Bias detection and fairness metrics in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is bias detection in machine learning?

Bias detection is the process of identifying unfair or prejudiced behavior in a machine learning model, where the model's predictions may favor or discriminate against certain groups.

beginner

Name two common fairness metrics used in bias detection.

Two common fairness metrics are Demographic Parity (ensuring equal positive rates across groups) and Equalized Odds (ensuring equal true positive and false positive rates across groups).

intermediate

What does Demographic Parity measure?

Demographic Parity measures whether different groups have the same probability of receiving a positive prediction, regardless of the true outcome.

intermediate

Explain Equalized Odds in simple terms.

Equalized Odds means that a model should have similar true positive and false positive rates for different groups.

beginner

Why is bias detection important in MLOps?

Bias detection is important in MLOps to ensure models are fair, trustworthy, and comply with ethical and legal standards before deployment and during monitoring.

Which fairness metric checks if all groups have the same chance of a positive prediction?

A Equalized Odds

B Demographic Parity

C Precision

D Recall

Demographic Parity ensures equal positive prediction rates across groups.

What does Equalized Odds require from a model?

A Equal number of samples per group

B Equal overall accuracy across groups

C Equal training time for each group

D Equal false positive and true positive rates across groups

Equalized Odds means the model has similar true positive and false positive rates for all groups.

Why is bias detection part of MLOps?

A To ensure models are fair and ethical

B To speed up model training

C To reduce model size

D To increase data volume

Bias detection helps maintain fairness and ethical standards in deployed models.

Which of these is NOT a fairness metric?

A Mean Squared Error

B Equalized Odds

C Demographic Parity

D Predictive Parity

Mean Squared Error measures prediction error, not fairness.

If a model favors one group over another in predictions, what is this called?

A Accuracy

B Overfitting

C Bias

D Normalization

Bias means unfair preference or discrimination in model predictions.

Describe what bias detection means in machine learning and why it matters.

Think about how models can treat groups differently and why we want to avoid that.

Explain two fairness metrics used to evaluate machine learning models.

Focus on how these metrics check if the model treats groups fairly.