Resource type
Use type
Tools
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Standards & Guidelines
Lesson Overview
This lesson introduces students to how supervised machine learning can be trained to classify complex datasets based on labeled data. Students will train their teachable machine models and learn that AI can learn from labeled data. They will also revisit the idea that training AI systems with an increasing amount of data does not necessarily mitigate bias if there’s not enough diversity in the data.
Total Lesson Time: 3 45 minute lessons
Learning Objectives: Students will be able to . . .
- Describe a prediction made by an AI technology
- Give an example of algorithmic bias from everyday life
- Explain why a) certain items in a dataset, b) the size of the dataset, and c) the diversity of items in the dataset might introduce algorithmic bias
- Justify a method of re-curating a dataset to solve a algorithmic bias problem
Vocabulary Introduced: training data, test data, algorithmic bias,
Pacing:
Day 1
- Opening (5 min)
- Introduction to new material (including Prediction Matching Activity), terms, (20 min)
- Thumbs-Up/Thumbs-Down Activity (15 min)
- Closing (5 min)
Day 2
- Opening (5 min)
- Introduction to new material, terms, guided practice (20 min)
- Cat-Dog Classifier Activity (15 min)
- Closing (5 min)
Day 3
- Opening (5 min)
- Introduction to new material, terms, guided practice (20 min)
- Re-Curate a Data Set Activity (15 min)
- Closing (5 min)
Planning Guide
Preparation Needed: 10-30 minutes each lesson
Prep Needed for Teaching In-Person:
- Print the worksheet and exit ticket each day
- Print the Prediction Matching (cut out and sort) materials
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Teacher Modifications
One of the amazing things about this curriculum is how much teachers have been involved in modifying it make it more fun, engaging, and inclusive for their students.
Click on Teacher Modifications in the Navigation menu to see what teachers of made!
Activity Usage
Copyright held by MIT STEP Lab
License: CC-BY-NC under Creative Commons
These materials are licensed as CC-BY-NC 4.0 International under creative commons. (For more information visit https://creativecommons.org/licenses/by-nc/4.0/). This license allows you to remix, tweak, and build upon these materials non-commercially as long as you include acknowledgement to the creators. Derivative works should include acknowledgement but do not have to be licensed as CC-BY-NC. People interested in using this work for for-profit commercial purposes should reach out to Irene Lee at [email protected] for information as to how to proceed. Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Attribution:
The original “Cat and Dog predictor” was created as part of the “An Ethics of Artificial Intelligence Curriculum for Middle School Students” by Blakeley H. Payne.
Teachable Machine is an AI Experiment from Google.
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