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Designing Feedback Loops for Human-in-the-Loop AI

Even the most advanced AI models benefit from human expertise. Human-in-the-loop (HITL) systems integrate human judgment into the AI decision-making process – not as an afterthought, but as a core design principle.

Event Sourcing provides the ideal infrastructure for HITL, because it records every prediction, every human intervention, and every outcome as immutable events. This creates a complete audit trail and enables continuous improvement through well-structured feedback loops.

Why Human Feedback Matters

AI models can misinterpret signals, miss edge cases, or produce confident but incorrect predictions. Humans bring:

  • Contextual understanding – knowledge of domain rules, exceptions, and cultural nuances.
  • Ethical judgment – the ability to consider fairness, risk, and long-term consequences.
  • Adaptability – rapid response to novel or ambiguous situations.

By integrating human feedback at the right points, you can increase accuracy, reduce risk, and maintain stakeholder trust.

Designing the Feedback Loop

An effective HITL loop involves four stages:

  1. Prediction – the AI model makes an initial recommendation or decision.
  2. Review – a human assesses the prediction, validating or adjusting it as needed.
  3. Action – the decision is executed, either automatically or with human oversight.
  4. Recording – both the AI's output and the human's input are stored as events.

Because each step is captured in the event stream, you can later analyze where human input had the most impact and whether the AI's reasoning improved over time.

Example (Library Domain)

Imagine a recommendation system that suggests books to members. For rare or high-value items, a librarian might review the recommendation before it's shown.

Events could include:

  • RecommendationGenerated (AI output)
  • RecommendationApproved or RecommendationModified (human decision)
  • RecommendationDelivered (action taken)

This allows you to measure, for example, whether librarian modifications improved member engagement or borrowing rates.

AI/ML Considerations

For AI pipelines, HITL feedback:

  • Improves training data – human-corrected outputs can be fed back into the model to reduce future errors.
  • Supports explainability – each human decision is tied to specific model outputs and context.
  • Enables selective automation – over time, you can identify scenarios where the AI consistently performs well and reduce manual review.

Best Practices

  • Define clear criteria for intervention so humans focus on cases where they add the most value.
  • Capture both the AI's original output and the human's final decision for full context.
  • Use feedback data to retrain models regularly, ensuring the system evolves with domain needs.
  • Maintain transparency by making the feedback process and its impact visible to stakeholders.

By embedding human judgment directly into the event flow, you get the best of both worlds: scalable AI automation and the nuanced decision-making that only humans can provide.

Next up: Return to Closing the Loop and review how all these elements fit together into a continuous learning and improvement cycle.