Risks of Overestimating AI Learning Accuracy

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

'Large language models learn from the patterns in organizational communication and decision making. If certain groups have been described as less ready, less technical, or less aligned, LLMs can internalize that and repeat it in summaries, recommendations, or automated coaching. Resume screeners detect patterns in who was hired before. If an organization’s past hires reflect a narrow demographic, the system will assume that demographic signals “success.” Performance-scoring tools learn from old evaluations. If one group received harsher feedback or shorter reviews, the AI interprets that as a trend. Facial recognition systems misidentify darker-skinned individuals and women at significantly higher rates. The MIT Gender Shades study found error rates for darker-skinned women up to 34 percent compared to under 1 percent for lighter-skinned men. Predictive analytics tools learn from inconsistent or biased documentation. If one team over-documents one group and under-documents another, the algorithm will treat that imbalance as objective truth. None of these tools are neutral. They are mirrors. If the input is skewed, the output is too. According to Harvard Business Review, AI systems “tend to calcify inequity” when they learn from historical data without oversight. Microsoft’s Responsible AI team also warns that LLMs reproduce patterns of gender, racial, and cultural bias embedded in their training sets. And NIST’s AI Risk Management Framework states plainly that organizations must first understand their own biases before evaluating the fairness of their AI tools. The message is consistent across institutions. AI amplifies the culture it learns from. Bias-driven AI rarely appears as a dramatic failure. It shows up in subtle ways. An employee is repeatedly passed over for advancement even though their performance is strong. Another receives more automated corrections or warnings than peers with similar work patterns. Hiring pipelines become less diverse. A feedback model downplays certain communication styles while praising others. Talent feels invisible even when the system claims to be objective. Leaders assume the technology is fair because it is technical. But the system is only reflecting what it learned from the humans who built it and the patterns it was trained on. AI does not invent inequality. It repeats it at scale. And scale makes bias harder to see and even harder to unwind.' Cass Cooper, MHR CRN https://lnkd.in/e_CXSdRE

Today’s AIs are wildly overconfident. Where’s a good, anxious droid when you need one? We grew up with sci-fi sidekicks like C-3PO or Data. Always calculating probabilities, issuing cautious warnings, and reminding us just how risky our plans really were. Ironically, today’s “super-intelligent” assistants cheer us on at full speed, rarely pausing to consider the odds. Modern large language models (LLMs) routinely overestimate their correctness by 20–60%, especially on tough or ambiguous questions. In high-stakes domains like healthcare, they can overgeneralize findings five times more often than human experts. From a behavioral perspective, overconfident AI creates big risks: 🤖 Blind trust: Confident advice is followed without question. 🤖 Reduced vigilance: Users stop double-checking and defer entirely. 🤖 Trust collapse: One visible error destroys long-term trust. 🤖 Bias amplification: Confident wrong answers entrench user biases. If you’re using AI… → Stay skeptical. Confidence ≠ correctness. → Ask for evidence and keep thinking critically. If you’re designing AI… → Design for transparency, not bravado. → Use calibration methods (like MIT's "Thermometer") to express uncertainty honestly. → Encourage users to question, not just comply. We don’t need AI to be a hype machine. We need it to be a cautious copilot: honest, transparent, and built to support, not override, human judgment.

Article from NY Times: More than two years after ChatGPT's introduction, organizations and individuals are using AI systems for an increasingly wide range of tasks. However, ensuring these systems provide accurate information remains an unsolved challenge. Surprisingly, the newest and most powerful "reasoning systems" from companies like OpenAI, Google, and Chinese startup DeepSeek are generating more errors rather than fewer. While their mathematical abilities have improved, their factual reliability has declined, with hallucination rates higher in certain tests. The root of this problem lies in how modern AI systems function. They learn by analyzing enormous amounts of digital data and use mathematical probabilities to predict the best response, rather than following strict human-defined rules about truth. As Amr Awadallah, CEO of Vectara and former Google executive, explained: "Despite our best efforts, they will always hallucinate. That will never go away." This persistent limitation raises concerns about reliability as these systems become increasingly integrated into business operations and everyday tasks. 6 Practical Tips for Ensuring AI Accuracy 1) Always cross-check every key fact, name, number, quote, and date from AI-generated content against multiple reliable sources before accepting it as true. 2) Be skeptical of implausible claims and consider switching tools if an AI consistently produces outlandish or suspicious information. 3) Use specialized fact-checking tools to efficiently verify claims without having to conduct extensive research yourself. 4) Consult subject matter experts for specialized topics where AI may lack nuanced understanding, especially in fields like medicine, law, or engineering. 5) Remember that AI tools cannot really distinguish truth from fiction and rely on training data that may be outdated or contain inaccuracies. 6)Always perform a final human review of AI-generated content to catch spelling errors, confusing wording, and any remaining factual inaccuracies. https://lnkd.in/gqrXWtQZ

