Multiple Choice Questions on Bias in Algorithms for High School Students

📚 Quick Study Guide

• 🤖 Algorithmic Bias: Occurs when an algorithm produces unfair or discriminatory results due to biased input data or flawed design.
• 📊 Data Bias: Skewed or unrepresentative data used to train the algorithm. This can perpetuate existing societal biases.
• ⚙️ Selection Bias: Occurs when the data used for training doesn't accurately reflect the population the algorithm will be used on.
• 📏 Measurement Bias: Arises from inaccuracies or inconsistencies in how data is collected and labeled.
• 🧪 Evaluation Bias: Occurs when evaluating an algorithm's performance using biased metrics or datasets.
• ⚖️ Fairness Metrics: Tools to assess and mitigate bias, such as equal opportunity, demographic parity, and predictive equality.
• 💡 Mitigation Techniques: Methods to reduce bias, including data preprocessing, algorithm modification, and post-processing of results.

Practice Quiz

1. Which of the following is the MOST accurate definition of algorithmic bias?
   1. A) An algorithm that runs very quickly.
   2. B) An algorithm that always produces correct results.
   3. C) An algorithm that produces unfair or discriminatory results.
   4. D) An algorithm with no input data.
2. What is data bias primarily caused by?
   1. A) Perfectly balanced datasets.
   2. B) Skewed or unrepresentative data.
   3. C) Algorithms that are too simple.
   4. D) Algorithms designed to correct bias automatically.
3. Selection bias occurs when:
   1. A) The algorithm selects the best possible outcome.
   2. B) The data used for training doesn't reflect the target population.
   3. C) The algorithm is used for a different purpose than intended.
   4. D) The algorithm is trained on a small, random sample.
4. Measurement bias arises from:
   1. A) Accurate and consistent data collection.
   2. B) Inaccuracies in how data is collected and labeled.
   3. C) Using only numerical data.
   4. D) Using only categorical data.
5. Which of the following is an example of a fairness metric?
   1. A) Processing speed.
   2. B) Code complexity.
   3. C) Equal opportunity.
   4. D) Memory usage.
6. What is a mitigation technique used to reduce bias in algorithms?
   1. A) Ignoring the input data.
   2. B) Data preprocessing.
   3. C) Always using the same algorithm.
   4. D) Increasing the amount of bias.
7. Evaluation bias occurs when:
   1. A) The algorithm is evaluated using unbiased metrics.
   2. B) The algorithm is evaluated using biased metrics.
   3. C) The algorithm is never evaluated.
   4. D) The evaluation process is very quick.