Take a look at the blog posts that I've curated for my portfolio!
“Free Wi-Fi Hotspot at To’Hajiilee Senior Citizen Center” Originally posted: 1/29/2023, Edited: 5/2/2023
“Marginalized and oppressed people are linked to the status of their group” Originally posted: 2/25/2023, Edited: 5/2/2023
“Colorblind Algorithms” Posted: 5/2/2023
My first blog was about Marisa Elena Duarte’s “Network Sovereignty.” It was about information sharing, colonial technology, and the neglected issue of Wi-Fi for contemporary Indigenous reservations. I chose this one because it demonstrates a recurring theme of white supremacy as authority. It’s a post that really touches on a lot of important subjects. While editing, I realized that I didn’t spend enough time connecting the Havasupai people and Wi-Fi (or information sharing). So, it was great seeing how much growth I’ve experienced from that first post to my most recent one. The second blog I included is a polished version of Noble’s analysis of white coders, Google, and algorithms. I decided to include this one because it is chock-full of information I never knew existed. It truly blows my mind. Not to mention, Google – the search engine I and 4.3 billion others use is home to biased search results. This blog also hints at information sharing and colonial technology as authority as we learn how Black, Indigenous, and People of Color are defined. Again, it shows growth in the terms I use and the snippets of relevant quotes. The last annotated blog I included regarded our most recent readings of “Race After Technology.” I felt this blog was more focused than the others and provided modern and relevant instances and anecdotes from Benjamin’s own experience and that of Facebook’s billions of users. Each blog post relates to the creation of AI mirroring beliefs about humanity. If you would like to learn more about these reflections of reality, please check out my TikTok page "algorithmsofdepression"
Tell me about the dream where we pull the bodies out of the lake
and dress them in warm clothes again.
How it was late, and no one could sleep, the horses running
until they forget that they are horses.
It’s not like a tree where the roots have to end somewhere,
it’s more like a song on a policeman’s radio,
how we rolled up the carpet so we could dance, and the days
were bright red, and every time we kissed there was another apple
to slice into pieces.
Look at the light through the windowpane. That means it’s noon, that means
we’re inconsolable.
Tell me how all this, and love too, will ruin us.
These, our bodies, possessed by light.
Tell me we’ll never get used to it.
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.
There is a saying in computer science: garbage in, garbage out. When we feed machines data that reflects our prejudices, they mimic them. Do
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.
People have a tendency to treat technology and data as neutral, sterile and immune to mortal failings. Yet the digital tools we use at schoo
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.
The Verge is about technology and how it makes us feel. Founded in 2011, we offer our audience everything from breaking news to reviews to a
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.
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/.
Zou, James, and Londa Schiebinger. “AI Can Be Sexist and Racist - It's Time to Make It Fair.” Nature News, Nature Publishing Group, 18 July 2018, https://www.nature.com/articles/d41586-018-05707-8.
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?
Colonial tech is so focused on reaching the biggest audience it can that there really is no space for them to care about the repercussions of their product. And if they are legitimately concerned, it's generally in favor of discriminating against Black, Indigenous, and People of Color. Decolonizing tech looks like creating tech according to accurate histories and with values that empower people. It doesn’t look like plowing through sacred land. It doesn’t look like perpetuating racism or claiming racism doesn’t exist. Tech has the power to be something more. Tech has the power to create better lives not just for white people but for Black, Indigenous, and People of Color.
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).
Benjamin, Ruha. Race after Technology: Abolitionist Tools for the New Jim Code, Polity Press, 2019.
Image: Wangechi Mutu, 'You are my sunshine,' collage painting on paper, 24 x 36", 2015.
“Marginalized and oppressed people are linked to the status of their group and are less likely to be afforded individual status and insulation from the experiences of the groups with which they are identified. The political nature of [search engines] demonstrates how algorithms are a fundamental invention of computer scientists who are human beings–and code is a language full of meaning and applied in varying ways to different types of information” -Safiya Umoja Noble, "Algorithms of Oppression"
Before reading Safiya Umoja Noble’s “Algorithms of Oppression,” I was among the many that truly believed Google was a neutral public resource. Maybe you’ve heard the saying “library at your fingertips.”
However, despite popular belief, its algorithms serve paid advertising which happens to sport search results as a product of “most relevant and useful information” (Noble 37). Algorithms involve the process of “rendering,” a procedure that Noble calls “expressly social, economic, and human,” implemented through a series of steps called “algorithms” (37). The common misconception that Search is a mirror to human beliefs is combatted by Noble’s notion that “current algorithmic constraints [do] not provide appropriate social, historical, and contextual meaning to already overracialized and hypersexualized people who materially suffer along multiple axes” (36).
This means that for the white programmers who are uncomfortable talking about race, accurate depictions of Black, Indigenous, and People of Color escape the clutches of our everyday algorithms.
Furthermore, search engine optimization or SEO represents pushing ads or websites to the “top of a results list for a query,” providing profit to various companies or Google based on website clicks (Noble 40). Because Google is what Noble calls a “multinational advertising company,” it can prioritize search results for, say, “Black women” under a multitude of porn hyperlinks over the “eleven and a half billion documents that could have been indexed” (49).
