Bias in AI and real-world consequences in AI for Everyone - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the sentence to explain what bias in AI means.

AI for Everyone

Bias in AI occurs when the system [1] certain groups unfairly.

Drag options to blanks, or click blank then click option'

Afavors

Bignores

Ctreats

Davoids

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'treats' because it sounds neutral, but bias is about unfair favoring.

Bias in AI means the system favors or disadvantages certain groups unfairly.

2fill in blank

medium

Complete the sentence to identify a common source of bias in AI.

AI for Everyone

One common source of bias in AI is [1] data that reflects past inequalities.

Drag options to blanks, or click blank then click option'

Aclean

Bbiased

Crandom

Dbalanced

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'clean' or 'balanced' which mean the opposite of biased.

Biased data contains unfair patterns from the past, causing AI to learn those biases.

3fill in blank

hard

Fix the error in the statement about AI bias consequences.

AI for Everyone

Bias in AI can lead to [1] decisions that affect people's lives unfairly.

Drag options to blanks, or click blank then click option'

Aneutral

Baccurate

Crandom

Dunfair

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'random' thinking bias means randomness.

Bias causes unfair decisions, not random or neutral ones.

4fill in blank

hard

Fill both blanks to describe how bias can enter AI systems.

AI for Everyone

Bias can enter AI through [1] data and [2] design choices.

Drag options to blanks, or click blank then click option'

Abiased

Brandom

Chuman

Dneutral

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'random' or 'neutral' which do not cause bias.

Bias enters AI from biased data and human design decisions.

5fill in blank

hard

Fill all three blanks to explain how to reduce bias in AI.

AI for Everyone

To reduce bias, AI developers should use [1] data, apply [2] testing, and involve [3] perspectives.

Drag options to blanks, or click blank then click option'

Adiverse

Bregular

Cmultiple

Dlimited

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'limited' which reduces variety and increases bias.

Using diverse data, regular testing, and multiple perspectives helps reduce bias.

Practice

1. What does bias in AI mean?

easy

A. Unfair treatment of some groups by AI systems

B. AI working faster than humans

C. AI making no mistakes

D. AI being used only for games

Solution

1. Step 1: Understand the meaning of bias

   Bias means unfair preference or prejudice towards some groups.

2. Step 2: Connect bias to AI systems

   Bias in AI means AI treats some groups unfairly, not equally.

3. Final Answer:

   Unfair treatment of some groups by AI systems -> Option A

4. Quick Check:

   Bias in AI = Unfair treatment [OK]

Hint: Bias means unfairness in AI decisions [OK]

Common Mistakes:

• Thinking bias means AI is always wrong
• Confusing bias with AI speed
• Believing bias means AI never makes mistakes

2. Which of the following is a common cause of bias in AI?

easy

A. Using biased or incomplete data

B. Writing code in Python

C. Running AI on fast computers

D. Using balanced and fair data

Solution

1. Step 1: Identify causes of bias

   Bias often comes from data that is not fair or complete.

2. Step 2: Evaluate options

   Only Using biased or incomplete data mentions biased or incomplete data causing bias.

3. Final Answer:

   Using biased or incomplete data -> Option A

4. Quick Check:

   Biased data causes AI bias [OK]

Hint: Bias comes from bad or unfair data [OK]

Common Mistakes:

• Thinking fast computers cause bias
• Believing programming language causes bias
• Assuming balanced data causes bias

3. An AI system used for hiring mostly selects candidates from one gender. What is the likely cause?

medium

A. The AI was programmed in a new language

B. The AI is too slow to process applications

C. The AI uses random selection

D. The AI was trained on biased data favoring that gender

Solution

1. Step 1: Analyze the hiring bias problem

   The AI favors one gender, showing unfair treatment.

2. Step 2: Identify cause of bias

   This usually happens if training data had bias favoring that gender.

3. Final Answer:

   The AI was trained on biased data favoring that gender -> Option D

4. Quick Check:

   Biased training data causes biased hiring AI [OK]

Hint: Bias in training data leads to biased AI results [OK]

Common Mistakes:

• Thinking AI speed affects bias
• Assuming random selection causes bias
• Believing programming language causes bias

4. An AI loan approval system rejects many applicants from a certain ethnic group unfairly. What is a likely error to fix?

medium

A. Make the AI run faster

B. Change the AI programming language

C. Remove biased data from training and retrain the AI

D. Add more applicants from other groups

Solution

1. Step 1: Identify the unfair rejection problem

   The AI unfairly rejects a group, showing bias.

2. Step 2: Find the fix for bias

   Fixing bias means removing biased data and retraining AI fairly.

3. Final Answer:

   Remove biased data from training and retrain the AI -> Option C

4. Quick Check:

   Fix bias by cleaning data and retraining AI [OK]

Hint: Fix bias by cleaning data, not by speed or language [OK]

Common Mistakes:

• Thinking speed or language fixes bias
• Adding more data without checking bias
• Ignoring biased training data

5. A company wants to use AI to screen job applicants fairly. Which approach best reduces bias?

hard

A. Make AI decisions secret so no one questions them

B. Use diverse and balanced training data and regularly check AI decisions

C. Use only data from one city to keep it simple

D. Train AI quickly without reviewing data quality

Solution

1. Step 1: Understand how to reduce AI bias

   Reducing bias needs diverse, balanced data and ongoing checks.

2. Step 2: Evaluate options for fairness

   Only Use diverse and balanced training data and regularly check AI decisions includes balanced data and regular checks to ensure fairness.

3. Final Answer:

   Use diverse and balanced training data and regularly check AI decisions -> Option B

4. Quick Check:

   Diverse data + checks = less AI bias [OK]

Hint: Balanced data and checks reduce AI bias best [OK]

Common Mistakes:

• Using limited data thinking it's simpler
• Hiding AI decisions instead of checking
• Ignoring data quality and speed training