Multiple Choice Questions on Algorithmic Bias for AP Computer Science Principles

📚 Quick Study Guide

• ⚖️ Algorithmic bias refers to systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.
• 📊 Bias can arise from various sources including biased training data, flawed algorithm design, or societal biases reflected in the data.
• 💻 Algorithms are created by humans, and therefore, can unintentionally reflect the biases of their creators.
• 🌐 Data used to train algorithms often reflects existing societal inequalities, leading to biased outputs.
• 🛡️ Mitigation strategies include using diverse datasets, auditing algorithms for fairness, and implementing fairness-aware algorithms.
• 🔍 Evaluating fairness involves considering different metrics such as equal opportunity, demographic parity, and predictive parity.
• 💡 Understanding the social context of algorithms is crucial for identifying and addressing potential biases.

Practice Quiz

1. Which of the following is the BEST definition of algorithmic bias?
   1. A. Random errors in a computer program.
   2. B. Systematic errors in a computer system that create unfair outcomes.
   3. C. Intentional discrimination programmed into an algorithm.
   4. D. Errors caused by hardware malfunctions.
2. Which of the following is a common source of algorithmic bias?
   1. A. Perfectly balanced training data.
   2. B. Flawless algorithm design.
   3. C. Biased training data.
   4. D. Algorithms written in machine code.
3. Who is primarily responsible for the biases that appear in algorithms?
   1. A. The end-users of the algorithm.
   2. B. The programmers and designers of the algorithm.
   3. C. The hardware manufacturers.
   4. D. The internet service providers.
4. Why does data used to train algorithms often lead to biased outputs?
   1. A. Because all data is inherently biased.
   2. B. Because data is always collected anonymously.
   3. C. Because data often reflects existing societal inequalities.
   4. D. Because data is always perfectly representative.
5. Which of the following is a strategy for mitigating algorithmic bias?
   1. A. Using smaller datasets.
   2. B. Using diverse datasets.
   3. C. Ignoring the social context of the algorithm.
   4. D. Removing all human input from the algorithm.
6. Which of the following is a metric used to evaluate fairness in algorithms?
   1. A. Processing speed.
   2. B. Memory usage.
   3. C. Equal opportunity.
   4. D. Code complexity.
7. What is a crucial aspect of understanding algorithmic bias?
   1. A. Understanding the programming language used.
   2. B. Understanding the social context of algorithms.
   3. C. Understanding the hardware requirements.
   4. D. Understanding the algorithm's licensing terms.