Why You Need Transparent AI Demos

How do you know what you know? Now, ask the same question about AI. We assume AI "knows" things because it generates convincing responses. But what if the real issue isn’t just what AI knows, but what we think it knows? A recent study on Large Language Models (LLMs) exposes two major gaps in human-AI interaction: 1. The Calibration Gap – Humans often overestimate how accurate AI is, especially when responses are well-written or detailed. Even when AI is uncertain, people misread fluency as correctness. 2. The Discrimination Gap – AI is surprisingly good at distinguishing between correct and incorrect answers—better than humans in many cases. But here’s the problem: we don’t recognize when AI is unsure, and AI doesn’t always tell us. One of the most fascinating findings? More detailed AI explanations make people more confident in its answers, even when those answers are wrong. The illusion of knowledge is just as dangerous as actual misinformation. So what does this mean for AI adoption in business, research, and decision-making? ➡️ LLMs don’t just need to be accurate—they need to communicate uncertainty effectively. ➡️Users, even experts, need better mental models for AI’s capabilities and limitations. ➡️More isn’t always better—longer explanations can mislead users into a false sense of confidence. ➡️We need to build trust calibration mechanisms so AI isn't just convincing, but transparently reliable. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐚 𝐡𝐮𝐦𝐚𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐚𝐬 𝐦𝐮𝐜𝐡 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. We need to design AI systems that don't just provide answers, but also show their level of confidence -- whether that’s through probabilities, disclaimers, or uncertainty indicators. Imagine an AI-powered assistant in finance, law, or medicine. Would you trust its output blindly? Or should AI flag when and why it might be wrong? 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬—𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐮𝐬 𝐚𝐬𝐤 𝐛𝐞𝐭𝐭𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬. What do you think: should AI always communicate uncertainty? And how do we train users to recognize when AI might be confidently wrong? #AI #LLM #ArtificialIntelligence

There's a great new paper exploring why the Metaverse failed. Here's how I think we can apply the learnings when considering AI. Lesson 1: Stop selling the technology. Start solving real problems. The problem wasn't the technology. It was the pitch. Meta rushed to market with a solution looking for a problem. They led with features, not benefits. They forgot the cardinal rule of persuasion: people don't buy what you do, they buy why it matters to them. Sound familiar? Look at how most brands are approaching AI right now. Same playbook. Same mistakes. Lesson 2: Build trust through demonstration, not declaration. The research is clear: privacy concerns and trust issues kill adoption faster than anything else. When consumers fear their data is being harvested or manipulated, no amount of flashy demos will convert them. Your AI strategy needs to lead with transparency. Not as a footnote in your terms of service, but as your opening statement. Show people exactly what data you're using and how you're protecting it. Make opt-in controls obvious. Give users the wheel. Lesson 3: Leverage social proof or die in obscurity. Meta told us the Metaverse would revolutionize work. Then they gave us cartoon meetings that nobody asked for. Instead of promising AI will transform everything, show one specific thing it does brilliantly. Let people experience a small win before you ask for a big commitment. Think progressive disclosure, not information overload. Lesson 4: Segment your audience or waste your budget. Not everyone is ready for AI. Just like not everyone was ready for the Metaverse. Map your market by trust level and tech readiness. Your early adopters need innovation stories. Your skeptics need security guarantees. Your mainstream market needs proof it works. One message won't move all three. The Metaverse failed because it forgot that technology adoption is about psychology, not programming. Your AI won't succeed because it's powerful. It will succeed because you made people feel powerful using it. #AIStrategy #MarketingStrategy #TechAdoption #BrandStrategy #DigitalTransformation #AIMarketing #ConsumerPsychology #TechTrends

Ever been fooled by a chatbot thinking it was a real person? It happened to me! As AI continues to evolve, particularly in the realm of chatbots, transparency is more important than ever. In many interactions, it’s not always clear if you’re talking to a human or an AI—an issue that can affect trust and accountability. AI-powered tools can enhance convenience and efficiency, but they should never blur the lines of communication. People deserve to know when they’re interacting with AI, especially when it comes to critical areas like healthcare, customer service, and financial decisions. Transparency isn’t just ethical—it fosters trust, allows users to make informed decisions, and helps prevent misinformation or misunderstandings. As we integrate AI more deeply into our daily lives, let’s ensure clarity is a top priority. Transparency should be built into every interaction, making it clear when AI is at the wheel. That’s how we build responsible, reliable, and user-friendly AI systems. GDS Group #AI #Transparency #EthicsInAI #TrustInTechnology

