How to Translate ML Confidence into User Trust

The better your ML system gets, the more painful its failures become. When a system works 95% of the time, people start to 𝘁𝗿𝘂𝘀𝘁 it. They stop checking. They assume it just works. And then? One strange failure. Unexplained. Misaligned. Just off. And trust is gone. This happens all the time in: - Agent-based systems - RAG pipelines - Customer-facing applications - Even a simple churn model that flags your biggest client by mistake 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗯𝘂𝗶𝗹𝗱𝘀 𝘁𝗿𝘂𝘀𝘁. 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝗿𝗶𝘀𝗸. That’s the paradox. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗿𝗮𝗶𝘀𝗲𝘀 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀. And when your system slips, it doesn’t feel like a bug.  It feels like a breach of trust. 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁? This is not just for production teams. Even small portfolio projects benefit from this mindset. 𝟭. 𝗗𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗳𝗼𝗿 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆. • Track weird and rare inputs • Tag errors by why they happened, not just how many • Log how users react: ignore, undo, repeat 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗮𝗻𝗱 𝗳𝗮𝗹𝗹𝗯𝗮𝗰𝗸𝘀. • Add a check: if confidence is low, return a safe default • Show a confidence score or quick explanation • Catch hallucinations and infinite loops with basic logic 𝟯. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁. • First-time users need context and safety • Repeat users can become overconfident, so remind them • If your model or data changes, let the user know 𝗜𝗻 𝗼𝗻𝗲 𝗥𝗔𝗚 𝗽𝗿𝗼𝗷𝗲𝗰𝘁, 𝘄𝗲 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝘀𝗼𝗺𝗲 𝘂𝘀𝗲𝗿 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝘄𝗲𝗿𝗲 𝘁𝗼𝗼 𝘃𝗮𝗴𝘂𝗲 𝗼𝗿 𝗼𝘂𝘁-𝗼𝗳-𝘀𝗰𝗼𝗽𝗲. Instead of letting the system hallucinate, we added a simple check: → If similarity score was low and the top documents were generic, we showed: “𝘚𝘰𝘳𝘳𝘺, 𝘐 𝘯𝘦𝘦𝘥 𝘮𝘰𝘳𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘵𝘰 𝘩𝘦𝘭𝘱 𝘸𝘪𝘵𝘩 𝘵𝘩𝘢𝘵.” It wasn’t fancy. But it prevented bad answers and kept user trust. Because these systems don’t just run on code. They run on trust. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹 𝘂𝘀𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗳𝗼𝗿 𝗮 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁: → Start small. → Add a fallback → Explain your outputs → Track how your system performs over time These aren’t advanced tricks. They’re good engineering. 💬 What’s one thing you could add to your current project to make it more reliable? ♻️ Repost to help someone in your network.

𝗪𝗵𝘆 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗖𝗮𝗻 𝗕𝗲 𝗗𝗲𝗰𝗲𝗶𝘃𝗶𝗻𝗴 (𝗔𝗻𝗱 𝗪𝗵𝗮𝘁 𝗧𝗼 𝗗𝗼 𝗔𝗯𝗼𝘂𝘁 𝗜𝘁) Choosing between ML models. It's a common task. Look at this: Model A and B have identical 88% accuracy. But Model B boasts 99% average confidence, while A is at 89%. Instinct might scream, "Model B!". Higher confidence, same accuracy, sounds better, right? Not so fast. First, let's be clear. "Accuracy" is simple: how often the model gets it right. "Average Confidence" is the model's own reported certainty in its predictions, averaged out. The trap? Picking Model B purely on higher confidence. An uncalibrated model can be consistently overconfident. Imagine it shouting "I'm 99% sure!" but still being wrong 12% of the time. That's not useful. It's actually misleading. This is where "calibration" comes in. A well-calibrated model's confidence 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘳𝘦𝘧𝘭𝘦𝘤𝘵𝘴 its likelihood of being correct. If it says 70% confident, it should be right about 70% of those times. This is crucial for trust, especially in systems where wrong decisions have real costs. An overconfident, poorly calibrated model is a risk. ⚙️ So, how do we aim for well-calibrated neural networks? 𝗗𝘂𝗿𝗶𝗻𝗴 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴:  • Use appropriate loss functions (cross-entropy is a good start).  • Sufficient, diverse data helps prevent skewed learning. 𝗣𝗼𝘀𝘁-𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴:  • Temperature scaling is a common technique to adjust output probabilities.  • Methods like Platt Scaling or Isotonic Regression can also be applied. Essentially, we need to:  1. Measure calibration (e.g., using reliability diagrams, Expected Calibration Error - ECE).  2. Design and train with calibration in mind from the outset.  3. Apply post-hoc adjustments if the model's confidence scores are still off. It's not just about 𝘪𝘧 a model is right, but 𝘩𝘰𝘸 𝘳𝘦𝘭𝘪𝘢𝘣𝘭𝘺 it knows when it's right. 🤔 What are your go-to methods for ensuring your models are well-calibrated, especially when stakes are high? #MachineLearning #ModelSelection #Calibration #TechWithRamesh

What large language models know and what people think they know Large language models (LLMs) have rapidly become integral to scientific and practical applications, from educational support to complex decision-making. Despite their growing influence, researchers still grapple with determining how certain these systems are when generating responses solely through textual output. Such uncertainty communication is vital for building trust, yet it remains unclear whether users can accurately gauge an LLM’s internal confidence simply by reading its explanations. Steyvers et al. tackled this challenge by testing multiple-choice and short-answer questions from established benchmarks (MMLU and Trivia QA), using GPT-3.5, PaLM2, and GPT-4o to generate both answers and token-likelihood estimates of correctness. These internal probabilities, often extracted via a “readout” of the model’s token-level predictions, served as the reference for each model’s own confidence. The authors then manipulated explanation styles, including variation in length and phrasing of uncertainty, to see how textual cues influenced user judgments. Calibration metrics such as expected calibration error (ECE) and the area under the curve (AUC) were used to compare how well human confidence aligned with the models’ measured probabilities. The researchers observed that default model explanations often led users to overestimate correctness, with explanation length unintentionally increasing perceived reliability. However, when explanation text was tailored to reflect actual internal probabilities, both the calibration gap and the discrimination gap narrowed, making people’s confidence more proportional to the model’s true accuracy. By systematically translating numeric model confidence into verbal expressions, the authors show how improved uncertainty communication can help scientists and non-experts better gauge when an LLM is likely correct or in error, thus enhancing informed, responsible use of advanced AI systems. Paper: https://lnkd.in/d4rZsc5f #MachineLearning #AI #LanguageModels #NaturalLanguageProcessing #NeuralNetworks #Calibration #ExplainableAI #TrustInAI #HumanAIInteraction #AcademicResearch #ComputationalModels #DataScience #Innovation #ResearchInsights #AIforScience