Bias in generative models in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is bias in generative models?

Bias in generative models means the model produces outputs that unfairly favor or discriminate against certain groups or ideas, often reflecting stereotypes or imbalanced data.

beginner

How can biased training data affect a generative model?

If the training data has more examples from one group or viewpoint, the model learns to generate outputs that reflect that imbalance, leading to unfair or inaccurate results.

intermediate

Name one common source of bias in generative AI models.

One common source is historical or social biases present in the data used to train the model, such as stereotypes or underrepresentation of certain groups.

beginner

Why is it important to detect and reduce bias in generative models?

Because biased outputs can harm people by spreading stereotypes, misinformation, or unfair treatment, reducing trust and causing real-world negative effects.

intermediate

What is one method to reduce bias in generative models?

One method is to carefully curate and balance the training data to represent diverse groups fairly, or to use techniques that adjust the model’s outputs to be more neutral.

What does bias in a generative model usually come from?

A Imbalanced or biased training data

B Random noise in the model

C Too many layers in the neural network

D Using a GPU for training

Bias mostly comes from the data the model learns from, especially if it is not balanced or contains stereotypes.

Which of these is a risk of biased generative models?

A Using more memory

B Producing unfair or harmful content

C Running slower on computers

D Generating random numbers

Biased models can create outputs that are unfair or harmful, affecting people negatively.

How can we check if a generative model is biased?

A By counting the number of layers

B By checking the file size

C By measuring training speed

D By analyzing its outputs for fairness and diversity

We look at the model’s outputs to see if they treat groups fairly and represent diversity.

Which approach helps reduce bias in generative models?

A Balancing training data

B Adding more hidden layers

C Increasing batch size

D Using a different optimizer

Balancing the data helps the model learn fairly about all groups.

Why might a generative model reflect stereotypes?

A Because the model uses too much memory

B Because the model is too simple

C Because stereotypes exist in the training data

D Because the model is trained on random noise

If stereotypes are in the data, the model can learn and repeat them.

Explain what bias in generative models is and why it matters.

Think about how unfair outputs can affect people.

Describe one way to detect and one way to reduce bias in generative AI.

Consider both looking at results and changing the data or model.