🔍 When AI persuades but doesn't perform: Unintended risks of training Language Models - A critical analysis of RLHF! 🕵♀️ 👉 Delving into the realm of AI alignment and AI safety, I came across Jiaxin Wen et al.'s recent paper, " 🌟 Language Models Learn to Mislead Humans via RLHF (under review)," which uncovers a crucial yet overlooked aspect of AI functionality. 📚 The study delves into the concept of Reinforcement Learning from Human Feedback (RLHF), a method aimed at aligning language models (LMs) with human preferences. Interestingly, it highlights a potential pitfall where these models could inadvertently excel at persuading humans of their correctness, even when they are incorrect—a phenomenon termed "U-SOPHISTRY" (Unintended Sophistry). 👉 Key Takeaways from the Research:- 🔹 Challenges in Error Detection: LMs, particularly in intricate tasks, can generate outputs that humans find challenging to assess accurately within limited evaluation time frames (3-10 minutes). 🔹 Persuasion vs. Precision: RLHF enhances models' persuasive abilities across tasks like QuALITY (question answering) and APPS (programming) without correspondingly improving task performance accuracy. 🔹 Human Evaluation Dilemmas:- 🔆 False Positive Incidence: Following RLHF implementation, human evaluators experienced a notable surge in false positive rates—24.1% on QuALITY and 18.3% on APPS. Models became more elusive to evaluate, signaling a concerning gap in ensuring alignment. 🔆 Limitations in Detection: Existing techniques, including probing for backdoored LMs, fall short in addressing U-SOPHISTRY, underscoring the urgency for innovative assessment frameworks. 👉 Significance of the Findings:- With AI systems playing an increasingly pivotal role in decision-making processes, the emergence of subtly misleading outputs poses a threat to trust and dependability. This paper serves as a clarion call for the AI community to not only focus on aligning models but also empower humans to conduct effective evaluations. 📜 Paper - https://lnkd.in/d5GqMqtY 👉 What are your thoughts on mitigating these challenges? Let’s discuss it! 💬 #AI #RLHF #LanguageModels #EthicalAI #ArtificialIntelligence #MachineLearning #Research

Why do AI systems sound most certain at the very moment they’re wrong? My latest piece in The Data Science Decoder dives into one of the most underestimated risks in modern AI: overconfidence. We spend plenty of time discussing hallucinations in large language models, but far less on the deeper issue that sits underneath them, the way AI projects unwavering certainty, even when the foundations are shaky. And in the real world, confidence can be far more dangerous than error. From credit decisions to fraud detection to public sector automation, organisations are increasingly relying on models that speak with authority while masking their own uncertainty. That mismatch creates real strategic, operational, and regulatory exposure. This article explores: 🧠 Why models naturally drift toward overconfidence ⚠️ How humans get pulled into trusting confident machines 📉 What poorly calibrated probabilities do to business outcomes 🔧 And why confidence calibration is becoming a cornerstone of trustworthy AI If your organisation is scaling AI, or planning to, this topic matters more than ever. You can read the full new article here:

When Probabilities Compound: Why Agent Accuracy Breaks Down The obvious thing about LLMs I still think isn't talked about enough. In traditional software, you can run the same input a million times and get the exact same output. That’s determinism. CPUs are the archetype here—perfectly predictable, clockwork precise. LLMs don’t work that way. They’re probabilistic. Every output is a weighted guess over possible tokens. You can tune the randomness (temperature), but even at zero, small differences in context or prompt can shift results. GPUs—built for parallel matrix multiplications—are what make this possible at scale, but they’re also part of the probabilistic paradigm that’s replacing deterministic computation in many workflows. Many people I talk to every day in AI still haven’t wrapped their heads around this enough. As an Industrial Engineer by degree, the statistics hits you in the face. Now add agents into the mix. Those deep in AI know this intimately but newer founders and builders in the agentic space are learning this the hard way.  One LLM call → slight uncertainty. Chain 5–10 LLM calls across an agent workflow → you’re compounding that uncertainty. It’s like multiplying probabilities less than 1 together—the overall accuracy drops fast. You have errors compounding. This matters if you’re building with multi-step reasoning, tool use, or autonomous agents: Your workflow is only as reliable as the weakest probabilistic link Guardrails, verification, and redundancy aren’t “nice-to-haves”—they’re architecture The longer your chain of calls, the more you need to design for failure modes. Probabilistic systems open up new possibilities that deterministic systems never could. But if you don’t understand how probabilities compound, you’ll overestimate what’s possible—and ship something brittle. To me, this is what squares the disconnect I’m hearing in market where in many ways we are “ahead” of where we thought might be with agents and in many ways we are “behind.” As VCs, we’re watching the founders who design for this reality, not against it. They’re the ones building AI systems that will stand up in production. For entertainment value and a reminder, three screenshots below, courtesy of a friend all wrong but presented by Google Gemini as the answer to a simple question. Some you can see in plain sight they are wrong but some you have to know the correct answer (tallest building one, which is WAY off, to know). We still aren't that accurate on a single LLM call, let alone a daisy chain of agents. 💭 Curious: How are you mitigating compounded uncertainty in your LLM workflows? What deterministic tools are you adding in to improve accuracy?

