Module 3 -> Lesson 3

Close reading, collaborative discussion, writing to learn

Essential Question:

How can I craft a technical claim?

Anchor Text(s) for this Lesson

Supporting Text(s)/ Resources for this Lesson

Lesson Overview

In this lesson, students continue building their skills to make technical claims by looking more deeply at facial recognition technologies. Specifically, students are exposed to multiple potential applications of the technology and are tasked to work with a partner to generate as many examples of use cases for each application type as possible. Students then choose two of their listed use cases: one that they think presents the most risks and the other that they anticipate has the most potential to help humans and to explain in writing why they have made these choices. Finally, students closely read a diagram that explains how facial recognition technology works and they determine which application type is being described.

Nota Bene

In this lesson, students are exposed to excerpts from the two anchor texts listed above. These excerpts are already included in the student activity linked above. The student activity allows for students to adjust the complexity level on their own BUT if you have students who are looking for more, consider the following options for enrichment:

  • Read the report "How does facial recognition work?" and compare that report's treatment of various application types to those presented in the poster.

  • Create a poster that attempts to depict the differences between two or more of the application types mentioned.

  • Develop a new use case for any of the application types OR develop an entirely new category.


Students will be able to...

  • Explain the difference between different application of facial recognition technologies.

  • Identify benefits and concerns associated with different facial recognition technologies.

  • Formulate a technical claim regarding the use of facial recognition technologies.

Suggested Duration

45 minutes (adjust according to your students' needs)

NYS Next Generation ELA Standards

  • RST7: Translate information expressed visually or mathematically into words.

  • RH9: Compare and contrast treatments of the same topic in several primary and secondary sources.

  • W1:Write arguments to support claims that analyze substantive topics or texts, using valid reasoning and relevant and sufficient evidence.

NYS Computer Science & Digital Fluency Standards

  • 9-12.DL.2: Communicate and work collaboratively with others using digital tools to support individual learning and contribute to the learning of others.

  • 9-12.IC.5 Describe ways that complex computer systems can be designed for inclusivity and to mitigate unintended consequences.

  • 9-12.IC.3 Debate issues of ethics related to real world computing technologies.

  • 9-12.IC.1 Evaluate the impact of computing technologies on equity, access, and influence in a global society.


  • machine learning: a type of artificial intelligence in which computers use huge amounts of data to learn how to do tasks rather than being programmed to do them

  • artificial intelligence: the study and development of computer systems that can copy intelligent human behavior

  • detect: to discover or notice something, especially something that is not easy to see, hear, etc.

  • technical: connected with the practical use of machinery, methods, etc. in science and industry

  • technical claim: use technical information to make a statement about something that you will argue is true

  • facial detection: technology that allows a computer to 'see' whether a face is in its view

  • facial recognition: technology that allows a computer to identify a person by their face

  • algorithm: a set of rules that must be followed when solving a particular problem, such as “find the face(s) in this setting”


Present a slide with an image of a popular Snapchat filter (check slide deck). Ask students: Have you ever used filters on Snapchat, your phone, Instagram …?Would you say that face recognition technology helps to power that particular feature? Explain your reasoning.


Remind students that a computer is very unlikely to ‘see’ something that it has NOT encountered multiple times in the data set provided by the humans training that computer. How might a limited data set impact algorithms that are trained to support facial detection and facial recognition technologies?

Briefly review ideas from the previous lessons:

We learned from Dr. Buolamwini’s TED Talk that she discovered that FRT failed to detect her face because she has dark skin. She understood that this was because the training data–the many pictures that were used to train the algorithm–did not include as many pictures of black and brown people as it did of white men.

In the Ovide article we read in Module 1 with thought about the question: What problems might arise if images of celebrities are most often used to train computers how to recognize faces?

What problems might arise if algorithms are trained to detect faces using images that are mostly of white men?

Let students know that in this module, we are working to understand enough about the technology to understand how it is applied, its benefits and risks, and its technical limitations, so we can make technical claims that we support with valid evidence and sound reasoning.

We know that data plays a very important role in training algorithms to ‘see.’ It is also important to understand that FRT is not just ONE type of application. There are several applications, each with their own use cases.

Present students with the following chart and elicit examples of potential use cases for each category.

After presenting the chart and eliciting potential use cases, replay the video from Module 1 Lesson 1 with the following purpose for viewing: What use cases do you hear or see mentioned in this video? What category would you put them in?


Students work in pairs to complete the activity guide, which includes the following tasks:

  • Task 1: Work with a partner to generate as many examples of possible use cases for the different applications of facial recognition technologies outlined in the graphic below. (Same as graphic shown above)

  • Task 2: Of all the use cases that you and partner listed, which one do you think has the most potential to cause harm? Which has the most potential to benefit people? Explain your reasoning below.

  • Task 3: Closely read the following diagram. And answer the following questions in the box below: What type of facial recognition application is being described in this diagram? How do you know? (diagram included in activity guide)

Wrap Up

Ask students: What use case did you identify as most likely to present risks? How about the most beneficial? Which type of FRT is being described by the diagram. Explain your reasoning. Where did you get stuck? What questions do you have? Give students a preview of what will happen in the next class.

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