Future of Trust and Transparency in Analytics

As AI becomes integral to our daily lives, many still ask: can we trust its output? That trust gap can slow progress, preventing us from seeing AI as a tool. Transparency is the first step. When an AI system suggests an action, showing the key factors behind that suggestion helps users understand the “why” rather than the “what”. By revealing that a recommendation that comes from a spike in usage data or an emerging seasonal trend, you give users an intuitive way to gauge how the model makes its call. That clarity ultimately bolsters confidence and yields better outcomes. Keeping a human in the loop is equally important. Algorithms are great at sifting through massive datasets and highlighting patterns that would take a human weeks to spot, but only humans can apply nuance, ethical judgment, and real-world experience. Allowing users to review and adjust AI recommendations ensures that edge cases don’t fall through the cracks. Over time, confidence also grows through iterative feedback. Every time a user tweaks a suggested output, those human decisions retrain the model. As the AI learns from real-world edits, it aligns more closely with the user’s expectations and goals, gradually bolstering trust through repeated collaboration. Finally, well-defined guardrails help AI models stay focused on the user’s core priorities. A personal finance app might require extra user confirmation if an AI suggests transferring funds above a certain threshold, for example. Guardrails are about ensuring AI-driven insights remain tethered to real objectives and values. By combining transparent insights, human oversight, continuous feedback, and well-defined guardrails, we can transform AI from a black box into a trusted collaborator. As we move through 2025, the teams that master this balance won’t just see higher adoption: they’ll unlock new realms of efficiency and creativity. How are you building trust in your AI systems? I’d love to hear your experiences. #ArtificialIntelligence #RetailAI

GenAI’s black box problem is becoming a real business problem. Large language models are racing ahead of our ability to explain them. That gap (the “representational gap” for the cool kids) is no longer just academic, and is now a #compliance and risk management issue. Why it matters: • Reliability: If you can’t trace how a model reached its conclusion, you can’t validate accuracy. • Resilience: Without interpretability, you can’t fix failures or confirm fixes. • Regulation: From the EU AI Act to sector regulators in finance and health care, transparency is quickly becoming non-negotiable. Signals from the frontier: • Banks are stress-testing GenAI the same way they test credit models, using surrogate testing, statistical analysis, and guardrails. • Researchers at firms like #Anthropic are mapping millions of features inside LLMs, creating “control knobs” to adjust behavior and probes that flag risky outputs before they surface. As AI shifts from answering prompts to running workflows and making autonomous decisions, traceability will move from optional to mandatory. The takeaway: Interpretability is no longer a nice-to-have. It is a license to operate. Companies that lean in will not only satisfy regulators but also build the trust of customers, partners, and employees. Tip of the hat to Alison Hu Sanmitra Bhattacharya, PhD, Gina Schaefer, Rich O'Connell and Beena Ammanath's whole team for this great read.

