#raceaftertechnology

image: 2022 Max Löffler, illustration for Bandcamp Daily

Introduction: 

An overarching theme for my unit projects is how white people in tech have historically neglected accurate elements of a racialized society. For my multimodal redesign, I’d like to take my most recent unit project, “How Are Tech Companies Responsible for Racist AI?” and expand on how data that is discriminatory or unrepresentative of Black, Indigenous, and People of Color creates inaccurate algorithms. I chose this topic because it is honestly astounding to me that the public, that is, the average internet user, is made to be unaware of how algorithms are not simply objective. First, there is a piece by Megan Garcia that I’ve chosen as one of my scholarly sources. It’s titled “Racist in the Machine: The Disturbing Implications of Algorithmic Bias,” and it tells the story of a Twitter bot designed by Microsoft named Tay. Tay went from a happy helper to a “racist Holocaust denier” in the span of twenty-four hours. With Garcia’s piece, I will analyze how AI is tested in isolated, controlled environments that rely on a select few coder’s biases. Then, I’ll expand on what exactly this does, its consequences, and possible solutions. The next scholarly source I’m adding is a piece by James Zou and Londa Schiebinger titled “AI can be sexist and racist — it’s time to make it fair.” The work analyzes ImageNet, a large visual database designed for visual object recognition software research. ImageNet receives 45% of its data from the United States alone, which Zou and Schiebinger argue is under representative of the world at large.

image: The ArchAndroid, album by Janelle Monáe

Literature Review: 

“Race After Technology” is a book by Ruha Benjamin that focuses specifically on internet-based technologies according to the “New Jim Code.” Such technologies include the modern creation and usage of facial recognition software, predictive crime algorithms, and even soap dispensers. Benjamin analyzes the tech, claiming it's hastily fastened and is less of a marker of societal progression and more of an irresponsibly coded software. Benjamin has specifically talked about the MIT scientists who have avoided programming aspects of gender, class, and race in an attempt to create robots without bias. 

Olga Akselrod, writer of “How Artificial Intelligence Can Deepen Racial and Economic Inequities,” talks about how AI is touted as a “smart economic investment for the future.” But she asks for who? The author continues with several instances of how AI has caused discriminatory harm, including housing discrimination, lack of representation in data, and racial profiling in job screenings. 

“What Really Happened When Google Ousted Timnit Gebru” is an article written by Tom Simonite that describes the work culture of non-white ethicists who research the effects of tech. The article examines a back-and-forth between Gebru and a Google executive. 

Gerrit De Vynck and Will Oremus, authors of “As AI Booms, Tech Firms Are Laying Off Their Ethicists,” write about Twitch streamers who claim the platform has a racial bias. Next, they discuss various social platforms that have cut their ethics and social teams. 

“Pause Giant AI Experiments” is an open letter from the Future of Life Institute. It calls for all AI labs to stop the production of AI systems exceeding the capability of GPT-4. It also calls for at least six months of training for such systems. The AI systems in question are defined as “human-competitive” in intelligence. The open letter claims that such systems can pose “profound risks to society and humanity.” 

“Racist in the Machine” is an essay by Megan Garcia that challenges unconscious and institutional biases that fly under the radar of companies and governments. She discusses “distorted data,” “cybersecurity,” and “crowd-level” monitoring. 

James Zou and Londa Schiebinger, writers of “AI Can Be Sexist and Racist— It’s Time to Make it Fair,” call for the importance of recognizing sources of bias and de-bias training. 

image: Debra Yepa-Pappan Live Long & Prosper, Spock was a Half Breed, 2008

Discussion: 

White coders, who are over-representative of implicit bias, exist in conditions that only compound the racism found therein. Black, Indigenous, and People of color are, therefore, underrepresented not only in terms of accurate data but of literal population in tech companies. Coders at an individual level and companies alike need to understand that colorblind ideology is inevitably complacent with racism. 

For starters, Nikon is programmed to see Asian eyes as always blinking, sending an alert to its user (Zou and Schiebinger). Microsoft and Twitter don’t see the point in continuing ethical research of AI (Vynck and Oremus). Google buries unsavory research on its social and ethical ramifications (Simonite). There aren’t enough Black, Indigenous, and People of Color employed by tech companies (Akselrod). Twitter bot Tay, a Microsoft algorithm, started out as a playful, childlike newbie of Earth, only to utter outlandish statements like “[feminists] should all die and burn in hell” (Garcia). Garcia suggests the reason why this bot took in ideologies of racism, bigotry, and xenophobia is that it's isolated in creation. It has zero experience with the spectrum of humans that roam this Earth. What’s worse is how these isolated and controlled environments perform. 

