Module 2 -> Overview

Essential Question

Who has the right to use facial recognition technology and for what purposes?

Module Overview

This module assumes that students have previous knowledge of and experience with the SPAR debate protocol. This module gives students an opportunity to deepen their proficiency with SPAR debates by: challenging them to argue a different position; evaluating their claims and evidence to ensure alignment; track the distribution of evidence collected using their self-designed research tracker. Students should be encouraged to be attentive to both affirmative and negative claims and evidence as they closely read texts about FRT applications in different contexts and to lean into nuance--what might constitute a middle ground argument?

Nota Bene

Starting in Module 2, you will see a new Confirm or Challenge prompt at the top of each lesson. These are debatable prompts that can be used for SPAR debates that students argue in favor of (the affirmative) or opposition to (negative).

Depending on your students' needs, you can assign all four of the texts or allow students to select which two they will read. The texts are curated to include different perspectives, so if you opt to have students read two texts only, have them choose one from each of the following pairs: Facial Recognition is Improving Customer Experience while Enhancing Security OR Top Ten Face Recognition Apps; and Madison Square Garden Uses Facial Recognition to Ban its Owner's Enemies OR New Trends May Help TikTok Collect Your Personal, Unchangeable Biometric Identifiers. Of course, as students prepare for their first SPAR debate in this unit, they should also be making reference to the claims and evidence they took note of in module 1.

Anchor Texts for this Module

• She was Denied Entry to a Rockettes Show--Then a Facial Recognition Debate Ignited by Manuela López Restrepo on All Things Considered (AUDIO)

• Madison Square Garden Uses Facial Recognition to Ban its Owner's Enemies by Kashmir Hill and Corey Kilgannon, NY Times, December 22, 2022

  • Same text with language and comprehension scaffolds; glossary linked within document.

  • Excerpts from Madison Square Garden text with language and comprehension scaffolds AND modified instructions.

• Facial Recognition is Improving Customer Experience while Enhancing Security by CP Plus in asmag.com

Supporting Text(s)/ Resources for this Module

• Excerpts from Ovide article for barometer activity

• Evidence Tracker

• SPAR debate instructional tools

  • SPAR Debate Activity Guide

• Recommended reading for enrichment

  • Top 10 Face Recognition Apps by Tilly Kenyon, April 5, 2022, Cybermagazine.com

  • New Trends May Help TikTok Collect Your Personal, Unchangeable Biometric Identifiers by Allison Fiedler, April 14, 2022, ACLU.org

  • A Case for Facial Recognition by Shira Ovide, NYTimes, November 11, 2020

    • Same text with language and comprehension scaffolds–glossary linked within document

NYS Next Generation ELA Standards

• R1 Cite strong and thorough textual evidence to support analysis of what the text says explicitly/ implicitly and make logical inferences; develop questions for deeper understanding and for further exploration.

• RST1: Cite specific evidence to support analysis of scientific and technical texts, charts, diagrams, etc. attending to the precise details of the source. Understand and follow a detailed set of directions.

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

• L6: Acquire and accurately use general academic and content-specific words and phrases, sufficient for reading, writing, speaking, and listening; demonstrate independence in applying vocabulary knowledge when considering a word or phrase important to comprehension or expression.

• L4c: Consult general and specialized reference materials (e.g., dictionaries, glossaries, thesauruses) to find the pronunciation of a word or determine or clarify its precise meaning, its part-of-speech, or its etymology.

NYS Computer Science & Digital Fluency Standards

• 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.

• 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.DL.1: Type proficiently on a keyboard.

Vocabulary

• opponent (noun): a person who is against something and tries to change or stop it

• proponent (noun): a person who supports an idea or a course of action

• oversight (noun): the fact of making a mistake because you forget to do something or you do not notice something

• rectify (verb): to fix something that is wrong

• balancing act (noun): a process in which someone tries to please two or more people or groups who want different things

• middle ground (noun): a set of opinions, decisions, etc. that two or more groups who oppose each other can agree on; a position that is not extreme