Knowledge Graphs as a source of trust for LLM-powered enterprise question answering That has been our position from the beginning when we started our research of understanding how knowledge graphs increase the accuracy of LLM-powered question answering systems over 2 years ago!  The intersection of knowledge graphs and large language models (LLMs) isn’t theoretical anymore. It's been a game-changer for enterprise question answering and now everyone is talking about it and many are doing it. 🚀 This new paper is a summary of our lessons learned of implementing this technology in data.world and working with customers, and outline the opportunities for future research contributions and where the industry needs to go (guess where the data.world AI Lab is focusing). Sneak peek and link in the comments Lessons Learned ✅ Knowledge engineering is essential but underutilized: Across organizations, it’s often sporadic and inconsistent, leading to assumptions and misalignment. It’s time to systematize this critical work. ✅ Explainability builds trust: Showing users exactly how an answer is derived, including auto-corrections, increases transparency and confidence. ✅ Governance matters: Aligning answers with an organization’s business glossary ensures consistency and clarity. ✅ Avoid “boiling the ocean”: don’t tackle too many questions at once A pay-as-you-go approach ensures meaningful progress without overwhelm. ✅ Testing matters: Non-deterministic systems like LLMs require new frameworks to test ambiguity and validate responses effectively. Where the Industry Needs to Go 🌟 Simplified knowledge engineering: Tools and methodologies must make this foundational work easier for everyone. 🌟 User-centric explainability: Different users have different needs so we need to focus on “explainable to whom?”. 🌟 Testing non-deterministic systems: The deterministic models of yesterday won’t cut it. We need innovative frameworks to ensure quality in LLMs powered software applications. 🌟 Small semantics vs. Larger semantics: The concept of semantics is being increasingly referenced in industry in the context of “semantic layers” for BI and Analytics. Let’s close the gap between the small semantics (fact/dimension modeling) and large semantics (ontologies, taxonomies) 🌟 Multi-agent systems: break down the problem into smaller, more manageable components. Should an agent deal with the core task of answering questions and managing ambiguity, or should these be split into separate agents? This research reflects our commitment to co-innovate with customers to solve real-world challenges in enterprise AI. 💬 What do you think? How are knowledge graphs shaping your AI strategies?

Transparency has become essential across AI legislation, risk management frameworks, standardization methods, and voluntary commitments alike. How to ensure that AI models adhere to ethical principles like fairness, accountability, and responsibility when much of their reasoning is hidden in a “black box”? This is where Explainable AI (XAI) comes in. The field of XAI is relatively new but crucial as it confirms that AI explainability enhances end-users’ trust (especially in highly-regulated sectors such as healthcare and finance). Important note: transparency is not the same as explainability or interpretability. The paper explores top studies on XAI and highlights visualization (of the data and process that goes behind it) as one of the most effective methods when it comes to AI transparency. Additionally, the paper highlights 5 levels of explanation for XAI (each suited for a person’s level of understanding): 1.      Zero-order (basic level): immediate responses of an AI system to specific inputs 2.      First-order (deeper level): insights into reasoning behind AI system’s decisions 3.      Second-order (social context): how interactions with other agents and humans influence AI system’s behaviour 4.      Nth order (cultural context): how cultural context influences the interpretation of situations and the AI agent's responses 5.      Meta (reflective level): insights into the explanation generation process itself

A Design Road Map for an Ethical Generative AI How to Monetize Ethics and Operationalize Values What if the next competitive edge in GenAI isn’t speed, but quality? As GenAI floods the enterprise, companies face a stark choice: automate everything and risk trust, or design with people and values at the center. Ethics will be the single most important strategic asset. Don’t take my word for it: A McKinsey study found that companies scoring highest on trust and transparency outperform their industry peers by up to 30% in long-term value creation.[1] Gartner predicts that by 2026, 30% of major organizations will require vendors to demonstrate ethical AI use as part of procurement.[2] Deloitte reports that consumers are 2.5x more likely to remain loyal to brands that act in alignment with their stated values.[3] It’s clear: Trust scales. Ethics compounds. Values convert. So how do we build AI systems around those principles? Here’s a practical, open-source roadmap to do just that: 1. Design for Ambiguity The best AI doesn’t pretend every question has a single answer. It invites exploration, not conclusions. That’s not weakness—it’s wisdom. 2. Show Your Values Expose the logic behind your systems. Let users see how outcomes are generated. Transparency isn’t just ethical—it’s the foundation of brand trust. 3. Stop Guessing. Start Reflecting. Don’t design AI to guess what users want. Design it to help them figure out what matters to them. Prediction is easy. Clarity is rare. 4. Lead With Ethics While others optimize for speed, you can win on something deeper: clarity, trust, and long-term loyalty. Ethical systems don’t break under scrutiny—they get stronger. 5. Turn Users Into Co-Creators Every value-aligned interaction is training data. Slower? Maybe. But smarter, more adaptive, and more human. That’s the kind of intelligence we should be scaling. The myth is that ethics slows you down. The truth? It makes you unstoppable. Imagine how what it would be like to have a staunch and loyal employee and customer base, an eco-system of shared values? That's the greatest moat of all time ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Stephen Klein is the Founder & CEO of Curiouser.AI, the only values-based Generative AI platform, strategic coach, and advisory designed to augment individual and organizational imagination and intelligence. He also teaches AI ethics and entrepreneurship at UC Berkeley. To learn more or sign up: www.curiouser.ai or connect on Hubble https://lnkd.in/gphSPv_e Footnotes [1] McKinsey & Company. “The Business Case for AI Ethics.” 2023. [2] Gartner. “Top Strategic Technology Trends for 2024.” 2023. [3] Deloitte Digital. “Trust as a Differentiator.” 2022.

