Fooled - Teaching AI bias with machine learning

In this lesson, students will explore the impact of bias in AI models. They will understand how bias can arise from cognitive biases or a lack of diverse data. Through hands-on activities and discussions, students will learn to identify and mitigate biases in AI systems.

Students will engage with Google’s Teachable Machine to create and test AI models, gaining practical experience with biased datasets. They will experiment with image classification tasks, starting with unbalanced datasets and working towards improving their models by incorporating more diverse examples.

By the end of the lesson, students will be able to share their improved models, explain the changes made to reduce bias, and discuss the real-world implications of biased AI. The assessment will focus on their participation, accuracy, and ability to demonstrate understanding and application of concepts related to bias in AI.