Building Trust in AI Applications

🚨 BREAKING: An extremely important lawsuit in the intersection of PRIVACY and AI was filed against Otter over its AI meeting assistant's lack of CONSENT from meeting participants. If you use meeting assistants, read this: Otter, the AI company being sued, offers an AI-powered service that, like many in this business niche, can transcribe and record the content of private conversations between its users and meeting participants (who are often NOT users and do not know that they are being recorded). Various privacy laws in the U.S. and beyond require that, in such cases, consent from meeting participants is obtained. The lawsuit specifically mentions: - The Electronic Communications Privacy Act; - The Computer Fraud and Abuse Act; - The California Invasion of Privacy Act; - California’s Comprehensive Computer Data and Fraud Access Act; - The California common law torts of intrusion upon seclusion and conversion; - The California Unfair Competition Law; As more and more people use AI agents, AI meeting assistants, and all sorts of AI-powered tools to "improve productivity," privacy aspects are often forgotten (in yet another manifestation of AI exceptionalism). In this case, according to the lawsuit, the company has explicitly stated that it trains its AI models on recordings and transcriptions made using its meeting assistant. The main allegation is that Otter obtains consent only from its account holders but not from other meeting participants. It asks users to make sure other participants consent, shifting the privacy responsibility. As many of you know, this practice is common, and various AI companies shift the privacy responsibility to users, who often ignore (or don't know) what national and state laws actually require. So if you use meeting assistants, you should know that it's UNETHICAL and in many places also ILLEGAL to record or transcribe meeting participants without obtaining their consent. Additionally, it's important to have in mind that AI companies might use this data (which often contains personal information) to train AI, and there could be leaks and other privacy risks involved. - 👉 Link to the lawsuit below. 👉 Never miss my curations and analyses on AI's legal and ethical challenges: join my newsletter's 74,000+ subscribers. 👉 To learn more about the intersection of privacy and AI (and many other topics), join the 24th cohort of my AI Governance Training in October.

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗮 𝗱𝗲𝗺𝗼. 𝗜𝘁 𝗳𝗮𝗶𝗹𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝗿𝗲𝗮𝗹 𝘂𝘀𝗲𝗿𝘀 𝘀𝗵𝗼𝘄 𝘂𝗽. Embed → retrieve → generate looks clean in a notebook. Real requirements break it: → Questions whose answer is spread across many documents → Industry terms that embeddings get wrong → Bad chunks the pipeline never catches → Answers that live in how things connect, not in any single chunk → PDFs full of tables and images a text-only index cannot read These 5 architectures are how serious teams stay ahead in the agentic AI era: 𝟬𝟭 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚 → Dense vectors find meaning. BM25 finds exact words. → Reciprocal Rank Fusion combines both ranked lists. → A safe baseline for almost every team. 𝟬𝟮 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 → Pull entities and their relationships into a knowledge graph. → Retrieve subgraphs and community summaries, not chunks. → Best when the answer lives in how things connect. 𝟬𝟯 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 → A planner agent picks the right tool: vector, web, or SQL. → A reasoner agent keeps trying until the answer is solid. → Retrieval becomes a plan, not a single step. 𝟬𝟰 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝘃𝗲 𝗥𝗔𝗚 (𝗖𝗥𝗔𝗚) → Grade every retrieval before you trust it. → Correct → answer. Unclear → rewrite the query. Wrong → search the web. → This is what production RAG actually looks like. 𝟬𝟱 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚 → One embedding model (CLIP, ColPali) for text, images, and tables. → One vector index. One multimodal LLM. → No more separate pipelines for PDFs with charts. I built a runnable example for each of the five patterns. GitHub link in the first comment. The best teams in 2026 do not pick one. They combine them — hybrid retrieval inside an agentic loop, with a corrective grader, over a multimodal index. Naive RAG is a starting point, not a finish line. That is why most enterprise GenAI projects stall at the demo. Which of these five becomes the default RAG stack in the next 18 months — and which stays a specialized tool?

Trust at scale has always been the hardest thing to build in business. Word of mouth was the original mechanism. One person tells another, credibility transfers, trust builds slowly. It worked, but it was a limited mechanism you couldn't control. What's changed today is the infrastructure. Reach, repeated visibility to a large audience, is now one of the most powerful trust-building tools available to any founder or business. I am not saying being seen is the same as being trusted, but trust requires repeated exposure before it forms. The people and businesses that maintain high engagement at scale on their social media are the ones that showed up repeatedly, with a clear point of view, long before the numbers got impressive. Trust is a perception built over time through repeated signals: what you say, what you stand for, what you consistently show up for. Reach accelerates that process. Every post is another data point for your audience to evaluate whether your judgment is worth following. Enough of those data points, delivered consistently, and reach becomes evidence that you are someone worth trusting. The people and businesses who understand this aren't just building audiences. They're building credibility that makes everything else, fundraising, hiring, selling, structurally easier. #rajshamani #figuringout