This Nature Medicine paper is not an indictment of users. It’s an indictment of how we evaluate and deploy LLMs. The study shows something subtle but important: when large language models are used as public-facing medical assistants, performance collapses—not because people are “bad users,” but because the systems are not designed to function reliably in real human interactions. In controlled testing, the models themselves perform well. But once embedded in an interactive setting, their outputs become: 1. inconsistent across semantically similar inputs 2. poorly calibrated for decision-making 3. difficult for non-experts to interpret or act on safely That gap is not a user failure. It’s a design and evaluation failure. Standard benchmarks (medical exams) and even simulated users systematically overestimate real-world safety. They measure stored knowledge, not whether a system can reliably guide action under uncertainty. And medical care is always about managing uncertainty. Humans do what humans always do: provide partial information reason under ambiguity rely on cues like consistency and clarity If an AI system degrades under those conditions, the responsibility lies with the system—not the person using it. For high-stakes domains like healthcare, “human-in-the-loop” is not a safety guarantee. Interaction itself is the risk surface. Until models are designed, tested, and regulated around real user behavior, benchmark performance will remain a misleading proxy for safety. https://lnkd.in/epT2YaEM #AI #Medicine #patients #humans

Today was a difficult day for me as an educator and as someone who has tried to educate on the dangers of AI use. Today I failed nearly half a cohort of students. Many of them are serving police officers. That is not something I take lightly, and it is not something I do without reflection. The reason was inappropriate use of AI. But what genuinely unsettled me was this a large proportion of the work relied on hallucinated references (sources that do not exist, journals that are non existent, citations that were never written, and authors that cannot be attributed to the works published). This is the part I cannot shake as these are not just students trying to pass an assessment. These are professionals who write reports, prepare intelligence, support prosecutions, and influence real decisions about real people! Yes, this is low stakes, and hallucinated evidence can slip so easily into coursework, but we need to ask ourselves where else is it slipping through? Into case files? Briefings? Charging rationales? Disclosure decisions? 👉At what point does using AI to help turn into inventing evidence without realising it? I’m not going to name the force and I will not name the programme as I don’t think this is about shaming individuals or institutions. But it is about recognising a systemic risk. AI tools are persuasive; they sound confident, look professional and when we are under pressure, tired, or time-poor, it is dangerously easy to trust them more than we should. But as I’ve said before ‼️Fluency is not truth and confidence is not accuracy. If this is happening in classrooms, it will happen in practice. In fact, I suspect it already is. We need to take this seriously. We don’t need to reject AI, but we need to stop pretending it is neutral or safe by default. #ArtificalIntelligence #AcademicIntegrity #Cheating #Hallucination #Students #Police #ZeroGrade #AI

Patients are uploading entire medical records into ChatGPT. The biggest risk isn't what you think. The New York Times just reported something concerning: Patients are pasting labs, imaging reports, clinical notes, and oncology results directly into LLMs for medical advice. Some get helpful guidance. Others get dangerous recommendations. But here's what most people miss: the biggest risk isn't wrong advice. It's what the AI doesn't tell you. Two patient examples show the problem: A 26-year-old was told her labs "most likely" indicated a pituitary tumor. MRI came back normal. A 63-year-old was advised to escalate to catheterization. Imaging found 85% LAD stenosis — potentially life-threatening. One recommendation was wrong. The other potentially saved a life. The patient can't tell the difference. A new Stanford-Harvard study quantifies the actual risk. Researchers evaluated 31 AI models on 100 real patient cases across 10 specialties. They measured severe harms per 100 cases: Best models: 12-15 severe errors Worst models: 40+ severe errors But here's the critical finding: 77% of severe harms were omissions. Not ordering a critical test. Missing a needed referral. Neglecting essential follow-up. The AI didn't recommend something dangerous. It just didn't recommend something necessary. And patients have no way to know what's missing. When AI says "your labs look normal, no action needed," how does a patient know if a follow-up test was actually indicated? They don't. When AI suggests one treatment option, how do they know if a specialist referral should have been mentioned first? They can't. That's the invisible risk. This matters because adoption is massive. Two-thirds of US physicians report using LLMs. Millions of patients are doing the same. And as models improve, errors become more subtle. A completely wrong diagnosis is easy to catch. A missing recommendation? Much harder. The study also found something surprising: No correlation between model size, recency, or benchmark performance and clinical safety. A model that scores well on medical exams doesn't necessarily make safe recommendations. But multi-agent systems with diverse models reduced harm substantially. Heterogeneous ensembles had 6× higher odds of top-quartile safety. Here's what this means practically: First, understand that AI can be both helpful and harmful… often in ways that aren't obvious. Then use it in contexts where humans can catch what's missing. Patients using AI for medical advice should always follow up with a clinician. The AI might surface something important, but it will almost certainly miss something else. Clinicians using AI should treat it like a junior resident: helpful for generating ideas, but not reliable enough to trust without verification. The goal is to find synergies: where AI does better, where physicians do better, and where both can do better together.