MIT's data scientists work hard to construct robots without gender, class, or race (Benjamin). 

Quote: While the robots indeed were “servants” and “workers,” MIT scientists referred to them as “friends and children, addressing them in “class-avoidant” terms (42). Programmers decided not to input the varying histories of racism, transphobia, and misogyny that made them uncomfortable. Benjamin states this colorblind, class-blind, and gender-blind approach merely serves as “another avenue for coding inequity” (42). 

While this kind of care for Black, Indigenous, and People of color is often described as covert, I argue that it is most often worse than undisguised modes of racism. It becomes almost impossible to name and stop discriminatory AI when it matches human intelligence on a mass scale. There is comfort in being on top. Receiving the daily privileges that make life as a white person so bearable determines why it is difficult for white coders to recognize white power. It’s easier to leave these histories out. It’s easier not to have to examine why we don’t feel the need to include dark-skinned people in image data software. 

image: Cover detail of Grace Dillon, Walking the Clouds: An Anthology of Indigenous Science Fiction (University of Arizona Press, 2012). Art by Beth Dillon.

Conclusion: 

Akselrod says, “The tech industry’s lack of representation of people who understand and can work to address the potential harms of these technologies only exacerbates [racist AIs]” (1). Because we live in a racialized society, one with histories of slavery and colonization, there is an unconscious bias inherently in the minds of white people. There is no way for us to have accurate histories and representations of Black, Indigenous, and People of Color without involving them in mass quantities in the process of AI development. That’s the very first step that needs to be taken. Relinquishment of the white leader. 

image: “Time Traveller,” 2018, by Kongkee/ Image: Courtesy of the artist and Penguin Lab. © 2018 the artist.

Bibliography

Akselrod, Olga. “How Artificial Intelligence Can Deepen Racial and Economic Inequities: ACLU.” American Civil Liberties Union,https://www.aclu.org/news/privacy-technology/how-artificial-intelligence-can-deepen-racial-and-economic-inequities. 

Benjamin, Ruha. Race after Technology: Abolitionist Tools for the New Jim Code, Polity Press, 2019. 

Garcia, Megan. “Racist in the Machine: The Disturbing Implications of Algorithmic Bias.” Duke University Press, Duke University Press, 1 Dec. 2016, https://read.dukeupress.edu/world-policy-journal/article-abstract/33/4/111/30942/Racist-in-the-MachineThe-Disturbing-Implications. 

“Pause Giant AI Experiments: An Open Letter.” Future of Life Institute, 21 Apr. 2023, https://futureoflife.org/open-letter/pause-giant-ai-experiments/. 

Simonite, Tom. “What Really Happened When Google Ousted Timnit Gebru.” Wired, Conde Nast, 8 June 2021, https://www.wired.com/story/google-timnit-gebru-ai-what-really-happened/. 

Vynck, Gerrit  De, and Will Oremus. “As AI Booms, Tech Firms Are Laying off Their Ethicists.” The Washington Post, WP Company, 3 Apr. 2023, https://www.washingtonpost.com/technology/2023/03/30/tech-companies-cut-ai-ethics/. 

Technology, simply defined, is information with a goal and a way to be reproduced. Writing is technology. So are telephone polls, hammers, knives, and AI. Technology has often been cited as the driving force of the United States empire. And for Western ways of living, technology has been seen as a gift to foreign regions. For the purposes of this essay, I will connect concepts of racism that have led to what we consider today as objective technology– that is, tech that is unbiased and bears science-backed ideas of truth. And thus, we will discover how the intersection of tribal sovereignty, (un)naturalized algorithms, and colorblind ideology all help us understand how technology has historically been used as a tool to boost colonial motives. Let’s first look at tribal sovereignty, what it means, and how it works in our modern world.

