Module 3 -> Overview

EQ: How do computers see? What is algorithmic bias?

Module Overview

Up to this point, students' arguments have likely been focused on the impacts of FRT as it is applied due to the resources they have been provided. In this module, students are presented with texts that explore how FRT works, how/when/why it doesn't work properly. We begin by understanding, in simple terms, what an algorithm is and then move on to how machines learn. Students learn about algorithmic bias and Joy Buolamwini's Incoding movement and, choosing from a menu of options, develop an artifact that urges their community to learn about FRT. Students who are motivated to dive deeper are offered a rich repository of recommended reading. With this exposure to how FRT works, students will be empowered to write technical claims to support their position.

Anchor Text(s) for this Module

• Street Level Surveillance: Face Recognition by Electronic Frontiers Foundation (EFF), last updated October 24, 2017

  • Excerpted highlights from the same text with language and comprehension scaffolds [coming soon]

• Understanding Facial Recognition, Characterization, and Recognition Technologies by Future of Privacy Forum.

• How I am Fighting Bias in Algorithms TED Talk by Dr. Joy Buolamwini

  • video transcript

Supporting Text(s)/ Resources for this Module

• How Machines Learn by CGP Gray, YouTube, December 18, 2017

  • video transcript

• AI Teachable Machine video introduction

  • video transcript

• Teachable Machine an AI Experiment by folks from Google

• Recommended reading/ viewing to dive deeper:

  • Face Detection & Recognition: How Machine Learning Approaches & Algorithms Make it Possible by Roger Brown, August 11, 2021, Medium [archived link]

  • The Coded Gaze by Joy Buolamwini 📽 (recommended extension)

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.

• RH7: Integrate and evaluate visual and technical information (e.g., in research data, charts, graphs, photographs, videos or maps) with other information in print and digital texts.

• 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.CT.10: Collaboratively design and develop a program or computational artifact for a specific audience and create documentation outlining implementation features to inform collaborators and users.

• 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

• 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