Module 4 -> Lesson 3

Rich discussion, close reading, writing to learn

Confirm or Challenge

Police should be trusted to use facial recognition technology for the safety and betterment of all citizens.

Anchor Text(s) for this Lesson

• House Oversight Committee: EFF Senior Staff Attorney Jennifer Lynch's Testimony on Facial Recognition, video published by EFF on March 22, 2017

• New York City Council Hearing on the Use of Facial Recognition Technology, February 24, 2023, CBS New York

• New York City Police Department Surveillance Technology by by Ángel Díaz, October 4, 2019, Brennan Center for Justice (archived text)

• Activity Guide

Supporting Text(s)/ Resources for this Lesson

• New York City Council website

• Sample slide deck: Module 4 --> Lesson 3

• Recommended reading for enrichment

  • Testimony of Jennifer Lynch to the Senate Committee on the Judiciary Subcommittee on Privacy, Technology, and the Law

Lesson Overview

In this lesson, students bring together their learning about the ways in which different forms of bias can influence the reliability of FRT used in policing and their insights into the degree to which the NYPD's own guidelines for how it will use FRT in policing potentially calls for some oversight. Students learn briefly about the House Oversight Committee and view short video in which a lawyer from Electronic Frontiers Foundation provides testimony urging the committee to write legislation that regulations federal agencies use of FRT. Students then briefly learn about the role of the New York City Council in providing similar oversight. Students then then take on the role of an intern researcher who is exploring the ways in which the NYPD is using face recognition and video analytics in NYC to form arguments that they will present to the NY City Council to argue for or against regulations.

Nota Bene

Based on your students needs, either extend this lesson into a second class period to allow students more time to engage with the BONUS challenge or more on to Lesson 4, a writing intensive day that provides students with support for writing three distinct claims with reasoning and evidence pulling from all previous lessons up to this point. If you want to keep it to one 45 minute period, a natural cut-off point is for students to focus on TASK 1 only in the Activity Guide.

Objectives

Students will be able to...

• Identify claims made in video testimony presented to the House Oversight Committee

• Closely read charts to extract specific information

• Prepare claims, reasoning and evidence to present to NY City Council

Suggested Duration

90 minutes (adjust according to your students' needs)

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.

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

• W1c: Use precise language and content-specific vocabulary to express the appropriate complexity of the topic.

• 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.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.

Vocabulary

• oversight: the state of being in charge of someone or something

• guidelines: rules or instructions that are given by an official organization telling you how to do something, especially something difficult

• algorithmic bias: systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others; example: facial recognition algorithms trained with a data set that does not reflect human diversity and therefore results in different reliability based on gender and race.

• cognitive bias: a thought process caused by the tendency of the human brain to simplify information processing through a filter of personal preferences and opinions; examples: confirmation bias; automation bias; authority bias; gender bias; group attribution error

• automation bias: to trust information provided by a machine/ computer and ignore information that contradicts it; automation bias is one type of cognitive bias. example: trusting the output of FRT when it produces a “match” without verifying that match by other means.

• confirmation bias: the conscious or subconscious tendency for people to seek out information that confirms their pre-existing viewpoints, and to ignore information that goes against them (regardless of whether that information is true or false); example: only paying attention to news stories that confirm your opinion and ignoring or dismissing information that challenges your position.

• authority bias: tendency to trust / follow influence of a leader/ person in position of authority

• false positive: when the face recognition system does match a person’s face to an image in a database, but that match is actually incorrect

• false negative: when the face recognition system fails to match a person’s face to an image that is, in fact, contained in a database

Hook

Remind students that in the last two lessons they have considered the ways in which different types of bias influence the impacts of FRT when it is used in policing. They watched an oral history describing a scenario in which police used FRT to identify an individual and then took action based on the identification made by their systems. They watched another video about why the South Orange, NJ PD have chosen NOT to use FRT in policing AND they have been exposed to various examples within the slide decks and materials of use cases where FRT can be helpful.

Remind students of the guidelines the NYPD has published for their use of FRT. Present the following question to students and ask them to take a stand on the barometer to show their position:

The NYPD's guidelines for how and when to use FRT guarantee that the potential risks of the technology will be avoided.

The barometer should be arranged as it was in Module 2 but this time: Strongly agree, somewhat agree, somewhat disagree, and strongly disagree.

Allow students time to talk with a partner about why they took this particular position. Facilitate a full class conversation with students, taking care to elicit a variety of opinions and see if anyone changes their perspective after hearing from others. If you notice that students are gathering on only one side of the barometer, play devil's advocate to challenge their thinking. Students saw more examples in the last two lessons about FRT gone wrong and may gather in one direction for that reason. If you see a need to play Devil's advocate, consider this slide from Module 3:

Ask students: Whose responsibility is it to ensure that police departments, billionaires, or other entities with the resources to deploy FRT don't abuse their power? How can we take advantage of the potential benefits of FRT to help solve crimes while mitigating the risks?

Mini-Lesson

Briefly explain the role of the House Oversight Committee and provide the context that the lawyer in this video, is before this committee to make an argument for laws that will regulate the ways in which FRT is used by federal agencies like the FBI.

Present the video House Oversight Committee: EFF Senior Staff Attorney Jennifer Lynch's Testimony on Facial Recognition, with the following purpose for viewing: What claims does Jennifer Lynch make to support her argument that there must be legal protections put in place to regulate the use of FRT by the FBI and other federal agencies?

Briefly explain to students that in New York City, if people want to press for legislative change, that this is something that happens through the City Council. Present slide that outlines the New York City Council's work (the following bullets were taken from the New York City Council website):

• Introduce and vote on legislation (proposed laws) having to do with all aspects of City life.

• Negotiate the City’s budget with the Mayor and approve its adoption.

• Monitor City agencies such as the Department of Education and the NYPD to make sure they’re effectively serving New Yorkers.

• Review land use and make decisions about the growth and development of our city. (SOURCE)

Activity

Inform students that they are going to conduct research into the NYPD's current use of surveillance technology in order to prepare claims, evidence and reasoning that they can use to make a presentation to the City Council about FRT & policing in NYC.

Here's the scenario:

You have scored an opportunity of a lifetime as a research intern for the Electronic Frontiers Foundation. You are being asked to do focused research on the ways in which FRT is being used by police in the NYPD, so you can help your team make a convincing argument before the New York City Council to pass laws that regulate the use of this technology.

Review the activity guide with students and model how they will use it alongside the text. The directions are outlined in the activity guide.

Wrap Up

Based on your students needs, either extend this lesson into a second class period to allow students more time to engage with the BONUS challenge or more on to Lesson 4, a writing intensive day that provides students with support for writing three distinct claims with reasoning and evidence pulling from all previous lessons up to this point.