Challenges of trusting without understanding

How can we trust something that doesn't understand emotions? This is the challenge AI managers face today. A recent study published in the Journal of Applied Psychology by Mingyu Li and Bradford Bitterly reveals crucial insights into employee trust in AI versus human managers: ⇢ Employees view AI management as less benevolent compared to human managers, which negatively impacts trust in AI managers. ⇢ In high-empathy contexts, employees strongly prefer human managers who can understand and share their emotions. ⇢ Trust in AI systems can be significantly hindered when employees feel AI lacks emotional depth and benevolence. So, what are the key takeaways for leaders integrating AI into management? ↳ Implement policies that enhance the perceived benevolence of AI systems. ↳ Consider emotional context when deciding between AI and human management. ↳ Deploy AI selectively, maintaining human involvement in high-empathy situations. ↳ Explore strategies to increase AI's perceived emotional capabilities. Reflecting on these findings, I’m convinced that trust in AI management will depend on more than just competence. Leaders must carefully consider the roles of perceived benevolence and emotional context in AI integration to maintain trust. What are your thoughts on this research? How do you think we can address the trust challenges in AI management. #FutureProofYourLeadership #AImanagement #futureofwork #employeetrust

In leadership, workplace culture, and relationships, we must always look deeper before making conclusions. This picture illustrates the danger of judging too quickly. At first glance, the man appears to be making a rude gesture, and the lioness looks like she is harming her cub. But when the full picture is revealed, the truth is very different: 🔹 The man was simply counting with his fingers. 🔹 The lioness was gently carrying her cub to safety. This teaches us that one-sided stories are misleading. Just as a single snapshot can distort reality, incomplete information can lead to wrong decisions in life and work. ✅ In leadership: Quick judgments without facts can weaken trust. ✅ In workplace culture: Misunderstandings can create unnecessary conflicts. ✅ In relationships: Reacting to half-truths can damage bonds permanently. 💡 True leaders don’t rely on assumptions. They listen actively, observe carefully, and seek the full picture before making decisions.

A new study just exposed a hidden truth about LLMs: Your LLM is a black box. You don’t understand how it works and neither do its creators. Here’s what they found: Everyone talks about "understanding AI." But here’s the truth: You can’t open the black box. You can only work around it. Even the engineers can’t explain why a model gave you that output. Read the complete paper here: https://lnkd.in/er3NfHHK The paper argues: Stop obsessing over what AI is. Start asking what AI does. Because most AI systems are judged not by logic, but by how much we trust their output. Think about it: → You trust Google to rank the best link → You trust ChatGPT to summarize a concept → You trust Face ID to unlock your phone But you don’t know why they work. You just trust the results. That’s the black box effect: You’re working with systems whose inner logic is opaque. And still you're using them to make decisions, form opinions, and shape your reality. The researchers say: that’s not a bug, it’s a feature. AI is now part of a sociotechnical system. Meaning: It’s not just about code. It’s about relationships. Between data, humans, interfaces, and beliefs. So the challenge isn’t “make AI explainable.” The challenge is: → When should you trust AI? → When should you doubt it? → How do you judge quality in a system you can’t dissect? That’s the new literacy: Not prompt engineering. Not model weights. Not API calls. But epistemic judgment learning how to live and act in a world where your tools are smarter than you, but dumber than you think. Because the real risk isn’t that AI is too powerful. It’s that we treat it like a god… …when it’s really just a very confident guesser. AI is becoming a co-pilot for everything but we still don’t know why it works. Do you think we’ll ever fully understand LLMs? Or will we just learn to live with the black box? Drop your take below. I’m reading all the replies. -- P.S. Want more like this? The Pathway is a no-fluff newsletter where I break down how to think, create, and work better with AI. Thousands already read it every week. Be one of them: 👉 https://lnkd.in/eW2srN5C Follow me at Sufyan for more AI educational content

Can You Trust AI If You Don’t Understand It? (If AI makes a decision, but you don’t know how, is it reliable?) AI is getting smarter, but it’s also becoming more complex. The challenge? Many AI models operate like “black boxes,” making decisions without clear explanations. Without transparency, businesses risk making decisions based on AI outputs they don’t fully understand. That’s where Explainable AI (XAI) comes in. ↳ XAI helps businesses understand why AI makes specific decisions ↳ It improves trust by providing clear reasoning behind AI-driven insights ↳ It ensures accountability, making it easier to identify and fix biases For industries like finance, healthcare, and legal services, XAI isn’t just helpful—it’s essential. When AI decisions impact real lives, transparency isn’t optional. Would your business benefit from more explainable AI models? ______________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI