OVERVIEW

Activity Overview:

This is the first set of three lessons in a six-lesson mini unit on the basics of how artificial intelligence/machine learning works and the ethics involved with artificial intelligence. In this lesson set, students will learn about artificial intelligence, supervised machine learning, build their own teachable machine, and look at how datasets influence results. Students will also begin to consider ethical implications of artificial intelligence, especially in relation to datasets.

Meta description

• Subject Area: Computer Science, Technology, Advisement classes. Lessons can also be adapted to other content areas.
• Grade Level : 9-12
• Computer Science Domains:
  • Algorithms and Programming
  • Impacts of Computing
• Computer Science Principles:
  • Fostering an Inclusive Computing Culture
  • Creating Computational Artifacts
  • Communicating About Computing
• Materials:
  • Website, Works best with Google Chrome and Google Education Suite; https://teachablemachine.withgoogle.com/
• Considerations:
  • Educators will need a general understanding of computer science concepts and how machine learning works in order to answer student questions that might come up during the lesson. They should test the links to slide decks, teachable machine, and other AI tools students will use as some require a Google account. Slide decks can be converted to PowerPoint if needed.

Lesson Plan

Overview

This is the first set of three lessons in a six-lesson mini unit on the basics of how artificial intelligence/machine learning works and the ethics involved with artificial intelligence. In this lesson set, students will learn about artificial intelligence, supervised machine learning, build their own teachable machine, and look at how datasets influence results. Students will also begin to consider ethical implications of artificial intelligence, especially in relation to datasets.

ASSESSMENT PRE/POST-TEST

I did not do these correctly and instead did an assessment question at the end of each lesson.

Lesson One: Students fill out an exit ticket with three things they learned and one AI tool that they use in their daily life. Bonus if they can write what they think the dataset and prediction is for that tool.

Lesson Two: Students will fill out an exit ticket where they answer the following questions about their experience with the musical instrument model or their own classification model: What did their model classify? Was their model successful in classification? What did they learn about the type of data their model needed to perform successfully? Where was their model biased?

Lesson Three: Have students complete the exit ticket where they answer the question “What is one way an algorithm can be biased?” and then write 2-3 sentences discussing a time when they utilized technology in an ethical way.

OBJECTIVES

Lesson One:

1. Understand that artificial intelligence is the science of making machines that can think like humans and have intellectual processes similar to a human – reasoning, discovering meaning, generalizing, learning from past experiences.
2. Know that artificial intelligence is a specific type of algorithm and has three specific parts: dataset, learning algorithm, and prediction.
3. Recognize AI systems in everyday life and be able to reason about the prediction an AI system makes and the potential datasets the AI system uses.

Lesson Two:

1. Be able to define and explain supervised machine learning.
2. Know what a classification problem is and how AI models work with it.
3. Understand potential issues with classification in the supervised machine learning context.
4. Understand how the quantity of training data affects the accuracy and robustness of a supervised machine learning model.

Lesson Three:

1. Know the term “algorithmic bias” in the classification context. 1A. Understand the effect training data has on the accuracy of a machine learning system. 1B. Recognize that humans have agency in curating training datasets. 1C. Understand how the composition of training data affects the outcome of a supervised machine learning system.
2. Know the term “ethics” in the context of artificial intelligence/machine learning. 2A. Understand how bias relates to ethics in machine learning. 2B. Identify how data sets can impact the predictions of machine learning systems allowing them to make unethical predictions. 2C. Recognize why ethics in relation to AI are important. 2D. Explain the top four principles of ethical artificial intelligence (fairness, transparency, privacy, human centeredness) and why they are important.

CATCH/HOOK

Lesson One: Students complete the “AI or Not” sorting activity where they sort common items (alarm clock, Amazon Echo Dot, automatic doors, etc.) into the categories of AI or not AI.

Lesson Two: To review the data/prediction sequence, students will participate in AI Bingo where they will go around the classroom and ask fellow students which AI devices they have used. Together, they will name the dataset and prediction for the device. Once a student gets five devices in a row, they get a bingo and win the activity.

Lesson Three: Have students watch this short clip on AI, humans, and bias (https://youtu.be/AUVcF7ehZ28). Ask students to think of one time they have encountered AI bias in their life and write it down. If they can’t think of a specific example of AI bias, they can write about a time they have experienced bias themselves. Invite a few students to share their experiences.

ACTIVITY INSTRUCTIONS

Lesson One:

1. After students have completed the “AI or Not” sorting activity, have them share out as to why they put certain technologies into each category. Ask if anyone has a definition for AI that they are willing to share with the group. Using the student given definition, build upon their understanding by going over the Introduction to AI slide deck that covers algorithms, datasets, learning, and prediction.
2. Showcase the various AI tools from the Explore AI Journal slide deck. Have each student pick one they would like to check out. Allow time for students to play with their chosen AI tool, then in their groups, have them talk about what data the machine was given, how it learned, and what it predicted.
3. Have one person from each group share out about what tool they used, what the tool did, and what data/learning/prediction they think it might have used.

Lesson Two:

1. After the students have participated in AI Bingo, review the concepts of machine learning, data set, and prediction after the activity. Tell the students that today you are going to learn more about supervised machine learning and go over the Introduction to Supervised Machine Learning slide deck through slide 23. Be sure to use version 1 of the Teachable Machine for the student activity (https://teachablemachine.withgoogle.com/v1). Debrief with students utilizing the questions from MIT AI Ethics Education Curriculum (linked above).
2. Let students know that in order to experience how a machine learns to classify, they are going to work with a partner to build a model that will classify dogs and cats. Work through the Dog/Cat classifier section of the MIT AI Ethics Education Curriculum, making sure to ask the questions dealing with AI bias in data.
3. Depending on the ability level of the students and the time available, do either the Musical Instrument Classification activity from the MIT curriculum or the Teachable Machine Project from the DAILy Workshop.

Lesson Three:

1. Remind students that last time you met, the group worked on building classification models that sorted a piece of data into one group or the other. You also talked a little bit about how AI models can be biased. Today you are going to take a deeper look at bias in machine learning. Go through the slide deck for Investigating Bias through slide 15.
2. Next, tell students that the class is going to look at different real-world examples of bias and complete the Exploring AI Bias activity utilizing slides 16 – 35 and handouts 1-4.
3. Tell students that you are going to build on their understanding of bias in machine learning by looking at the concept of ethics. Ask the student to define the word ethical, then build on their decision and give examples of situations where an ethical choice was made versus a choice that was not ethical. How do the real-world examples looked at on Google align with ethics? Discuss the top four ethical principles of artificial intelligence (from AI Club link above). How did students see those reflected in the Google search results? Where else have they encountered these principles?

Supplements

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REVIEW

Lesson One: Review the definition of AI, what an algorithm is and why it is important, and the data, learning, prediction cycle.

Lesson Two: Review the definition of a classification problem, how a machine learning model can solve it, and touch on bias in data sets.

Lesson Three: Review the terms algorithmic bias and ethics. Review how AI models can be biased and how that can render models that are not ethical.

STANDARDS