Ever feel like decisions are being made about you but without you? That’s what happens when data stays locked behind closed doors. And this is something we don’t talk about enough in leadership circles: Open data. - Not just dashboards and quarterly reports. - I mean accessible, public, actionable data. Because here's the thing... 🛑 Closed data leads to closed decisions. ✅ Open data leads to open impact. Let me break it down: Think of open data as a public library of truth. When governments, institutions, and even companies make their data accessible (not just available)… real change happens. ◽ Communities get to see how budgets are spent. ◽ Health workers spot trends early and save lives. ◽ Entrepreneurs build apps that solve real problems. ◽ Journalists expose inequalities that would otherwise stay hidden. We’ve seen it work. Remember how COVID dashboards helped people make safer choices in real time? That was open data in action. But here’s the catch: Open data without transparency is just noise. → Who collected it? → How was it cleaned? → What’s not being shown? It’s not just about making data public, it’s about making it understandable, reliable, and inclusive. So what can leaders do? ◦ Make your data open by default, not exception. ◦ Communicate the story behind the numbers. ◦ Invite communities to co-create solutions using your data. Transparency isn’t a PR move, it’s a trust-building strategy. And in today’s world of misinformation and AI uncertainty? Trust is currency. I’ll leave you with this: Open data isn’t just a tool for better decisions. It’s a mirror that reflects who we are, and who we want to be. I’d love to hear your thoughts! How are you using data to drive transparency in your work or community? P.S. Don’t forget to follow me for more insights on ethical data use! And if you’re passionate about using data for good, let’s connect - I’m always up for collaborating on ways to make an even bigger impact.

🚨 We have a hidden "fourth party" problem in B2B software. Your vendor uses AI/GenAI models. Their AI provider now has your data. Did anyone tell you? When your CRM, analytics platform, or support tool sends your data to OpenAI, Anthropic, Microsoft copilot or Google's AI - that's a relationship you never agreed to. Your contracts don't cover it. Your compliance team doesn't know about it. We need an "AI Nutrition Label" for B2B services (Sample Included): ✓ Which AI models process your data (e.g. open weights model hosted or ?) ✓ Where that data goes (on-prem vs. third-party APIs) ✓ What data protection exists ✓ How to opt out Just like food labels transformed consumer choice, AI transparency will transform B2B trust. I'm pushing for every vendor (including us) to publish a clear AI disclosure page. Not buried in terms of service. Not in legalese. Just straight answers about where customer data flows. The question isn't whether your vendors use AI - they do. The question is whether they'll tell you about it and whether you are given the opportunity to opt out. Who's ready to lead on transparency? 🙋 #DataPrivacy #AI #B2BTech #Transparency #SupplyChain #RadicalNotionAI

