Thumbs-Up/Thumbs-Down

This activity introduces Teachable Machines and has students follow the "Teachable Machines Tutorial" to create training data sets and test the algorithm using the camera on the computer.

AI Investigation Activity

After a review of concepts of bias and classification systems, students look at examples of AI and identify bias in them, including:
Google image search results for “physicist”.
Google translate “she is a doctor, he is a nurse” from English to Hungarian and back to English.
Explore QuickDraw’s database of faces.
Google image search results for “outdoor recreation”

3.5 Redesign Youtube Lesson

In this lesson, students will redesign the YouTube recommendation algorithm to meet their needs and reduce bias. This is a culminating project that can span several days of work and spark student reflection on lessons learned from the curriculum.

2.3 AI Generated Art Lesson

In this lesson, students will explore various forms of AI-generated art. They will engage in a conversation about what is art and who can make art.

2.1 What are GANs? Lesson

In this lesson, students will learn that GANs can generate art such as photographs, paintings, handwritten poetry, music, and jokes (that are kind of funny! Maybe.)

1.3 Classifying AI vs. Generating AI Lesson

In this lesson, students experience the process of generation and classification as they mix colors on an online platform and observe that they can create a varied palette of colors with a few input colors. Students will learn about examples of AI systems that perform classification and generation and practice distinguishing between generating and classifying AIs.

1.1 Introduction to Supervised Machine Learning Lesson

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.

0.5 Investigating Bias Lesson

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.

AI or Not? Activity

A sorting activity in which students sort pictures of contemporary technology into two groups: technology with AI and technology without AI. The activity is designed to get students thinking about the three parts of ML (dataset, learning algorithm, prediction) as a tool for determining whether the technology in their everyday life uses AI or not.

An example resource