Two identical CVs. Both written by AI. Both sent to 1,000 people. The only difference: one was named James, one was named Emily. James’s CV got a 97% approval rating. Emily’s got 76% - and reviewers were TWICE as likely to question her competence. Twenty-two percent more likely to question whether she could even be trusted. The feedback on Emily’s CV: “She can’t even write a CV herself - not sure she has the skills to carry out the job.” The feedback on James’s CV: “He just needed a bit of help putting it together.” Same words. Same AI. Different gender. Different verdict. 🚨🚨🚨🚨 How are we STILL HERE?!?!? The study, by former Meta strategist Zehra Chatoo, was reported in Fortune on 10 May. And the most uncomfortable finding wasn’t from older reviewers. It was from Gen Z men. They were 3.5 times more likely to call Emily’s CV “weak.” The generation that is growing up with AI. The generation telling us AI is the great equaliser. The data says otherwise. Chatoo summarised it in a sentence I have not been able to stop thinking about: “When men use AI, we question their effort. When women use AI, we question their integrity.” This is not one study. Harvard Business School has the AI adoption gender gap at 25%. Brookings has found that 86% of the roles with high AI exposure and low capacity to adapt to displacement are held by women. The pattern is consistent and it is widening. The conclusion most people are drawing from this data is “women should be more confident with AI.” I think that misses the point. The bias isn’t in the technology. It is in the people reading the output. Women are not being irrational when they hesitate to use AI openly - they are reading the room accurately. The reputational cost of being seen to use AI is genuinely higher for them. The data confirms what they already sense. The answer is not to ask women to ignore that. The answer is to fix the people doing the judging. To name what is actually happening when an “Emily” CV gets called weak and a “James” CV gets the benefit of the doubt for the same words. To call out the Gen Z men perpetuating a bias they like to claim their generation has moved past. And for women in leadership reading this - use AI anyway. Lead anyway. Document your AI workflows openly. Train your teams in them. Make your usage visible in the rooms where decisions get made. The cost of stepping back from AI in this moment is far higher than the cost of stepping in. We have the data to prove it now. If this resonated, I write about the AI gender gap, ethics, and practical strategy for women in leadership every week in my newsletter. The link is here: https://lnkd.in/emWjxC9t

Anyone can ship a chart. Trusted analysts aim for influence. Trust isn’t a vibe. It’s observable. Here are 20 signs of a data analyst you can trust 👇 1. They document their methodology transparently ↳ Every stakeholder can follow their analytical journey 2. They admit when they don’t know something ↳ “I need to investigate this further” builds more trust than guessing 3. They validate data quality before sharing insights ↳ Trust starts with clean, verified information 4. They communicate uncertainty honestly ↳ Express confidence levels and margin of error upfront 5. They follow up on previous recommendations ↳ Track whether their insights actually drove results 6. They explain their assumptions clearly ↳ Make their thinking process completely visible 7. They anticipate data limitations ↳ Proactively address what the analysis cannot prove 8. They use consistent definitions across reports ↳ Ensure metrics mean the same thing every time 9. They provide multiple scenarios when forecasting ↳ Present best case, worst case, and most likely outcomes 10. They cite their data sources religiously ↳ Full transparency on where every number originates 11. They avoid cherry-picking favorable results ↳ Present complete findings, even when inconvenient 12. They explain complex concepts in simple terms ↳ Technical accuracy doesn’t require technical jargon 13. They provide actionable next steps ↳ Never leave stakeholders wondering “what do we do now?” 14. They seek feedback and incorporate it genuinely ↳ Show they value others’ perspectives and domain expertise 15. They standardize their reporting formats ↳ Consistency reduces cognitive load for decision-makers 16. They proactively flag potential data issues ↳ Alert stakeholders to collection problems or anomalies 17. They maintain the confidentiality of sensitive data ↳ Respect data privacy and security protocols religiously 18. They provide training on how to interpret their outputs ↳ Empower others to use insights correctly 19. They collaborate with domain experts ↳ Combine analytical skills with business knowledge 20. They respond promptly to questions about their work ↳ Accessibility builds confidence in their expertise Trust isn’t about being perfect. It’s about being transparent, reliable, and genuinely committed to accuracy. Which trust-building practice do you prioritize most as a data analyst? ♻️ Repost to help your network build trusted analytics practices 🔔 Follow for daily insights on building credibility through data

Breast cancer can now be detected 5 years before it develops thanks to AI: Recent advances in artificial intelligence have shown remarkable potential for early breast cancer detection. AI systems are being developed that can analyze mammograms and identify potential cancer risks up to five years before clinical manifestation. These systems operate through sophisticated deep learning models trained on extensive mammogram databases, enabling them to detect subtle imaging patterns that might escape human notice. Different research teams have taken varied approaches to this challenge. For instance, scientists at MIT and Massachusetts General Hospital created a comprehensive model that examines entire mammogram images for cancer-predictive patterns. Meanwhile, Duke University researchers developed AsymMirai, which takes a more focused approach by analyzing breast tissue asymmetry between left and right breasts, achieving similar accuracy through a more streamlined and transparent method. AI is also proving valuable as a complementary tool for radiologists. The Mia system, currently being tested by Britain's National Health Service, serves as an additional layer of scrutiny, helping identify minute cancerous formations that human reviewers might miss. This capability for earlier detection can lead to more timely interventions and less aggressive treatment options.

