Examples of Algorithmic Bias in Websites (Grade 7)

📚 Quick Study Guide

• 🤖 Algorithmic bias happens when computer systems make unfair decisions.
• 🌐 Websites use algorithms to show you content, ads, and search results.
• 📰 Bias can appear if the data used to train the algorithm isn't diverse or accurate.
• 👤 This can lead to some groups being unfairly represented or excluded.
• 🕵️‍♀️ It's important to recognize and address algorithmic bias to ensure fairness online.

🧪 Practice Quiz

1. Which of the following is the BEST definition of algorithmic bias?
   1. A) A feature that makes websites load faster.
   2. B) A system where computers make unfair decisions.
   3. C) A way to organize data on a website.
   4. D) A type of computer virus.
2. If an algorithm is trained mostly on data from one group of people, what might happen?
   1. A) It will work perfectly for everyone.
   2. B) It might not work well for other groups.
   3. C) It will automatically correct any errors.
   4. D) It will become immune to viruses.
3. A search engine shows mostly results about male CEOs when you search for "CEO." What type of bias might this indicate?
   1. A) Gender bias.
   2. B) Age bias.
   3. C) Geographic bias.
   4. D) No bias at all.
4. Why is it important to address algorithmic bias?
   1. A) To make websites more colorful.
   2. B) To ensure fairness and equal opportunity.
   3. C) To make websites load faster.
   4. D) To confuse hackers.
5. Which of these is an example of algorithmic bias in advertising?
   1. A) Showing everyone the same ads.
   2. B) Showing ads for expensive items only to wealthy zip codes.
   3. C) Showing ads randomly.
   4. D) Not showing any ads at all.
6. What is one way to help reduce algorithmic bias?
   1. A) Using only data from one source.
   2. B) Using diverse and representative data.
   3. C) Ignoring the problem.
   4. D) Making the algorithm more complicated.
7. Imagine a website that automatically rejects job applications from people with certain names. What kind of bias is this?
   1. A) Random chance.
   2. B) Name bias.
   3. C) Skill bias.
   4. D) Education bias.