Excited to share a piece I recently wrote for Nava on something I’ve seen make a real difference when testing AI-powered tools: working with participant advisory councils (PACs). While AI systems are often judged by metrics like accuracy and speed, what ultimately determines success is whether people trust them, understand them, and can use them in their day-to-day work. That’s where PACs come in. In this article, I break down 5 reasons why PACs are so valuable—from uncovering workflow friction to surfacing risks that traditional testing can miss. Grateful to our Comms team for sharing this, and to everyone who’s contributed their time and insights to this work. Would love to hear how others are thinking about testing and trust when it comes to AI 👇 https://lnkd.in/gZnEvTcU
5 Reasons PACs Boost Trust in AI Systems
This title was summarized by AI from the post below.
More Relevant Posts
-
Array is hosting a short masterclass on May 26th focused on practical review workflows and defensibility. AI is rapidly changing document review, which is exciting until someone suggests replacing legal judgment with a prompt and a prayer. The more interesting conversation is where AI actually improves workflows, where it creates risk, and how teams are balancing speed with defensibility in the real world. Looking forward to hearing the Array team cut through some of the noise around AI and talk about what is actually working in practice including optimizing the tried and true workflows. Accelerating Review Without Sacrificing Defensibility: What Works in Practice May 26th | 2:00 PM – 2:45 PM ET Register here: https://lnkd.in/gUNdAfgA #DocumentReview #LegalTech #ArtificialIntelligence #GenerativeAI #TrustArray #eDiscovery #PracticeInnovation
-
AI Illiteracy Is Becoming a Leadership Liability Artificial intelligence illiteracy is rapidly emerging as a material leadership risk. MIT has released ten high-quality AI courses at no cost, yet many executives are likely to overlook them, an oversight with significant strategic implications. While technical coding skills are not required, approving AI-related budgets without a foundational understanding leaves decision-makers exposed to vendor bias, inflated claims, and poorly aligned implementations. Leaders who are successfully navigating this space are asking more disciplined and critical questions: which processes still require human judgment, where AI systems fail at scale, what data underpins model outputs, and whether there is a demonstrable return on investment. This level of inquiry is quickly becoming a baseline expectation rather than a differentiator. The MIT course portfolio offers a practical and accessible pathway to build this capability, covering everything from foundational concepts to applied AI strategy and system design. The ten courses include: 1. Foundation Models and Generative AI https://lnkd.in/gNwgbtaE 2. AI 101 https://lnkd.in/gyJz7whc 3. Artificial Intelligence https://lnkd.in/ggneRvcZ 4. Introduction to Machine Learning https://lnkd.in/gT5HcRs5 5. Introduction to Deep Learning https://lnkd.in/gq8PwrnP 6. Understanding the World Through Data https://lnkd.in/gVdj_EhG 7. Machine Learning with Python https://lnkd.in/gUdHfAhx 8. How to AI Almost Anything https://lnkd.in/ghfgKgsx 9. Introduction to Algorithms https://lnkd.in/g2P-3ptd 10. AI in K-12 Education https://lnkd.in/gs9Fesqy By 2026, the designation “non-technical” will no longer shield executives from accountability in AI-driven decisions.
-
Anthropic co-founder Chris Olah just stood at the Vatican and told the world his own company can't be trusted to govern itself. Not in those exact words. But close. At the presentation of Pope Leo XIV's first encyclical on AI today, Olah said: "Every frontier AI lab... operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing." He said this made outside scrutiny essential. This is the most candid thing I've heard from an AI company executive in years. It's not a regulator saying AI labs can't self-police. It's one of the people building the technology saying it. The Pope's encyclical - 83 pages, titled "Magnifica Humanitas" - called for governments to slow down AI development, protect workers, and limit private control over AI data. Those are the expected calls. Olah's presence at the Vatican, and his words, are the actual news. Competitive pressure, capital pressure, geopolitical pressure - all of it pushes AI labs in one direction. He knows it. He said it publicly. For enterprise buyers who've been wrestling with AI governance for two years, this matters. The trust problem isn't theoretical anymore. One of the people closest to the technology just confirmed it's real. https://lnkd.in/dDhAN7aY
-
If you're building BI at scale, the Analytics & BI track at hashtag #DataAISummit 2026 has sessions worth adding to your schedule. Leaders from Atlassian, HP, Virgin Atlantic and more will cover the work that is moving their organizations forward: — Scaling AI-powered BI from pilot to production — Modernizing semantic layers with AI — Building trusted generative BI Join us this June to learn how to turn data into impact: https://lnkd.in/gd_2Kw76
-
Observe most organizations today and you’ll see the same thing: Teams using AI, but still working the same way they always have. Result? A bit of efficiency, maybe some marginal gains... but far from the value that's possible (or even measurable). Our latest World Economic Forum report helps breaks down what it actually takes to get there (and the behavioral changes required to make it stick). https://lnkd.in/gqqSth-K
Why are companies struggling to get value from #AI? Outcome = Ambition x Adoption This equation (credit to my brilliant colleague Oliver Grange) explains why so many companies are struggling with turning AI investments into value. We have the ambition; we lack adoption. Or put more simply: It’s not the technology; it’s the people. In our latest paper with the World Economic Forum, we share actions organizations can take to help their people on a positive journey, unlocking human potential and succeeding in the market. https://lnkd.in/gqqSth-K Zara Ingilizian Till Dudler Oliver Wright Amanda Frick Javier Rodriguez Regina Maruca
-
From the same institution that produced the individuals building the models everyone is discussing, here is a comprehensive list of resources for a complete MIT-level AI education: FOUNDATIONS 1. Foundations of Machine Learning — lnkd.in/gytjT5HC 2. Understanding Deep Learning — lnkd.in/dgcB68Qt 3. Machine Learning Systems — lnkd.in/dkiGZisg ADVANCED TECHNIQUES 4. Algorithms for ML — algorithmsbook.com 5. Deep Learning — lnkd.in/g2efT6DK REINFORCEMENT LEARNING 6. RL Basics (Sutton & Barto) — lnkd.in/guxqxcZZ 7. Distributional RL — lnkd.in/d4eNP-pe 8. Multi-Agent Systems — marl-book.com 9. Long Game AI — lnkd.in/g-WtzvwX ETHICS & PROBABILITY 10. Fairness in ML — fairmlbook.org 11. Probabilistic ML Part 1 — lnkd.in/g-isbdjj 12. Probabilistic ML Part 2 — lnkd.in/gJE9fy4w This collection covers essential topics in AI and machine learning, providing a solid foundation for learners at all levels.
-
Free AI Courses: 1. Claude 101 https://lnkd.in/gCPUQsRg 2. AI Fluency: Frameworks & Foundations https://lnkd.in/gS6ceZ_M 3. Introduction to Agent Skills https://lnkd.in/g_wWNiEb 4. Building with the Claude API https://lnkd.in/gDr5K_B4 5. Claude Code in Action https://lnkd.in/g9wWZbK9 6. Introduction to Model Context Protocol https://lnkd.in/gAj5HqMY 7. MCP: Advanced Topics https://lnkd.in/g3eDwBFY 8. AI Fluency for Students https://lnkd.in/gKKujHGG 9. AI Fluency for Educators https://lnkd.in/gVcKnuhA 10. Teaching AI Fluency https://lnkd.in/g9P4gJFM 11. AI Fluency for Nonprofits https://lnkd.in/gpsm_BVf 12. Claude with Amazon Bedrock https://lnkd.in/gbfPjSFt 13. Claude with Google Vertex AI https://lnkd.in/gvVgB4Ub Appreciated / Courtesy Nishat Jahan
