Overview
Bias and fairness in machine learning are crucial topics that help ensure technology treats everyone fairly. Bias can occur when data used to train models reflects unfair stereotypes or inequalities. Understanding these concepts helps us create better algorithms that promote fairness and reduce discrimination. In this introduction, students will learn about different types of bias and why fairness is essential in AI applications.
📚 Key Learning Objectives
- ✓ Define bias and fairness in machine learning.
- ✓ Identify examples of bias in datasets.
- ✓ Explain the impact of bias on AI outcomes.
- ✓ Discuss methods to mitigate bias in models.
- ✓ Evaluate fairness in machine learning applications.
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