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.