Tribal sovereignty, as defined by Marisa Elena Duarte, is the right for Indigenous and Native tribes to self-govern. But because white leaders have a penchant for manifest destiny, the Indigenous peoples of what is now known as “America” are forced into difficult situations like losing land and access to the internet. To understand how to move forward, Duarte asks us in her book “Network Sovereignty” to look at Indigenous spiritual practices and appreciation of the land in contrast to how colonial technology is developed under racist guidelines. She lists these considerations for the digital generation: colonial technologies have historically been weaponized against Native and Indigenous peoples, digital technology can support Native youth, and awareness of digital technology’s ability to limit traditional Native notions of grace and peace.

On that note, the “Natives in Tech” conference, which was held in 2020, has provided that there are big movements created by Indigenous and Native tribes to cut out third-party vendors and big tech companies and is moving forward with creating a network of Native software engineers. These software engineers move within the parameters of the sacred land connection integral to Indigenous ways of living. These movements teach us that it is possible to create systems of technology that fully encapsulate non-white and non-western existences and that doing so actually makes the spread of information found in the complexities of globalization.

Ever since the birth of search engines and the internet at large, we've been told that algorithms tell us more about ourselves than they do about the people who make them. What our screens show us is a product of what we’ve searched previously and what we’ve clicked on the most. The reality is algorithms are not naturalized. Algorithms are, actually, highly simplified systems based on the coder’s bias. Safiya Umoja Noble, author of “Algorithms of Oppression,” contends they currently “[do] not provide appropriate social, historical, and contextual meaning to already overracialized and hypersexualized people who materially suffer along multiple axes” (36). A big hint for why this is such an issue comes from the lack of Black, Indigenous, and People of Color employed in Silicon Valley. In “Silicon Valley Pretends That Algorithmic Bias Is Accidental,” Amber Hamilton discusses the tech culture, which has a history of racist and sexist hiring discrimination.

What’s more, Hamilton continues, is that tech companies, like Google, have a habit of dissuading employees from holding political discussions in the workplace. Yet again, we see an example of how easy it is for white tech to reflect white interests. We return, again, to the overarching idea that white tech seeks white power. Even if these instances seem unintentional, they tell us a story. The colorblind ideology that ensues, as Ruha Benjamin says in “Race After Technology,” “are sold as morally superior because they purport to rise above human bias” (38). It is almost impossible to challenge tech as we are brought to believe it's an entity all of its own, totally void of its creator's morality. MIT's data scientists work hard to construct robots without gender, class, or race. While the robots indeed were “servants” and “workers,” MIT scientists referred to them as “friends and children, addressing them in “class-avoidant” terms (Benjamin 42). Programmers felt so uncomfortable inputting the varying histories of racism, transphobia, and misogyny that they just let them out altogether. Unfortunately, acting as if these things didn’t exist doesn’t make technology better. It only makes it worse. So how do tribal sovereignty, naturalized algorithms, and colorblind ideology all tie together?

Image: Pranchetober 2020 - Day 21 by Leandro Massai https://www.behance.net/gallery/128359753/Pranchetober-2020?locale=en_US

Colorblind Algorithms

“These tech advances are sold as morally superior because they purport to rise above human bias, even though they could not exist without data produced through histories of exclusion and discrimination.” — Ruha Benjamin, “Race After Technology.”

“Names are racially coded,” writes Benjamin, after explaining that giving her son an Arabic first and middle name will most definitely ensure racial profiling at the airport. “While [names] are of the everyday tools we use to express individuality and connections, they are also markers interacting with numerous technologies, like airport screening systems and police risk assessments, as forms of data” (Benjamin 1).

But the list doesn’t end there. The Old Jim Crow transitions our world from “explicit racialization” to The New Jim Crow of “colorblind ideology” as legal systems and software programs alike discriminate against Black names (Benjamin 5). A key factor here is the tech companies' drive for objectivity —which Benjamin notes is merely an “allure [of nonpartisan information] without public accountability” (36).

For instance, Mark Zuckerberg’s famous quote, “Move fast and break things,” pushed Facebook into the hands of billions as a radical attempt to avoid government oversight. But for Benjamin, it only glossed over important distinctions of racial inequity. As a result, we got Facebook faster than we should've, and with that came a hoard of issues.

Benjamin ultimately retorts with the slogan, “Move slower and empower people,” against the wave of Facebook's racist ad delivery, allowance of hate speech, and lack of racial bias research (6).