Module 3 -> Lesson 4
Close reading, collaborative discussion, learn through writing
Anchor Text(s) for this Lesson
POSTER Understanding Facial Detection, Characterization, and Recognition Technologies by Future of Privacy Forum, March 2018
Street-Level Surveillance by Electronic Frontier Foundation, October 24, 2017
Abbreviated version of Street-Level Surveillance with language and comprehension scaffolds
Student produced Zine or brochure still in progress
Supporting Text(s)/ Resources for this Lesson
Sample slide deck: Module 3 --> Lesson 4
How does facial recognition work? Report by James Andrew Lewis and William Crumpler, CSIS, June 10, 2021
Executive summary of the same text with language and comprehension scaffolds
Lesson Overview
In this lesson, students build on their work in Lesson 3 by considering the full text of both the poster and the article Street-Level Surveillance. Students deepening their consideration of the various use cases of FRT by taking into further consideration the benefits and any privacy concerns. Students use their learning to formulate additional technical claims with reasoning and evidence that they will add to the zine / brochure they began building in lesson 2.
Nota Bene
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 reports treatment of various application types to those presented in the poster.
Create a poster that attempts to depict the differences between to or more of the application types mentioned.
Develop a new use case for any of the application types OR develop an entirely new category.
Objectives
Discuss benefits and concerns associated with different facial recognition technologies.
Formulate a technical claim regarding the use of specific facial recognition technologies.
Suggested Duration
45 minutes (adjust according to your students' needs)
NYS Next Generation ELA Standards
W1:Write arguments to support claims that analyze substantive topics or texts, using valid reasoning and relevant and sufficient evidence.
RH9: Compare and contrast treatments of the same topic in several primary and secondary sources.
RST7: Translate information expressed visually or mathematically into words.
NYS Computer Science & Digital Fluency Standards
9-12.IC.1 Evaluate the impact of computing technologies on equity, access, and influence in a global society.
9-12.IC.3 Debate issues of ethics related to real world computing technologies.
9-12.IC.5 Describe ways that complex computer systems can be designed for inclusivity and to mitigate unintended consequences.
9-12.DL.2: Communicate and work collaboratively with others using digital tools to support individual learning and contribute to the learning of others.
Vocabulary
algorithm: a set of rules that must be followed when solving a particular problem, such as “find the face(s) in this setting”
facial recognition: technology that allows a computer to identify a person by their face
facial detection: technology that allows a computer to 'see' whether a face is in its view
technical claim: use technical information to make a statement about something that you will argue is true
technical: connected with the practical use of machinery, methods, etc. in science and industry
detect: to discover or notice something, especially something that is not easy to see, hear, etc.
artificial intelligence: the study and development of computer systems that can copy intelligent human behavior
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
Hook
Present the following blurbs to students along with the above image (see slide deck) and ask them to compare and contrast the DeepFace and FaceNet programs. Prompt students to identify which type of FRT each of these programs represents.
“In 2014, Facebook announced its DeepFace program, which can determine whether two photographed faces belong to the same person, with an accuracy rate of 97.25%. When taking the same test, humans answer correctly in 97.53% of cases, or just 0.28% better than the Facebook program.”
“In June 2015, Google went one better with FaceNet. On the widely used Labeled Faces in the Wild (LFW) dataset, FaceNet achieved a new record accuracy of 99.63% (0.9963 ± 0.0009). Using an artificial neural network and a new algorithm, the company from Mountain View has managed to link a face to its owner with almost perfect results.” (SOURCE)
Mini-Lesson
Build on the hook by modeling for students the crafting of a technical claim that supports or opposes the use of FRT. Pull from one or both of the excerpts presented in the hook as evidence and elicit reasoning from students to connect the claim and evidence.
Activity
Students build on the work they did in the last lesson but go deeper in this lesson as they closely read the entire poster and the full article (both anchor texts listed above; consider using the abbreviation version of Street-Level Surveillance with language and comprehension supports for students who need that scaffolding).
Students formulate a technical claim and pair it with evidence from one of the texts and draft reasoning that links the two. When students are satisfied with their technical argument (claim, evidence and reasoning), they will add it to the Zine / brochure they began drafting in Lesson 2.
Wrap Up
Let students know that in the next module they will be studying the uses of facial recognition technologies in New York City.
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