Ethics and bias in AI in Intro to Computing - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is ethics in AI?

Ethics in AI means making sure AI systems behave in a way that is fair, safe, and respects people's rights. It is about doing the right thing when creating and using AI.

beginner

Define bias in AI.

Bias in AI happens when an AI system treats some people unfairly because of wrong or incomplete data or design. It can cause unfair results for certain groups.

intermediate

Why can AI systems become biased?

AI systems can become biased because they learn from data that may have mistakes, stereotypes, or missing information. Also, the way people design AI can add bias.

beginner

Give an example of ethical concern in AI.

An example is when AI decides who gets a loan. If the AI unfairly denies loans to certain groups, it raises ethical concerns about fairness and discrimination.

intermediate

How can we reduce bias in AI?

We can reduce bias by using diverse and complete data, testing AI for fairness, involving different people in design, and being open about how AI works.

What does bias in AI usually come from?

A Faulty or incomplete data

B AI hardware failure

C User mistakes only

D Internet speed

Bias in AI mostly comes from faulty or incomplete data that the AI learns from.

Which is an ethical issue in AI?

A AI using too much electricity

B AI running slowly

C AI making unfair decisions about people

D AI needing updates

Making unfair decisions about people is an ethical issue in AI.

How can AI bias affect people?

A By increasing screen brightness

B By making AI faster

C By improving battery life

D By giving unfair advantages or disadvantages

AI bias can give unfair advantages or disadvantages to certain groups of people.

Which action helps reduce AI bias?

A Avoiding testing AI

B Using diverse data sets

C Ignoring user feedback

D Using only one data source

Using diverse data sets helps reduce bias in AI systems.

Ethics in AI means:

A Making AI systems fair and safe

B Making AI faster

C Making AI cheaper

D Making AI colorful

Ethics in AI means making AI systems fair, safe, and respectful of people.

Explain what bias in AI is and why it happens.

Think about how AI learns from data and how that can cause unfair results.

Describe some ways to make AI ethical and reduce bias.

Consider steps to make AI fair and trustworthy.

Practice

1. What does ethics in AI mainly focus on?

easy

A. Making fair and safe choices in AI systems

B. Improving AI speed and performance

C. Adding more features to AI software

D. Reducing the cost of AI development

Solution

1. Step 1: Understand the meaning of ethics in AI

   Ethics in AI means making decisions that are fair and safe for everyone.

2. Step 2: Compare options with the definition

   Only Making fair and safe choices in AI systems talks about fairness and safety, which matches ethics in AI.

3. Final Answer:

   Making fair and safe choices in AI systems -> Option A

4. Quick Check:

   Ethics = Fair and safe choices [OK]

Hint: Ethics means fairness and safety in AI decisions [OK]

Common Mistakes:

• Confusing ethics with technical improvements
• Thinking ethics is about cost or speed
• Ignoring fairness and safety aspects

2. Which of the following is the correct way to describe bias in AI?

easy

A. A feature that makes AI faster

B. Unfairness that sneaks into AI from data or design

C. A tool to improve AI accuracy

D. A method to reduce AI errors

Solution

1. Step 1: Define bias in AI context

   Bias means unfairness that can come from the data or how AI is designed.

2. Step 2: Match options with bias definition

   Unfairness that sneaks into AI from data or design correctly describes bias as unfairness sneaking into AI.

3. Final Answer:

   Unfairness that sneaks into AI from data or design -> Option B

4. Quick Check:

   Bias = Unfairness in AI [OK]

Hint: Bias means unfairness in AI data or design [OK]

Common Mistakes:

• Thinking bias improves AI
• Confusing bias with speed or error reduction
• Ignoring the unfairness aspect

3. Consider this simple AI decision flow:

Input: Age
If Age < 18 then Output = "Minor"
Else Output = "Adult"

What is the output if the input age is 20?

medium

A. "Minor"

B. "Unknown"

C. "Error"

D. "Adult"

Solution

1. Step 1: Check the input value against the condition

   Input age is 20, which is not less than 18.

2. Step 2: Follow the decision flow for age 20

   Since 20 ≥ 18, output is "Adult".

3. Final Answer:

   "Adult" -> Option D

4. Quick Check:

   Age 20 ≥ 18 -> Output = "Adult" [OK]

Hint: Check if age is less than 18 or not [OK]

Common Mistakes:

• Choosing "Minor" for age 20
• Assuming error without reason
• Ignoring the else condition

4. An AI system shows bias by unfairly favoring one group. Which fix helps reduce this bias?

medium

A. Use more diverse and balanced data for training

B. Make the AI run faster

C. Add more layers to the AI model

D. Ignore the bias since it is small

Solution

1. Step 1: Identify cause of bias

   Bias often comes from unbalanced or non-diverse training data.

2. Step 2: Choose the fix that addresses data bias

   Using diverse and balanced data helps AI treat all groups fairly.

3. Final Answer:

   Use more diverse and balanced data for training -> Option A

4. Quick Check:

   Diverse data reduces bias [OK]

Hint: Fix bias by improving training data diversity [OK]

Common Mistakes:

• Thinking speed or model size fixes bias
• Ignoring bias problems
• Assuming bias is acceptable if small

5. A company wants to build an AI hiring tool that avoids bias. Which approach best ensures fairness?

hard

A. Use AI to select candidates faster without checks

B. Train AI only on resumes from one city

C. Regularly test AI decisions for fairness and update data

D. Exclude all personal information from resumes

Solution

1. Step 1: Understand fairness in AI hiring

   Fair AI must be tested regularly to catch bias and updated with good data.

2. Step 2: Evaluate options for fairness

   Regularly test AI decisions for fairness and update data includes testing and updating, which helps maintain fairness over time.

3. Step 3: Consider why other options fail

   Train AI only on resumes from one city limits data diversity; C ignores fairness; D may help but alone is not enough.

4. Final Answer:

   Regularly test AI decisions for fairness and update data -> Option C

5. Quick Check:

   Testing and updating ensures fairness [OK]

Hint: Fair AI needs ongoing testing and data updates [OK]

Common Mistakes:

• Using limited data causing bias
• Ignoring fairness checks
• Assuming removing info alone fixes bias