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AI Ethics and Generative AI Worksheet

Total questions: 41

Worksheet time: 21mins

Name
Class
Date
1.

What is the definition of ethics in the context of AI?

a)

A set of mathematical formulas for AI programming

b)

An invisible rulebook that helps us decide what's right and wrong

c)

Guidelines for making AI faster and more efficient

d)

Legal requirements for AI deployment

2.

Which of the following is NOT one of the five main ethical considerations for AI?

a)

Privacy

b)

Explainability

c)

Bias

d)

Speed

3.

What does "explainability" refer to in AI ethics?

a)

The ability to understand how an AI reached a conclusion

b)

Making AI systems speak human language

c)

The speed at which AI can process information

d)

The cost of developing AI systems

4.

How can bias occur in AI systems?

a)

When AI is programmed with malicious intent

b)

When AI learns from biased data

c)

When AI systems become too complex

d)

When AI is used by biased people

5.

True or False: AI ethics is about ensuring AI is used in a fair, safe, and respectful manner.

a)

True

b)

False

6.

What does "generate" mean in the context of AI?

a)

To analyze existing data

b)

To cause something to exist, to produce

c)

To translate between languages

d)

To optimize algorithms

7.

What is generative AI?

a)

AI that only analyzes data

b)

AI that can create new content

c)

AI that controls robots

d)

AI that replaces human decision-making

8.

Which of the following is a limitation of language models?

a)

They always provide correct answers

b)

They lack common sense reasoning

c)

They are too expensive to use

d)

They only work with English language

9.

True or False: AI language models always generate accurate and sensible responses.

a)

True

b)

False

10.

What is one reason language models might produce incorrect responses?

a)

They generate text based on patterns, not understanding

b)

They are intentionally programmed to be creative

c)

They only use limited training data

d)

They try to mimic human errors

11.

What are AI image generation models like?

a)

Computer artists that can make new pictures

b)

Digital cameras that capture images

c)

Photo editing software

d)

Image search engines

12.

What are the two components of a Generative Adversarial Network (GAN)?

a)

Encoder and Decoder

b)

Simulator and Analyzer

c)

Generator and Discriminator

d)

Creator and Evaluator

13.

True or False: The generator in a GAN is trained to fool the discriminator into believing that the images it generates are real.

a)

True

b)

False

14.

What type of data do image models primarily work with?

a)

Text

b)

Numeric data

c)

Image data (pixels)

d)

Sound

15.

How can you tell an image was created by AI?

a)

It's impossible to tell

b)

The image will be very dark and gloomy

c)

There will be a stamp on it that says it was made by AI

d)

Look for parts that are either too perfect or seem unnatural

16.

What are deep fakes?

a)

AI-generated images with deep colors

b)

Manipulated images/videos that seem very real but are made up

c)

Images created using deep learning algorithms only

d)

3D images with depth perception

17.

What is a prompt in the context of AI?

a)

A computer programming language

b)

A question or instruction given to AI

c)

A type of AI algorithm

d)

A dataset for training AI

18.

Why is it important to craft clear and specific prompts for AI?

a)

It helps to make the AI smarter

b)

It increases the chance you get accurate and useful responses

c)

It helps the AI understand human emotions

d)

It allows the AI to access the internet faster

19.

What is prompt engineering?

a)

Building physical prompts for robots

b)

Creating effective prompts for AI tools

c)

Engineering prompt delivery systems

d)

Designing computer prompt interfaces

20.

For a science exam, you need to learn about the water cycle. Which prompt is better?

a)

Tell me about the water cycle

b)

Could you explain the water cycle in five simple steps?

21.

True or False: Very long prompts always lead to better AI responses.

a)

True

b)

False

22.

How does AI image generation work?

a)

By copying existing images from the internet

b)

By learning from lots of images and combining elements

c)

By following explicit programming rules for each image

d)

By asking human artists for guidance

23.

