How feedback loops in AI work
In AI and machine learning, a feedback loop is a continuous, iterative process designed to improve the performance of an AI model and make it more reliable and accurate over time.
During anotación de datos, a team of expert annotators will label, enriche, and expand on an initial dataset to add nuanced context and perspectives. They flag potential bias and spot inaccuracies, preventing these issues from silently creeping into AI models down the road. Through multiple rounds of feedback, they refine the data, ensuring a solid, high-quality foundation for AI models.
Typically, an iterative AI feedback loop looks like this:
- Initial annotation: A team of human annotators manually labels a small, initial dataset based on a set of annotation guidelines or instructions. This data is used to train the first version of the AI model.
- Model prediction: The newly trained model is then used to automatically annotate a larger batch of unlabeled data. The model makes predictions over unseen data, based on the patterns it learned from annotated data.
- Human review: Annotators carefully review the AI’s predictions and evaluate if responses are accurate or appropriate. They provide feedback on factual errors, inconsistencies, and instances where the dataset might not be representative or sufficiently complete. Annotators pay special attention to edge cases or situations where the AI showed low confidence, as these are the moments where the model is most likely to fail.
- Feedback & retraining: The corrected data is fed back into the model. The AI learns from its mistakes and adjusts its internal parameters. Annotator insights can also be used to improve the annotation guidelines, add more specific examples, develop new rules for handling ambiguous situations, and refine criteria for future annotation projects.
- Iteration: This cycle repeats. Each round of human review and feedback makes the AI model smarter and more accurate.
Why AI models need human feedback loops
The quality of your training data directly influences the performance of AI models. But datasets are not static — they are dynamic and must be constantly improved through human feedback loops.
Through this iterative process, expert annotators not only correct factual errors and identify inconsistencies, but also flag potential biases and ensure annotation guidelines are clear and comprehensive.
Here’s why feedback loops are vital for building reliable AI models:
Enhance accuracy and correct errors
AI is prone to making factually incorrect predictions, known as hallucinations. This can be especially risky in sensitive fields such as legal and healthcare. Examples include an FDA AI tool citing nonexistent studies for drug approval, and hundreds of court cases where AI fabricated false legal citations.
Hallucinations are often the result of training models with incomplete, imprecise, or ambiguous data. Feedback loops help identify these errors and fill those gaps by providing the model with accurate data.
During AI model training and validation, human feedback is necessary to fine-tune certain parameters and reduce overfitting, improving the model’s capacity of making generalizations over unseen data.
Mitigate bias and uphold ethical standards
Through human feedback, it’s possible to detect biases in a model’s data. It can also predict issues early and guide the model towards fair and ethically sound decisions.
AI image generators, for instance, can reproduce and amplify societal stereotypes, such as consistently associating certain professions with a specific gender or underrepresenting particular cultural and ethnic groups. Having diverse teams of annotators and continuous feedback loops can help identify subtle imbalances in datasets and correct potential biases before they become ingrained in AI models.
Adapt to nuance and context
AI systems often struggle to grasp the nuances of human communication, such as humor, sarcasm, irony, and subtle changes in tones. These are all inherently human traits, connected with our experiences, emotions, and cultural background. That’s why human feedback is vital.
By providing contextual information and nuanced insights, expert human annotators can teach AI models how to understand these cues, which leads to more natural, relevant, and helpful responses.
Adapt to evolving conditions and stay updated
AI training is never truly completed. Once an LLM is in a live production environment, feedback loops are still necessary to ensure its predictions remain accurate and relevant over time.
By monitoring user interactions and analyzing real-time feedback, teams can ensure their models adapt to new data patterns, evolving trends, and changing user needs and preferences. This creates an ongoing process of learning and refinement, ensuring the AI stays effective and relevant in a dynamic world.
Best practices for building scalable and effective feedback loops in data annotation
As data volumes grow and models become more complex and sophisticated, ensuring high-quality training data becomes a constant challenge. To address this, you need to create a solid data annotation strategy — and this includes building scalable and effective feedback loops.
Here are some best practices to get started:
- Develop crystal clear annotation guidelines. Guidelines should always be under construction. Over time, you should include new, representative examples or detailed instructions based on annotator feedback. While they need to be complete, you should also allow annotators enough flexibility to handle nuanced or unforeseen scenarios.
- Encourage annotators to provide feedback. Encourage annotators to flag unclear instructions or new edge cases as soon as they notice them.
- Implement quality control methods. Quality assurance should be an ongoing process. This involves techniques such as:
- Inter-annotator agreement (IAA): Evaluate consistency by having multiple annotators label the same data. A high level of agreement (IAA) indicates that your guidelines are clear and your annotators are well-trained.
- Expert review panel: Allow senior annotators or domain experts to regularly audit a sample of the data. This provides targeted feedback to the team and ensures they meet the highest standards, especially for complex, niche, or highly subjective tasks.
- Active learning: Use a semi-automated approach where the AI model flags data it is “uncertain” about. This is an efficient approach that can point human annotators towards the most challenging examples, making their review most impactful.
- Curate a skilled, diverse annotator workforce. Choosing between expert annotators and crowdsourced teams has a decisive impact on the quality, speed, and cost of your AI model. Projects in complex, highly sensitive fields often require deep domain knowledge to ensure accuracy. While crowdsourced outsourcing is sometimes used for high-volume, simple, and objective tasks, it can lead to inconsistent quality due to a lack of domain expertise.
Building reliable and precise AI models requires high-quality training data, refined by expert human supervision. Implementing continuous feedback loops is the most efficient way to achieve this. Feedback not only makes your models smarter and more ethically sound, but also provides long-term efficiency gains by preventing model drift and reducing error rates over time.
At Sigma, we provide teams with high-quality human annotation at scale, helping you build and deploy even the most challenging AI models. We combine the skills of a professional, diverse, and continuously trained team of annotators, along with a framework of robust processes, which includes iterative feedback loops and quality assurance methods. Talk to an expert to learn how we can help you enhance your AI data quality.