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Standards & Guidelines
Lesson Overview
This lesson shows students that unfairly trained AI systems can be far from objective and neutral. Students will recognize that AI systems can be unfairly trained and that there are several strategies that AI designers can use to mitigate biases in AIs.
Total Lesson Time: 45 min
Learning Objectives: Students will be able to . . .
- Describe what it means to be fair
- Identify and describe bias from a given classification example
Vocabulary: We recommend making the meaning of these terms clear before this lesson (review from lesson 0.4, Decision Trees): classify, algorithmic bias
Pacing:
- Opening (5 min)
- Introduction to new material, terms, guided practice (20 min)
- AI Investigation Activity (10 min)
- Wrap-up + Exit Ticket (10 min)
Planning Guide
Preparation Needed: 15-20 min
Materials:
- AI Investigation in Person Worksheet
Prep Needed for Teaching In-Person:
- Print the AI Investigation in Person Worksheet
- Try it out! Try each activity on your own computer
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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.
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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 activity “Bias Investigation” in the Investigating Bias Lesson was created in 2018 for the CSTA workshop “Investigating Fairness in Machine Learning” by Irene Lee and Fred Martin.
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