Understanding AI bias in responses in AI for Everyone - Practice Questions & Exercises

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is AI bias?

Which of the following best describes AI bias?

A AI systems making decisions based on incomplete or unfair data.

B AI always giving correct answers without errors.

C AI systems that never change their behavior over time.

D AI that only works with numbers and not text.

💡 Hint

Think about how data affects AI decisions.

AI bias happens when the data used to train AI is incomplete or unfair, causing the AI to make biased decisions.

📋 Factual

intermediate

Common sources of AI bias

Which of these is a common source of bias in AI systems?

A Training AI only on data from one group or region.

B Using diverse and balanced data sets.

C Regularly updating AI with new data.

D Testing AI on many different tasks.

💡 Hint

Consider what happens if AI learns from limited or narrow data.

Training AI only on data from one group or region can cause bias because the AI does not learn about other groups.

🚀 Application

advanced

Identifying biased AI output

An AI chatbot gives different answers to similar questions depending on the user's name. What does this indicate?

A The AI is unbiased and fair.

B The AI has a bug unrelated to bias.

C The AI is learning correctly from all users.

D The AI may be showing bias based on user identity.

💡 Hint

Think about how user identity might affect AI responses.

If AI changes answers based on user identity like names, it may be biased toward or against certain groups.

🔍 Analysis

advanced

Effects of biased training data

What is a likely effect of training an AI system on biased data?

A The AI will perform equally well for all groups.

B The AI may produce unfair or inaccurate results for some groups.

C The AI will automatically correct the bias without help.

D The AI will ignore the biased data during training.

💡 Hint

Consider how training data influences AI behavior.

Biased training data causes AI to learn unfair patterns, leading to inaccurate or unfair results for some groups.

❓ Reasoning

expert

Mitigating AI bias

Which approach is most effective to reduce AI bias?

A Ignoring bias since AI is neutral by design.

B Using only data from one trusted source.

C Including diverse data and regularly testing AI for fairness.

D Allowing AI to learn without any human oversight.

💡 Hint

Think about how to make AI fair and balanced.

Using diverse data and testing AI regularly helps find and fix bias, making AI fairer.