Why responsible CV prevents misuse in Computer Vision - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is transparency important in responsible computer vision?

Transparency in computer vision models means making clear how the model works and what data it uses. Why does this help prevent misuse?

A It ensures the model only works on images with faces.

B It makes the model run faster and use less memory.

C It allows users to understand and trust the model, reducing chances of hidden biases causing harm.

D It hides the model's weaknesses from users to avoid confusion.

💡 Hint

Think about how knowing what a model does helps people avoid mistakes.

Transparency helps users see how decisions are made, which reduces risks of unfair or harmful outcomes by exposing biases or errors.

🧠 Conceptual

intermediate

How does data privacy in computer vision prevent misuse?

Computer vision systems often use images of people. How does respecting data privacy help prevent misuse?

A By protecting personal images, it prevents unauthorized use or sharing that could harm individuals.

B By making the model ignore all images with people.

C By storing all images publicly for easy access.

D By deleting the model after training.

💡 Hint

Think about what happens if personal photos are shared without permission.

Respecting privacy means personal data is handled carefully, reducing risks like identity theft or surveillance misuse.

❓ Metrics

advanced

Which metric best helps detect bias in a face recognition model?

You want to check if a face recognition model works equally well for different skin tones. Which metric is best to detect bias?

A Accuracy broken down by skin tone groups.

B Training loss value after 10 epochs.

C Overall accuracy on the entire dataset.

D Number of layers in the model.

💡 Hint

Think about comparing performance across different groups.

Measuring accuracy separately for each group reveals if the model favors some groups over others, indicating bias.

🔧 Debug

advanced

What error does this misuse cause in a CV model?

Consider a computer vision model trained only on daytime images but used at night. What problem arises?

A The model will run faster at night.

B The model will likely perform poorly due to different lighting, causing wrong predictions.

C The model will automatically adjust to night images without issues.

D The model will crash with a syntax error.

💡 Hint

Think about how training data affects model performance on new types of images.

Using a model on data very different from training causes errors because it has not learned to handle those conditions.

❓ Model Choice

expert

Which model choice best supports responsible CV to prevent misuse?

You want a computer vision model that is easy to explain and audit to avoid misuse. Which model type is best?

A An unsupervised clustering model with no labels.

B A very deep neural network with millions of parameters.

C A random forest with hundreds of trees and complex interactions.

D A simple decision tree model with clear rules.

💡 Hint

Think about which model type is easiest to understand and explain.

Simple models like decision trees are easier to interpret, making it simpler to detect and prevent misuse.