Bias in AI and real-world consequences in AI for Everyone - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is bias in AI?

Bias in AI means the system makes unfair or prejudiced decisions because of the data or design it learned from.

beginner

How can biased data affect AI outcomes?

If the data AI learns from is unfair or incomplete, the AI will make unfair decisions that can harm certain groups of people.

intermediate

Give an example of a real-world consequence of AI bias.

An AI hiring tool might unfairly reject qualified candidates from certain backgrounds if it learned from biased past hiring data.

beginner

Why is it important to detect and reduce bias in AI?

Reducing bias helps AI make fairer decisions, protects people's rights, and builds trust in technology.

intermediate

What role do humans play in preventing AI bias?

Humans design AI, choose data, and test results, so they must carefully check for bias and fix it.

What causes bias in AI systems?

A AI working too fast

B Biased or incomplete training data

C Using too much data

D AI being open source

Bias usually comes from the data AI learns from, especially if it is unfair or incomplete.

Which is a possible effect of AI bias in healthcare?

A Fair treatment for all patients

B Faster diagnosis for everyone

C Ignoring symptoms in certain groups

D More doctors hired

Biased AI might miss or wrongly assess symptoms in some groups, leading to unfair care.

How can AI bias affect job hiring?

A It can reject candidates unfairly based on past biased data

B It can help find the best candidates fairly

C It always improves diversity

D It replaces human interviewers

If AI learns from biased hiring data, it may unfairly reject qualified candidates from certain groups.

What is one way to reduce bias in AI?

A Use only one data source

B Ignore data quality

C Avoid testing AI results

D Use diverse and balanced data

Using diverse and balanced data helps AI learn fairly and reduce bias.

Who is responsible for checking AI bias?

A The designers and developers

B The users only

C Only the AI itself

D No one

Humans who create and manage AI must check and fix bias to ensure fairness.

Explain what bias in AI means and why it can cause problems in real life.

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

Describe ways to identify and reduce bias in AI systems.

Consider the role of data and human oversight.

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