A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social…

A major risk when using a pre-trained neural network for logo detection is that it may inherit biases

and defects from the original dataset and training process. This means that the model could

misidentify or fail to recognize certain logos due to:

Differences in data preparation: The original training data may have used a different preprocessing

method than the new dataset, leading to inconsistencies.

Limited transparency: The exact details of the dataset and biases within it may not be known, which

can cause unexpected behavior.

Bias in logo detection: If the model was trained on a dataset with certain color or text p

, it

may disproportionately misidentify logos with similar characteristics.

This inherited bias can result in:

False Positives: Recognizing other brand logos as the beer company's logo.

False Negatives: Failing to detect the actual logo when variations occur (e.g., different lighting or

partial visibility).

Algorithmic Bias: The model may favor certain shapes or color contrasts due to biased training data.

Thus, the most appropriate risk associated with using this pre-trained model is inherited bias.

Reference from ISTQB Certified Tester AI Testing Study Guide:

Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learning explains how pre-trained

models may inherit biases and undocumented defects that affect performance in a new

environment​.