What Is AI?:

According to dictionary.com Artificial Intelligence (AI) is “the branch of computer science involved with the design of computers or other programmed mechanical devices having the capacity to imitate human intelligence and thought.” However, ever in question has been which people are deserving of being called human, and relatedly whose intelligence matters to be reproduced. In this section of the syllabus are pieces that are helpful introductions to AI.

• Extended Definition: TechoPedia Artificial Intelligence

• Podcast: Don’t Fall for the AI Hype ft Timnit Gebru

• Report: Allied Media People’s Guide to AI

• Report: NAACP Resolutions relevant to Artificial Intelligence (AI), Algorithmic Bias, Data Justice, and Prevention of Data Harms (2019-2020)

• Article: How to report better on artificial Intelligence, Kapor et. al

• Article: AI ethics is all about power

•  Webinar: The societal Limits of AI Ethics

Because of the Internet:

While the internet is a useful tool for accessing information, it is also a space where disinformation and harmful ideas about people are perpetuated. Social media, search engines, and advertisements are all connected and influenced by our data. At times the human contribution to the way the internet runs however is minimized. Many platforms use data and the social capital of Black people to promote themselves while perpetuating harm through lack of content moderation and algorithmic suppression. This section focuses on relationships between the internet and Black people including data training for AI Art and discriminatory algorithms. 

• Book:  Black Software: The Internet and Racial Justice, from the AfroNet to Black Lives Matter, Charlton D. McIlwain

• Book: Distributed Blackness: African American Cyber Cultures, André Brock

  • Podcast: Distributed Blackness, The Most Dangerous thing in America podcast Chapters 1&2, Chapter 3, Chapters 4 & 5

• Paper: AI Art and Its Impact on Artists, Jiang et. al

• Paper:  Dismantling the “Black Opticon”: Privacy, Race, Equity, and Online Data-Protection Reform, Anita L. Allen

• Article: How content creators cope with discriminatory algorithms

Race as Technology:

Technology does not just refer to electronic devices, but to fire, pencils, and even the concept of race. Race functions in our society to categorize, subject, and uplift certain groups of people. This section offers an introduction to race as a technology and relationships between technology and race.

• Excerpt: Racial Formations, Omi & Winant

• Book: Race After Technology, Ruha Benjamin

  • Podcast: Race After Technology

• Paper: Benjamin 2016 - Racial Fictions, Biological facts: Expanding the sociological imagination through speculative methods

• Article: On Race, AI, and Representation Or, Why Democracy Now Needs To Redo Its June 1 Segment

• Audio Book: Race, Sex, and Robots: How to Be Human in the Age of AI, Ayanna Howard

• Videos: Lectures on Race and Technology

Algorithms and Race:

Referring back to questions of the human, this section explores algorithms and how they reinforce racism. Artificial Intelligence is built off of algorithms which are fed large data sets. These data sets and therefore algorithms include sets of values. They reinforce ideas about who or what is valuable, who is deserving of what, and what people need to see. This section explores race and algorithms across a variety of topics including healthcare, bias, and search engines.

• Book: Algorithms of Oppression: How Search Engines Reinforce Racism, Safiya Noble

• Book: The Black Technical Object: On Machine Learning and the Aspiration of Black Being, Ramon Amaro

• Article: Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks.

• Article: The Cyborg’s Prosody, or Speech AI and the Displacement of Feeling

• Article: It’s Our Fault that AI Thinks White Names Are More ‘Pleasant’ Than Black Names

• Podcast: “Racism is America’s Oldest algorithm:” How bias creps into health care AI

• Podcast: How algorithms and AI may threaten civil rights

Aims of Technology Education:

We aren’t just users of technology, but people who learn and shape technology. In this section, education is brought into focus in order to highlight how we learn about AI matters for the ways we build technology. This is oriented from concepts such as Science, Technology, Engineering, and Math (STEM), Computer Science Education, and AI Education most specifically.

• Book Chapter: Towards what ends? A critical analysis of militarism and STEM education, Shirin Vossoughi & Sepehr Vakil.

• Paper: We Tell These Stories to Survive: Towards Abolition in Computer Science Education, Stephanie T. Jones, natalie araujo melo

  • Zine: We Tell these Stories to Survive, Stephanie T. Jones, natalie araujo melo, Mia Shaw

• Paper: You Can’t Sit with Us: Exclusionary Pedagogy in AI Ethics Education, Raji et al.

• Paper: Unpacking the “Black Box” of AI in Education, Gillani et al.

• Report: The State of Tech Diversity: The Black Tech Ecosystem

Aims of Technology Education:

We aren’t just users of technology, but people who learn and shape technology. In this section, education is brought into focus in order to highlight how we learn about AI matters for the ways we build technology. This is oriented from concepts such as Science, Technology, Engineering, and Math (STEM), Computer Science Education, and AI Education most specifically.