The Imperative of #Transparency in #AI: Insights from Dr. Jesse Ehrenfeld and the Boeing 737 Max Tragedy Jesse Ehrenfeld MD MPH President of the #AmericanMedicalAssociation, recently highlighted the critical need for transparency in AI deployments at the RAISE Health Symposium 2024. He referenced the tragic Boeing 737 Max crashes, where a lack of transparency in AI systems led to devastating consequences, underscoring the importance of clear communication and human oversight in AI applications. Key Lessons: 1. **Transparency is Non-Negotiable**: Dr. Ehrenfeld stressed that users must be fully informed about AI functionalities and limitations, using the Boeing 737 Max as a cautionary tale where undisclosed AI led to fatal outcomes. 2. **Expectation of Awareness**: Dr. Ehrenfeld provided a relatable example from healthcare, stating he would expect to know if a ventilator he was using in surgery was being adjusted by AI. This level of awareness is essential to ensure safety and effectiveness in high-stakes environments. 3. **Human Oversight is Essential**: The incidents highlight the need for human intervention and oversight, ensuring that AI complements but does not replace critical human decision-making. 4. **Building Trust in Technology**: Prioritizing transparency, safety, and ethics in AI is crucial for building trust and preventing avoidable disasters. As AI continues to permeate various sectors, it is imperative to learn from past mistakes and ensure transparency, thereby fostering a future where technology enhances human capabilities responsibly. **Join the Conversation**: Let's discuss how we can further integrate transparency in AI deployments across all sectors. Share your thoughts and experiences below. #AIethics #TransparencyInAI #HealthcareInnovation #DigitalHealth #DrGPT

One of the things that's important about implementing AI is to ensure people know when they are interacting with AI, whether that be in a live interaction or via AI-produced content. Brands that fail to be transparent can risk doing damage to customer relationships and reputation. By offering AI transparency and options, people can decide if they wish to engage with the AI or prefer an alternative. But if you offer AI interactions or content without transparency, it can leave people feeling deceived and manipulated. Arena Group, which owns Sports Illustrated, fired its CEO. The announcement only mentions "operational efficiency and revenue," but it comes weeks after an AI scandal hit the sports magazine. A tech publication discovered articles on SI that appeared to be from real humans were, in fact, created by AI. Even the headshots and biographies of the "authors" were AI-created. At the time, Arena Group blamed a third-party ad and content provider and severed its relationship with the firm. #GenAI can provide some remarkable benefits, but leaders must recognize the variety of risks that AI can bring. Being transparent about when customers are interacting with AI is one of the ways to mitigate the risks. Make it clear and conspicuous when you provide a #CustomerExperience facilitated by AI so that customers have the information and control they desire. https://lnkd.in/gnC2fE57

🤖 AI is evolving, but so are the questions we must ask! While AI, and particularly large language models (LLMs) like ChatGPT, are progressing rapidly, it’s important to remember that we’re still in the nascent stages of this technology. 🌱 Every month brings new advancements, but it also introduces skepticism—especially when it comes to trust and transparency. Let’s consider some critical questions: 1) Why are certain recommendations made? Whether you’re using AI to shortlist candidates in recruiting or identify top deals in your CRM, understanding why the AI makes those suggestions is crucial. 2) How do we balance excitement with caution? AI’s strength lies in tasks like summarization, but when it comes to business recommendations, users need clear insights into the reasoning behind its choices. Trust comes from transparency. As AI continues to progress, it’s important to keep an eye on its why and how to ensure we're getting not just powerful tools but reliable, explainable solutions. Its great to see Aravind Srinivas at Perplexity is bringing transparency to the results. We need more explainability and transparency in the AI. 💬 How are you incorporating AI while ensuring transparency in your use cases? #AI #Transparency #SalesAI #TrustInTech #FutureOfWork #llm