇 📢 𝐅𝐚𝐯𝐨𝐫𝐢𝐭𝐞 𝐐𝐮𝐨𝐭𝐞 ---- “Consumers must have visibility into when and how AI systems are used to impact their lives and whether and how their information is being used to develop and train systems. Developers and entities that use AI, including businesses, nonprofits, and government, must ensure that AI systems are tested and validated, and that they are audited as appropriate to ensure that their use is safe, ethical, and lawful, and reduces, rather than replicates or exaggerates, human error and biases.” There are a lot of great details in this, but here are my takeaways regarding what developers of AI systems in California should do: ⬜ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲: Clearly disclose when AI is involved in decisions affecting consumers and explain how data is used, especially for training models. ⬜ 𝐓𝐞𝐬𝐭 & 𝐀𝐮𝐝𝐢𝐭 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Regularly validate AI for fairness, accuracy, and compliance with civil rights, consumer protection, and privacy laws. ⬜ 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐁𝐢𝐚𝐬 𝐑𝐢𝐬𝐤𝐬: Implement thorough bias testing to ensure AI does not perpetuate discrimination in areas like hiring, lending, and housing. ⬜ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Establish policies and oversight frameworks to mitigate risks and document compliance with California’s regulatory requirements. ⬜ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐇𝐢𝐠𝐡-𝐑𝐢𝐬𝐤 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: Pay special attention to AI used in employment, healthcare, credit scoring, education, and advertising to minimize legal exposure and harm. 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐞𝐞𝐭𝐢𝐧𝐠 𝐥𝐞𝐠𝐚𝐥 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬—it’s about building trust in AI systems. California’s proactive stance on AI regulation underscores the need for robust assurance practices to align AI systems with ethical and legal standards... at least this is my take as an AI assurance practitioner :) #ai #aiaudit #compliance Khoa Lam, Borhane Blili-Hamelin, PhD, Jeffery Recker, Bryan Ilg, Navrina Singh, Patrick Sullivan, Dr. Cari Miller

Data privacy and ethics must be a part of data strategies to set up for AI. Alignment and transparency are the most effective solutions. Both must be part of product design from day 1. Myths: Customers won’t share data if we’re transparent about how we gather it, and aligning with customer intent means less revenue. Instacart customers search for milk and see an ad for milk. Ads are more effective when they are closer to a customer’s intent to buy. Instacart charges more, so the app isn’t flooded with ads. SAP added a data gathering opt-in clause to its contracts. Over 25,000 customers opted in. The anonymized data trained models that improved the platform’s features. Customers benefit, and SAP attracts new customers with AI-supported features. I’ve seen the benefits first-hand working on data and AI products. I use a recruiting app project as an example in my courses. We gathered data about the resumes recruiters selected for phone interviews and those they rejected. Rerunning the matching after 5 select/reject examples made immediate improvements to the candidate ranking results. They asked for more transparency into the terms used for matching, and we showed them everything. We introduced the ability to reject terms or add their own. The 2nd pass matches improved dramatically. We got training data to make the models better out of the box, and they were able to find high-quality candidates faster. Alignment and transparency are core tenets of data strategy and are the foundations of an ethical AI strategy. #DataStrategy #AIStrategy #DataScience #Ethics #DataEngineering

FDA Calls for Greater Transparency and Bias Mitigation in AI Medical Devices: ⚖️The recently issued US FDA draft guidance emphasizes transparency in AI device approvals, recommending detailed disclosures on data sources, demographics, blind spots, and biases ⚖️ Device makers should outline validation data, methods, and postmarket performance monitoring plans to ensure ongoing accuracy and reliability ⚖️ The guidance highlights the need for data diversity to minimize bias and ensure generalizability across populations and clinical settings ⚖️ Recommendations include using “model cards” to provide clear, concise information about AI models and their updates ⚖️ The FDA proposes manufacturers submit plans for updating and maintaining AI models without requiring new submissions, using pre-determined change control plans (PCCP) ⚖️ Concerns about retrospective-only testing and site-specific biases in existing AI devices highlight the need for broader validation methods ⚖️ The guidance is currently advisory but aims to set a higher standard for AI device approvals while addressing public trust in AI technologies 👇Link to articles and draft guidance in comments #digitalhealth #FDA #AI