• Book Chapter: Towards what ends? A critical analysis of militarism and STEM education, Shirin Vossoughi & Sepehr Vakil.

• Paper: We Tell These Stories to Survive: Towards Abolition in Computer Science Education, Stephanie T. Jones, natalie araujo melo

  • Zine: We Tell these Stories to Survive, Stephanie T. Jones, natalie araujo melo, Mia Shaw

• Paper: You Can’t Sit with Us: Exclusionary Pedagogy in AI Ethics Education, Raji et al.

• Paper: Unpacking the “Black Box” of AI in Education, Gillani et al.

• Report: The State of Tech Diversity: The Black Tech Ecosystem

Surveillance and Policing

AI has been used for many things, and one of its largest applications is the surveillance of communities. Surveillance in the US is tied to histories of slavery, and trying to track who the government decided was “free.” Our modern technological systems are built on this history. AI systems are not without error and have led to the false arrest of Black people. This section discusses surveillance and policing in relation to technology.

• Book: Dark Matters: On the Surveillance of Blackness, Simone Browne

• Documentary: Coded Bias, Directed by Shalini Kantayya

• Paper: Everybody’s got a little light under the sun: Black luminosity and the visual culture of surveillance, Simone Brown.

• Website: The Perpetual Line Up: Unregulated Police Face Recognition in America

• Article: Police surveillance and facial recognition: Why data privacy is imperative for communities of color

• Paper: Fitting the description: historical and sociotechnical elements of facial recognition and anti-black surveillance Damien Patrick Williams

(dis)ability and Technology

This section considers how AI is being used to shape medicine and disability rights. Automating these systems has led to the perpetuation of race-based medicine and biases within healthcare. In developing AI systems, how are people with disabilities needs being considered? With the current mass disabling pandemic of Covid-19 there is an increase in people seeking healthcare for long Covid related problems. In our considerations of AI challenges we must consider how (dis)ability and Technology are related.

• Paper: Beyond the hype: large language models propagate race-based medicine

• Report: Who’s in Charge? Information Technology and Disability Justice in the United States

• Paper: Dissecting racial bias in an algorithm used to manage the health of populations

• Podcast: Ability and Accessibility in AI with Meridith Ringel Morris

• Book: More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech - Meredith Broussard

Labor

Artificial Intelligence systems do not operate on their own. There are not only the people who create the code for them, but the people who manage data sets and information that populates them. This labor is often hidden and being outsourced for minimal pay across many contexts including countries such as India and Kenya as well as prisons. The articles in this section offer insights on hidden labor and exploitation within AI work.

• Podcast: The Hidden Workforce That Helped Filter Violence and Abuse Out of ChatGPT

• Article: The Exploited Labor Behind Artificial Intelligence

• Article: These Prisoners are Training AI

• Article: AI Is a Lot of Work

• Podcast: Data & Labor

• Article: Artificial intelligence is creating a new colonial world order

• Paper: Algorithmic Colonization of Africa – Abeba Birhane

Gender and Sexuality

Similar to challenges with AI in healthcare, AI datasets and programming contribute to biases based on gender and sexuality. Many systems only use binary gender descriptions of “male” and “female” which is not gender inclusive and leads to misgendering of many people. Further, these systems have trouble distinguishing binary gender for darker skin tones. For example, darker skin women are misgendered as men. With this in mind, what is worth considering is whether we want these automated systems to try detecting gender or sexuality in the first place and the potential biases and harms that come as a result of that.

• Paper: Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification – Buolamwini and Gebru

• Paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

• Paper: Attack Helicopters and White Supremacy: Interpreting Malicious Responses to an Online Questionnaire about Transgender Undergraduate Engineering and Computer Science Student Experiences

• Article: As AI porn generators get better, the stakes get higher: Porn generators have improved while the ethics around them become stickier

The Environment

Artificial Intelligence does not just affect the digital environment, but contributes to changes in our physical environment. There is a need to consider climate justice in how we develop and use these systems. This section considers the effects of AI on the environment such as large cooling centers for computing. 

• Article: Artificial Intelligence Can Make Companies Greener, but It Also Guzzles Energy

• Article: A.I. tools fueled a 34% spike in Microsoft’s water consumption, and one city with its data centers is concerned about the effect on residential supply

• Article: The Green Dilemma: Can AI Fulfil Its Potential Without Harming the Environment?

• Podcast: AI Today Podcast #123: AI and the Environment

Additional Resources

• Data For Ethics and Social Justice

• Center for Critical Race + Digital Studies Library

• Resources to Learn about AI, BigData, Blockchain, Algorithmic Harms, and Data Justice

• Just Data Lab Resources

• Algorithmic Justice League Library

• Radical AI Technology Power Curriculum

• Black in AI

• DAIR Research