Term
Human-in-the-Loop (HITL) (ˈhjuː-mən ɪn ðə ˈluːp)
Definition
Human-in-the-Loop, or HITL, is a workflow process in artificial intelligence where humans review, correct, or guide the outputs produced by AI to ensure they meet quality standards.
Where you’ll find it
In most AI platforms, HITL is part of the development and maintenance areas. It can be integrated into various stages like data labeling, ongoing training, or final output checks. This setup is common across platforms, whether you are working with machine learning models, automated content generation, or data analysis tools.
Common use cases
- Data Labeling: Humans annotate training data, helping the AI learn accurately.
- Quality Control: Ensuring the final outputs from AI applications are correct and reliable.
- Ongoing Training: Human feedback improves AI algorithms by correcting errors and refining processes.
Things to watch out for
- Balance: Avoid over-relying on either AI or human input; finding a healthy balance is key.
- Bias: Human reviewers may introduce biases that can affect AI performance and fairness.
- Scalability: Excessive human involvement might limit the scalability of AI solutions.
Related terms
- Automated Machine Learning (AutoML)
- Model Validation
- Supervised Learning
- Data Annotation
- Quality Assurance