An AI system is trained to classify satellite images for urban planning applications. The system incorrectly classifies solar panels as rooftop HVAC units at a high rate. The training dataset contains 50,000 HVAC unit images and 200 solar panel images. Which risk does this illustrate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the 250:1 ratio of HVAC to solar panel training examples creates severe class imbalance. The model has insufficient examples of solar panels to learn their distinguishing features and defaults to classifying ambiguous rooftop objects as HVAC units (the majority class).
Full explanation below image
Full Explanation
B is correct because the 250:1 ratio of HVAC to solar panel training examples creates severe class imbalance. The model has insufficient examples of solar panels to learn their distinguishing features and defaults to classifying ambiguous rooftop objects as HVAC units (the majority class). This is a classic class imbalance risk. Concept drift (A) would require the solar panels to have been present in training data and the relationship to have changed over time. Adversarial manipulation (C) requires intentional attack. Overfitting (D) would typically show poor generalization on both classes, not a specific minority-class failure pattern.