📚 Topic Summary
Artificial Intelligence (AI) bias refers to systematic and repeatable errors in an AI system's output that lead to unfair or discriminatory outcomes. These biases often stem from the training data used to teach the AI model, which might reflect existing human prejudices, societal inequalities, or be unrepresentative of diverse populations. For high school students exploring data science with platforms like Scratch, understanding AI bias is crucial because even seemingly simple projects can inadvertently perpetuate these issues.
When you build an AI model in Scratch, for instance, if you train it with data that mostly represents one group or has hidden assumptions, the AI will learn those biases. This means its predictions or classifications could be unfair to other groups. Recognizing and addressing these biases is a fundamental skill for aspiring data scientists, ensuring they can build more equitable and reliable AI tools.
📝 Part A: Vocabulary
Match each term with its correct definition. Write the letter of the definition next to the corresponding term.
- ✍️ Terms:
- 1. AI Bias
- 2. Algorithmic Fairness
- 3. Training Data
- 4. Stereotype
- 5. Generalization
- 🧐 Definitions:
- A. The ability of an AI model to perform well on new, unseen data after being trained on a specific dataset.
- B. Systematic and repeatable errors in an AI system's output due to flawed assumptions in the machine learning process.
- C. An oversimplified and often negative generalization about a group of people.
- D. The dataset used to teach a machine learning model, influencing its patterns and predictions.
- E. The principle that AI systems should produce fair and equitable outcomes for all individuals and groups.
✏️ Part B: Fill in the Blanks
Complete the following paragraph using the words provided below. Each word should only be used once.
Words: 📉 bias, 📊 training data, ⚖️ fairness, 🎭 stereotypes, ⚙️ algorithms
AI systems can develop ____________________ if the ____________________ they learn from contains unrepresentative or prejudiced information. This can lead to unfair outcomes or reinforce existing ____________________. Ensuring ____________________ in AI development means carefully examining the data and the ____________________ themselves to prevent harmful generalizations and promote equitable results.
🤔 Part C: Critical Thinking
- 💡 Imagine you're creating a Scratch project that uses AI to recommend books based on user preferences.
- 💭 What are two specific ways AI bias could unintentionally appear in your recommendations?
- ✅ How would you try to prevent or mitigate these two types of bias in your Scratch project?