{
  "id": 8453252,
  "title": "How to Build a Good Human-in-the-Loop for Machine Learning",
  "url": "https://urgent.news/2026/09/19/how-to-build-a-good-human-in-the-loop-for-machine-learning",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-19T12:00:00.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/brennhill/how-to-build-a-good-human-in-the-loop-for-machine-learning-1ba5"
  },
  "original_language": "en",
  "account": "Human-in-the-Loop (HITL) machine learning involves integrating human judgment throughout the machine learning lifecycle. This includes labeling and annotating data, selecting which examples to label, reviewing the model's low-confidence predictions, and ranking outputs for further training. The goal is not to have humans check everything, as this is inefficient and trains people to rubber-stamp. Instead, human attention should be focused on the most uncertain or informative examples, ensuring genuine judgment and accurate labeling that can be trusted and reversed if necessary.\n\nBuilding an effective HITL system is crucial. Poorly implemented systems can waste labeling hours on irrelevant data. This article provides a step-by-step guide to building a good HITL system for machine learning. It complements LoopRails, which focuses on overseeing autonomous agent actions in the real world. While HITL in machine learning differs from agentic oversight, the core principles are similar.\n\nIn HITL, the loop works differently. It wraps around data and predictions, not live actions, as nothing is deployed in real-time. The human labels training examples, decides which to label, reviews predictions the model is unsure about, and ranks outputs to improve future learning. The same three fixes—routing by uncertainty, designing the review moment for genuine judgment, and avoiding automation bias—apply to both HITL and agentic oversight.",
  "summary": "Human in the loop machine learning is the practice of putting human judgment into the machine learning lifecycle itself: labeling and annotating data, choosing which examples are worth a human's time, reviewing the model's low-confidence predictions, and ranking outputs to train the model further. The goal is not to have a human check everything, which does not scale and trains people to…",
  "key_points": [
    "Human judgment integrated throughout machine learning lifecycle",
    "Focus on uncertain or informative examples for genuine judgment",
    "Three key principles: routing by uncertainty, genuine judgment, avoiding automation bias"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Dev.to",
        "title": "The Science of Machine Learning vs. the Push for AI Deployment",
        "url": "https://urgent.news/2026/09/19/the-science-of-machine-learning-vs-the-push-for-ai-deployment",
        "published": "2026-09-19T13:26:25.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}