Lesson Overview
In this lesson students put their project on a micro:bit and allow other students to test it out. They identify how adding more data could strengthen the ML model by making it work better for more people. Optionally, students can add more data from a more diverse range of people to their ML model, re-train it, and re-test it to observe the improvement.
Subjects & Topics
- AI literacy: Human role in AI design, Testing ML models, Impact of AI
- Data literacy: Data bias
Key Learning
By the end of this lesson, students will be able to:
- Identify how to make an ML model more robust by adding more data from different people.
- Understand that ML models perform better if they have been trained on data from different groups of people.