What is the primary purpose of AI in creating artwork?

a)

To eliminate the need for human artists

b)

To assist and enhance human creativity

c)

To mimic famous artists exactly

d)

To replace traditional art forms with digital art

24.

Why might a professional artist use AI?

a)

For inspiration to enhance their own creativity

b)

As a starting point and add their own artistic layers

c)

To generate art with creative prompts

d)

All of the above

25.

Which of the following is a tip for writing better prompts for AI image generation?

a)

Use vague descriptions to allow AI creativity

b)

Be specific and use descriptive language

c)

Always use short, one-word prompts

d)

Avoid mentioning colors or styles

26.

What should you specify besides the main character or subject in AI-generated art?

a)

The setting, art style, color, and texture

b)

The date the art was created

c)

What you would like to name the artwork

d)

Why you want to create the artwork

27.

What is critical thinking?

a)

Thinking quickly about problems

b)

Thinking carefully and deeply about an issue

c)

Criticizing everything you encounter

d)

Using critical software tools

28.

Why is critical thinking important when working with AI?

a)

AI output could be inaccurate, biased, or manipulative

b)

AI requires complex thinking to operate

c)

AI systems only respond to critical commands

d)

Critical thinking makes AI work faster

29.

What are AI hallucinations?

a)

When AI dreams like humans do

b)

When AI generates incorrect or strange output

c)

When AI sees things that aren't there in images

d)

When AI needs to sleep and reboot

30.

Why does AI have bias in certain situations?

a)

Because programmers intentionally add bias

b)

Because AI learns from biased datasets

c)

Because AI naturally prefers certain outcomes

d)

Because bias makes AI more efficient

31.

What should you do when you receive data from an AI?

a)

Always trust it completely

b)

Apply critical thinking and verify if needed

c)

Immediately share it with others

d)

Assume it's better than human-generated data

32.

How are AI and machine learning related to each other?

a)

Machine learning is a subset of AI

b)

AI is a subset of machine learning

c)

They are completely separate fields

d)

Machine learning only applies to robots

33.

Why is this type of machine learning called "supervised learning"?

a)

Because it is trained on super difficult datasets

b)

Because an engineer labels the dataset and corrects the model

c)

Because the model is very young and requires constant supervision

d)

Because an engineer watches the model as it learns

34.

What does it mean to label a dataset?

a)

To give each piece of data a text or numeric value that identifies it

b)

To specify whether a dataset is useful or not

c)

To print out each piece of data and scan it into a computer

d)

To remove unused information in a dataset

35.

If labeling a dataset to train an AI model to identify different types of trees, which labels would you use?

a)

Daisy, rose, tulip, poppy, lily

b)

Trunk, branch, twig, leaf, root

c)

Elm, pine, spruce, redwood, oak

d)

Grass, dirt, rock, bark, sand

36.

What is a decision tree in machine learning?

a)

A magical tree that makes decisions

b)

A method of programming that helps a person grow trees

c)

A way of determining an answer by asking many yes/no questions

d)

An algorithm that provides random answers to questions

37.

Why is this type of machine learning called "unsupervised learning"?

a)

Because it doesn't require human supervision during training

b)

Because it's easier than supervised learning

c)

Because it works without any data

d)

Because it supervises other AI models

38.

What is the main goal of the clustering technique in unsupervised learning?

a)

To predict future values

b)

To group similar data points together

c)

To classify data into predefined categories

d)

To optimize algorithms for speed

39.

What is the main goal of the association technique in unsupervised learning?

a)

To find relationships between variables

b)

To sort data alphabetically

c)

To translate between languages

d)

To generate new data points

40.

Which of the following is an example of a clustering algorithm?

a)

Grouping customers by purchasing behavior

b)

Predicting house prices based on features

c)

Classifying emails as spam or not spam

d)

Translating text from English to Spanish

41.

Why would an engineer use unsupervised learning on a large, unorganized dataset?

a)

To find interesting patterns in the data

b)

To reduce the data into a more manageable size

c)

To organize the data automatically

d)

All of the above