Trust: The Cornerstone of AI’s Next Chapter The rise of AI has taken us beyond mere tools—it’s now reshaping how we create, consume, and interact with content. From generating ideas to executing complex tasks, AI is rapidly transforming content into a low-cost commodity. But as this transformation unfolds, trust is emerging as the defining factor that will determine the leaders of the AI age. The companies that thrive over the next decade won’t just deliver cutting-edge AI—they’ll deliver confidence, accountability, and transparency to their users. The AI Differentiator: Trust Will Decide the Winners As the digital space becomes saturated with AI-driven outputs, trust will become the deciding factor for success. In a world of near-infinite content, authenticity and transparency will determine which companies thrive. Authenticity as a Competitive Edge The value of knowing a piece of content’s origin will rise sharply. Verifiable systems that establish a clear trail of how content was created and modified will set trusted platforms apart. The future isn’t about flashy logos or checkmarks—it’s about real, provable transparency. Ethical AI Wins Loyalty Companies that show they care about ethical AI practices and data integrity will gain the loyalty of increasingly skeptical users. Customers are looking for more than functionality—they want to trust the tools they rely on. Trust is the Moat AI Must Build AI systems that fail to address bias, hallucinations, and security risks will lose credibility fast. Organizations that invest in AI governance, robust guardrails, and user oversight will have a clear competitive advantage in a trust-first market. How to Build Trust in an AI-Driven World Winning the AI trust race requires more than just good algorithms. Companies must weave trust into their operational DNA. Transparent Origins: Use technology that tracks and verifies the lifecycle of AI-generated content, providing users with confidence in its accuracy and provenance. Ethical Guardrails: Integrate safeguards like human oversight for sensitive decisions, ensuring responsible and reliable use of AI. Openness is Key: Clear communication about how your systems work and what data they rely on builds user confidence. Adapt to Trust Shifts: As user expectations evolve, companies must continually refine their systems to meet the growing demand for authenticity and transparency. The Future Belongs to Trusted Innovators In the era of synthetic content and automated creativity, trust is more than a virtue—it’s the bedrock of success. Businesses that make trust a non-negotiable aspect of their AI offerings will set themselves apart in a crowded and rapidly evolving marketplace. The next decade will belong to those who understand that building trust isn’t just good ethics—it’s the key to building a sustainable competitive advantage in the AI-powered future. Author - Robert Franklin, Founder AI Quick Bytes

Transparency in AI isn’t just about trust—it’s about survival in regulated industries. The debate around AI "black boxes" is heating up, especially in finance and insurance. Here's why it's crucial to understand how AI thinks: 1. Regulatory compliance: Internal and external regulators will demand transparency. 2. Bias detection: Proving fairness and lack of bias becomes essential. 3. Decision tracing: A perfect lineage of thoughts, observations, and actions (both AI and human) is necessary. 4. Appeal processes: Understanding AI reasoning allows for effective appeals of outcomes. 5. Future-proofing: Building explainability now prepares you for upcoming regulatory requirements. Remember, AI thinks differently from humans. It can be brilliantly right or spectacularly wrong in unexpected ways. That's why chain of thought, observability, and explainability are non-negotiable in regulated industries. If you're not prioritizing this, start now. It's the key to successfully deploying AI agents in production while meeting compliance and regulatory demands. Keep building, but build with transparency in mind.

Organizations today don’t struggle with a lack of data. The real challenge is turning that data into 𝘁𝗿𝘂𝘀𝘁𝗲𝗱, 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. What’s becoming clear is that traditional dashboards are no longer enough. Leaders need:  • 𝗚𝗼𝘃𝗲𝗿𝗻𝗲𝗱 𝗱𝗮𝘁𝗮 they can rely on  • 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜 that makes insights transparent  • 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 to answer business questions instantly  • 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿 integration to build data-driven agents and applications that are context-aware through the Jedify MCP server The shift is toward platforms that combine these elements—simplifying analysis while ensuring confidence in outcomes. Instead of static reports, businesses gain a continuous flow of intelligence to guide decisions. One example of this direction is Jedify’s approach, which emphasizes explainability and trust while enabling powerful, context-aware analytics - https://bit.ly/415Z8PS As data becomes the foundation of competitive advantage, the question isn’t just how much data you have, but 𝗵𝗼𝘄 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗹𝘆 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗮𝗰𝘁 𝗼𝗻 𝗶𝘁.