Watch and read the following narratives:

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• 10 min
• n/a
• 2018

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• 
• 10 min
• n/a
• 2018
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In Event of Moon Disaster

Techniques of misinformation are used to make a film about an alternative history in which the Apollo 11 mission failed and the astronauts became stranded on the moon.

Discussion Prompts:

How do you foresee this type of misinformation being related to conspiracy theories? Do you believe you could have spotted the deepfake were you not specifically looking out for it? Are we approaching a future where we may have to watch all media with such scrutiny?

• Digital Media Misinformation
• Deep Fake
• Computer Vision
• Machine Learning

In Event of Moon Disaster

Techniques of misinformation are used to make a film about an alternative history in which the Apollo 11 mission failed and the astronauts became stranded on the moon.

• Digital Media Misinformation
• Deep Fake
• Computer Vision
• Machine Learning

• 
• 13 min
• Danielle Citron
• 2019

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• 
• 13 min
• Danielle Citron
• 2019
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How deepfakes undermine truth and threaten democracy

The use of deepfake technology to manipulate video and audio for malicious purposes — whether it’s to stoke violence or defame politicians and journalists — is becoming a real threat. As these tools become more accessible and their products more realistic, how will they shape what we believe about the world? In a portentous talk, law professor Danielle Citron reveals how deepfakes magnify our distrust — and suggests approaches to safeguarding the truth.

Discussion Questions:

• What are some of the possible uses for video and audio deepfakes?
• What is trust? How do we normally verify information we receive?
• How does this type of technology erode trust in existing systems of accountability in society?
• Can you think of any possible positive uses of this technology?

How deepfakes undermine truth and threaten democracy

The use of deepfake technology to manipulate video and audio for malicious purposes — whether it’s to stoke violence or defame politicians and journalists — is becoming a real threat. As these tools become more accessible and their products more realistic, how will they shape what we believe about the world? In a portentous talk, law professor Danielle Citron reveals how deepfakes magnify our distrust — and suggests approaches to safeguarding the truth.
Discussion Questions:

What are some of the possible uses for video and audio deepfakes?
What is trust? How do we normally verify information we receive?
How does this type of technology erode trust in existing systems of accountability in society?
Can you think of any possible positive uses of this technology?

• 
• 5 min
• MIT Tech Review
• 2020

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• 
• 5 min
• MIT Tech Review
• 2020
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The Year Deepfakes Went Mainstream

With the surge of the coronavirus pandemic, the year 2020 became an important one in terms of new applications for deepfake technology. Although a primary concern of deepfakes is their ability to create convincing misinformation, this article describes other uses of deepfake which center more on entertaining, harmless creations.

Discussion Prompts:

Should deepfake technology be allowed to proliferate enough that users have to question the reality of everything they consume on digital platforms? Should users already approach digital media with such scrutiny? What is defined as a “harmless” use for deepfake technology? What is the danger posed to real people in the acting industry with the rise of convincing synthetic media?

• Digital Media Misinformation
• Deep Fake
• Machine Learning
• Object Recognition

The Year Deepfakes Went Mainstream

With the surge of the coronavirus pandemic, the year 2020 became an important one in terms of new applications for deepfake technology. Although a primary concern of deepfakes is their ability to create convincing misinformation, this article describes other uses of deepfake which center more on entertaining, harmless creations.

• Digital Media Misinformation
• Deep Fake
• Machine Learning
• Object Recognition

We will work in trios to discuss the implications of deepfakes and developer's roles on the development of this type of technology.

1. Spend a few minutes experiencing these deepfakes put together by MIT Media Labs.

   • 
   • 10 min
   • MIT Media Labs
   • 2021

   

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   • 
   • 10 min
   • MIT Media Labs
   • 2021
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   Detect DeepFakes

   This is an MIT research project. All data for research is collected anonymously for research purposes.

   This project will show you a variety of media snippets including transcripts, audio files, and videos. Sometimes, we include subtitles. Sometimes, the video is silent. You can watch the videos as many times as you would like. The site will ask you to share how confident you are that the individual really said what we show. If you have seen the video before today, please select the checkbox that says “I’ve already seen this video.” And remember, half of the media snippets that are presented are statements that the individual actually said. Read more about this project and the dataset used to produce this research on the About page

   https://detectfakes.media.mit.edu/about

   Discussion Prompts:

   • What about these videos is so disturbing?
   • How can we be convinced to not trust our own judgement about the information being presented?

   • Digital Media Misinformation
   • AI
   • Computer Vision

   Detect DeepFakes

   This is an MIT research project. All data for research is collected anonymously for research purposes.
   This project will show you a variety of media snippets including transcripts, audio files, and videos. Sometimes, we include subtitles. Sometimes, the video is silent. You can watch the videos as many times as you would like. The site will ask you to share how confident you are that the individual really said what we show. If you have seen the video before today, please select the checkbox that says “I’ve already seen this video.” And remember, half of the media snippets that are presented are statements that the individual actually said. Read more about this project and the dataset used to produce this research on the About page
   https://detectfakes.media.mit.edu/about

   • Digital Media Misinformation
   • AI
   • Computer Vision

2. Brainstorm and take notes about these discussion prompts.

   Please, brainstorm with your group and list at least 5 ways in which deepfakes POSITIVELY impact or can impact societies.

   Please, discuss with your group and list at least 5 ways in which deepfakes NEGATIVELY impact or can impact societies.

   Take 3-minutes to quickly find a couple of recent news about deepfakes. Share them with your colleagues and select one to report to the entire group.

3. Select one person from your group to report your findings back to the entire class when we return from breakout rooms.

Please, answer this short concept check.

For a critical perspective on the video we watched in the pre-meeting activity, watch this documentary by Scientific American.

The goal of this module is to provide students with an opportunity to consider deepfake technologies and the ethical issues surrounding impact on individuals and society. The module is designed for introductory to intermediate CS courses (i.e., Intro to CS through Algorithms). It follows the CEN format asking students to consider their preexisting ideas and knowledge about the technology (Pre-Activity), consider the history of who created the technology, its original/intended purpose, the impact of its widespread applications (Activity 1), reflect on society level impact from multiple perspectives (technology creator, campus community member, person in society) (Activity 2), and assess existing ethical guidelines from technology organizations (Post-Activity).

• Students will be able to identify a technology, who designed it, and the purpose it was designed for.

• Students will be able to identify who the technology was not intended for and who it potentially may harm.

• Students will be able to articulate how the technology has changed over time and the different purpose it now serves.

• Students will be able to articulate and discuss possible benefits and harms with different types of deepfake technologies.

We have provided examples of assessments you may want to copy and use on your own LMS for secure student data collection. The rationale, format, and time length for the module components are listed below.

Pre-Activity is an online pre-module assignment asking students to respond to a series of questions about what they know about this technology and viewing provided links to narratives.

Activity 1 is a class activity (0.5 hours) that places students into small groups to read and discuss a set of provided narratives to inform them about certain perspectives on the technology.

Activity 2 is a class activity (45min to 1.5 hours) that provides a chance for the class to break into groups (n=~24 or more with larger class/online class) to evaluate the positive and negative impacts of deep fake technologies from multiple perspectives using several prompt questions on a class bulletin board (aka jamboard or something like it).

Post-Activity is an online post-module assignment that asks students to respond again to a series of questions about what they now know about this technology based on the narratives they viewed or read and the Activity 1 or 2 discussions.