feedback loop
What It Means
A feedback loop in AI is when your system learns from real-world results to get better over time. When users interact with AI recommendations or predictions, their actual choices and outcomes become new training data that automatically improves the model's future performance.
Why Chief AI Officers Care
Feedback loops are critical for maintaining AI system performance as business conditions change, but they can also amplify biases or create unstable behavior if not properly managed. They represent both your biggest opportunity for continuous improvement and your highest risk for systematic failures that compound over time.
Real-World Example
A retail AI recommends products to customers on your website. When customers click, purchase, or ignore these recommendations, this behavior feeds back into the system to improve future suggestions. However, if the AI starts showing only popular items because they get more clicks, it creates a loop where niche products never get recommended and sales become increasingly concentrated.
Common Confusion
People often think feedback loops are always beneficial, but negative feedback loops can make AI systems progressively worse by reinforcing bad patterns or biases in the data.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, feedback loops enable diagnostic algorithms to continuously refine accuracy by learning from clinician...
Finance: In finance, feedback loops enable AI models to continuously improve by incorporating actual trading outcomes, loan perfo...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"describes the process of leveraging the output of an AI system and corresponding end-user actions in order to retrain and improve models over time. The AI-generated output (predictions or recommendations) are compared against the final decision (for example, to perform work or not) and provides feedback to the model, allowing it to learn from its mistakes."Source: C3.ai_feedback_loop
Related Terms
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