We have to internalize the probabilistic nature of AI. There’s always a confidence threshold somewhere under the hood for every generated answer and it's important to know that AI doesn’t always have reasonable answers. In fact, occasional "off-the-rails" moments are part of the process. If you're an AI PM Builder (as per my 3 AI PM types framework from last week) - my advice: 1. Design for Uncertainty: ✨Human-in-the-loop systems: Incorporate human oversight and intervention where necessary, especially for critical decisions or sensitive tasks. ✨Error handling: Implement robust error handling mechanisms and fallback strategies to gracefully manage AI failures (and keep users happy). ✨User feedback: Provide users with clear feedback on the confidence level of AI outputs and allow them to provide feedback on errors or unexpected results. 2. Embrace an experimental culture & Iteration / Learning: ✨Continuous monitoring: Track the AI system's performance over time, identify areas for improvement, and retrain models as needed. ✨A/B testing: Experiment with different AI models and approaches to optimize accuracy and reliability. ✨Feedback loops: Encourage feedback from users and stakeholders to continuously refine the AI product and address its limitations. 3. Set Realistic Expectations: ✨Educate users: Clearly communicate the potential for AI errors and the inherent uncertainty involved about accuracy and reliability i.e. you may experience hallucinations.. ✨Transparency: Be upfront about the limitations of the system and even better, the confidence levels associated with its outputs.

Not every patient trusts (or distrusts) healthcare AI equally. A recent global survey published in @JAMA Network Open found interesting variances among groups, according to gender, tech literacy and health status. The survey of nearly 14,000 hospital patients in 43 countries sought to assess patient attitudes toward AI. Among the findings: ·      Most patients were positive about the general use of AI in healthcare (57.6%) and favored its increased use (62.9%) ·      Female patients were slightly less positive about the general use of AI (55.6%) than males (59.1%) ·      The worse their health, the more negative patients felt about AI ·      Not surprisingly, the more patients knew about AI and tech in general, the more positive they were about AI in healthcare.   There was also a clear lean toward explainable AI, with 70.2% of patients indicating a preference for AI with transparent decision-making processes. That’s higher than a previous U.S. study which found that only 42% of patients felt uncomfortable with highly accurate AI diagnoses that lacked explainability. Whatever the percentage, it’s clear that patients want to know what's going on "under the hood" with algorithms that support clinical decision-making. AI implementation must be transparent with clear explanations of decisions for providers and patients. That is the only way we can build the necessary foundation of trust in the technology that will allow it to achieve its full potential. You can read the survey results here: https://lnkd.in/dJmkRtSw #HealthcareAI #HealthcarePolicy #GenAI #AIHealth

Our paper on transparency reports for large language models has been accepted to AI Ethics and Society! We’ve also released transparency reports for 14 models. If you’ll be in San Jose on October 21, come see our talk on this work. These transparency reports can help with: 🗂️ data provenance ⚖️ auditing & accountability 🌱 measuring environmental impact 🛑 evaluations of risk and harm 🌍 understanding how models are used   Mandatory transparency reporting is among the most common AI policy proposals, but there are few guidelines available describing how companies should actually do it. In February, we released our paper, “Foundation Model Transparency Reports,” where we proposed a framework for transparency reporting based on existing transparency reporting practices in pharmaceuticals, finance, and social media. We drew on the 100 transparency indicators from the Foundation Model Transparency Index to make each line item in the report concrete. At the time, no company had released a transparency report for their top AI model, so in providing an example we had to build a chimera transparency report with best practices drawn from 10 different companies.   In May, we published v1.1 of the Foundation Model Transparency Index, which includes transparency reports for 14 models, including OpenAI’s GPT-4, Anthropic’s Claude 3, Google’s Gemini 1.0 Ultra, and Meta’s Llama 2. The transparency reports are available as spreadsheets on our GitHub and in an interactive format on our website. We worked with companies to encourage them to disclose additional information about their most powerful AI models and were fairly successful – companies shared more than 200 new pieces of information, including potentially sensitive information about data, compute, and deployments. 🔗 Links to these resources in comment below!   Thanks to my coauthors Rishi Bommasani, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang at Stanford Institute for Human-Centered Artificial Intelligence (HAI), MIT Media Lab, and Princeton Center for Information Technology Policy