🚨 Huge AI policy news for the Australian public service! The Government has just released its Australian Public Service (APS) AI Plan 2025, a major blueprint for how the APS will use artificial intelligence to deliver better, faster services for Australians. This is a practical plan that moves beyond ambition to focus on execution. It’s about ensuring the APS has the tools and the judgement to use AI responsibly, with AI leaders embedded in agencies to drive adoption. The plan rests on three pillars: 1️⃣ Trust: transparency, ethics and governance 2️⃣ People: capability building and engagement 3️⃣ Tools: access, infrastructure and support Key initiatives: 💡 GovAI – secure, onshore generative AI platforms 📜 A strengthened Responsible AI Policy, with mandatory AI strategies, impact assessments and accountable officers and a register for use cases 🧩 Chief AI Officers to drive safe, coordinated adoption 🤝 Supplier obligations – requirements that suppliers declare and take responsibility for AI use 🧠 Mandatory AI literacy and leadership training across the entire public service ☁️ A new whole-of-government cloud policy to unlock AI’s potential securely This is a major statement of intent from the Government: agencies are expected to lean in, not sit back on AI. My thoughts: 📄 Responsible AI policy overhaul coming: The current policy was fairly light. Expect an update by year’s end to embed clearer accountability, risk management and governance expectations. 🔨 Use-case-level governance: I’ve long argued that AI governance works best at the use-case level, not the system level. The Government agrees. The approach appoints accountable officers for use cases, which is the kind of granularity needed for real accountability. 👀 Central oversight: An AI Review Committee will scrutinise higher-risk use cases. This creates a feedback loop that allows lessons, failures and fixes to be shared across government rather than buried in individual agencies. It’s a smart step toward building consistency and collective trust. 💪 Massive capability uplift: Every public servant will receive foundational AI literacy training and rightly so. An AI tool is only as good as the hands it’s in, and training must cover responsible use AND effective use. 📡 Trust through communication. The plan directly acknowledges Australia’s trust gap on AI and puts communication and engagement at the core. 📶 A new benchmark for industry. A whole-of-government AI governance framework like this could very well become the de facto standard for everyone doing business with government and beyond. Requirements will inevitably flow through supply chains. Big picture: the aim is to boost service delivery, policy outcomes and productivity while fostering public trust. That’s the right balance: adopt AI boldly, but govern it deeply. Make no mistake, this is a big step for responsible AI in the APS. #AI #AIGovernance #ResponsibleAI #ArtificialIntelligence #TrustworthyAI

74% of business executives trust AI advice more than their colleagues, friends, or even family. Yes, you read that right. AI has officially become the most trusted voice in the room, according to recent research by SAP. That’s not just a tech trend — that’s a human trust shift. And we should be paying attention. What can we learn from this? 🔹 AI is no longer a sidekick. It’s a decision-maker, an advisor, and in some cases… the new gut instinct. 🔹 But trust in AI is only good if the AI is worth trusting. Blind trust in black-box systems is as dangerous as blind trust in bad leaders. So here’s what we should do next: ✅ Question the AI you trust Would you take strategic advice from someone you’ve never questioned? Then don’t do it with AI. Check its data, test its reasoning, and simulate failure. Trust must be earned — even by algorithms. ✅ Make AI explain itself Trust grows with transparency. Build “trust dashboards” that show confidence scores, data sources, and risk levels. No more “just because it said so.” ✅ Use AI to enhance leadership, not replace it Smart executives will use AI as a mirror — for self-awareness, productivity, communication. Imagine an AI coach that preps your meetings, flags bias in decisions, or tracks leadership tone. That’s where we’re headed. ✅ Rebuild human trust, too This stat isn’t just about AI. It’s a signal that many execs don’t feel heard, supported, or challenged by those around them. Let’s fix that. 💬 And finally — trust in AI should look a lot like trust in people: Consistency, Transparency, Context, Integrity, and Feedback. If your AI doesn’t act like a good teammate, it doesn’t deserve to be trusted like one. What do you think? 👇 Are we trusting AI too much… or not enough? #SAPAmbassador #AI #Leadership #Trust #DigitalTransformation #AgenticAI #FutureOfWork #ArtificialIntelligence #EnterpriseAI #AIethics #DecisionMaking