Ethics and bias in AI in Intro to Computing - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Bias in AI Systems

Which of the following best describes bias in an AI system?

A AI making decisions based on incomplete or unfair data leading to unfair outcomes

B AI always producing random results regardless of input data

C AI systems that never make mistakes or errors

D AI that only works with numerical data and ignores text

💡 Hint

Think about how data quality affects AI decisions.

Bias in AI happens when the data used to train the system is incomplete or unfair, causing the AI to make unfair or incorrect decisions.

❓ trace

intermediate

Tracing Bias Impact on AI Decision

Consider an AI system trained to approve loan applications. It was trained mostly on data from one city. What is the likely result when it evaluates applications from a different city?

A It will approve all applications regardless of data

B It may unfairly reject applications from the new city due to unfamiliar data patterns

C It will reject all applications from the original city

D It will randomly approve or reject applications without any pattern

💡 Hint

Think about how training data affects AI predictions on new data.

If the AI only learned from one city's data, it might not understand the different patterns from another city, causing unfair rejections.

❓ identification

advanced

Identifying Ethical Concerns in AI Use

Which of the following scenarios raises an ethical concern about AI use?

A An AI system that recommends job candidates but only uses gender and age as criteria

B An AI that helps doctors by analyzing medical images to detect diseases

C An AI that translates languages instantly for travelers

D An AI that suggests recipes based on available ingredients

💡 Hint

Consider fairness and discrimination in AI decisions.

Using gender and age alone to recommend job candidates can lead to unfair discrimination, which is an ethical issue.

❓ Comparison

advanced

Comparing Data Sets for Fair AI Training

Which data set is best suited to train an AI system to fairly recognize faces of all people?

A A data set with images of animals instead of people

B A data set with images mostly of one ethnicity and age group

C A data set with images of people from many different ethnicities, ages, and genders

D A data set with only cartoon faces

💡 Hint

Think about diversity and representation in training data.

Diverse data helps AI learn to recognize all types of faces fairly, reducing bias.

🚀 Application

expert

Applying Ethical Principles to AI Design

You are designing an AI system for loan approvals. Which approach best reduces bias and promotes fairness?

A Train AI only on past approved loans without checking for fairness

B Use only data from one region and automate all decisions without human checks

C Ignore data quality and focus on speed of decision-making

D Use diverse training data, regularly test for bias, and allow human review of decisions

💡 Hint

Consider how to ensure fairness and accountability in AI.

Using diverse data, testing for bias, and including human review helps create fair and ethical AI systems.

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