Even someone such as Noble, who defines herself from the Black feminist perspective, a pedagogy that analyzes the intersection between racism, sexism, and so on, would find that search results do not always directly reflect her interests.
One quote that I would like to include, and that pulls away the smoke screen of “naturalized” algorithms, comes early on in the chapter, “Marginalized and oppressed people are linked to the status of their group and are less likely to be afforded individual status and insulation from the experiences of the groups with which they are identified. The political nature of [search engines] demonstrates how algorithms are a fundamental invention of computer scientists who are human beings–and code is a language full of meaning and applied in varying ways to different types of information” (Noble 26).
Image: Andrew Melrose -- Westward the Star of Empire Takes Its Way-- Near Council Bluffs Iowa, 1867
When recounting the 2009 Bresnan Communications, Inc.'s effort to secure federal funds for effective broadband connectivity across Montana’s country and tribal lands, she reveals that the 70 million dollar bid needed to cover the vast rural areas of Montana was denied (118). Such a setback, Duarte writes, is due to disunity on the part of stakeholders, resulting in “resounding effects around future access” (118). “It can take years to rebuild trust among potential partners. Tribes partnering with regional infrastructure teams must be clear about sovereign rights to taxation of infrastructure and services, obtaining rights-of-way, tribal environmental standards, conduct of non-tribal personnel, expectations around investment in workforce training, and other practices for solidifying sound business practices” (Duarte 118). All of this becomes exceptionally clear when one recounts the history of telecommunications on the stolen land, which is now globally recognized as “America.” In chapter 6, Duarte analyzes the famous John Gast painting titled “American Progress,” noting that the otherworldly and angelic imagery of a giant woman caped in white carrying a book in one hand and stretching “the slender wires of the telegraph,” in the other signifies Manifest Destiny as settlers leisurely enjoy the fruits of their labor (111). “Depicted in the lower left corner, Indians flee westward under a dark cloud, trailed by the telegraph and railroad” (Duarte 112). Insofar as assumptions about Native and Indigenous ways of thinking and being and analysis involving zero-sum agonism concerning the techno-scientific world, Duarte says it best in chapter 7 “On the one hand, it is important to accommodate the skills and aptitudes of the digital generation of Native youth. On the other hand, it is likewise important to keep teaching future generations about the limitations of digital devices and methods and remind them of the grace and peace that can be achieved through traditional non-digital practices” (127).
Duarte ME. Network Sovereignty : Building the Internet Across Indian Country. University of Washington; 2017.
Network Sovereignty Among Native and Indigenous Information Technology and Ways of Being
There’s a quote in chapter three of Marisa Elena Duarte's "Network Sovereignty" that sticks with me. It goes like this “Operating within a frame of biological determinism, they [colonists] asserted that the blood showed that the Havasupai people were not entirely Havasupai. Treating the blood as pure information—removed from its context, devoid of significance beyond that of the university lab—the researchers objectified the samples and invested them with values far removed from the desert canyon philosophy of the Havasupai people” (Duarte 36). The researchers were found to have an alternative agenda of “[manipulating] the DNA samples” (Duarte 36).
I want to make a few distinctions here because phrases like “biological determinism” and “pure information” are hard to grasp when you’re not overly familiar with them. Colonial researchers who inherently apply a different understanding of the world, of science, and of ideas of what they believe is true, what they believe the whole world should regard as true, neglected the historical events that led to the regional and global blending of genes for the Havasupai people. What’s more, these scientists have applied their own meanings and have stated them as a universal truth. What they then do with this “truth” is weaponize it against Native and Indigenous peoples. This led to new ways of thinking for Indigenous tribes. Now, they were also tasked with protecting their data and information “as a matter of the integrity of tribal ways of knowing and modes of self-governance” (Duarte 37).
Duarte defines tribal sovereignty in several ways, outlining a “dynamic relationship” between the tenacity of Native and Indigenous peoples and “over [a] millennia” of knowledge and lived experience regarding the homeland in which they protect (37). Eight rights define “Cultural sovereignty and legal-political sovereignty” for “federally recognized tribes,” such as self-governance, determination of citizenship, the right to “administer justice,” regulation of domestic relations, “property inheritance,” “taxation,” “conduct of federal employees,” and “sovereign immunity” (Duarte 37).
There is a need to discern differences between the “integrity of a people [and] the integrity of their government,” as Native and Indigenous peoples nourish a long history of information, “tribal histories, languages, philosophies, spiritualities,” and “Indigenous modes of self-governance,” while also adjusting to ongoing colonial conflict (Duarte 37).
The motives of colonial America, then, have made it extremely difficult for Indigenous tribes to receive adequate broadband and internet services. “Inevitably, the decisions that tribal leaders make interface with the decisions and practices of neighboring governments,” writes Duarte when discussing the “Honoring Nations” program from the “Harvard Institute of American Indian Economic Development,” noting that information sharing in this way was designed explicitly with “intertribal and intergovernmental” connections and the accessibility of the Internet to the many tribal homes that lacked “basic phone, cellular, and Internet service” (49). Duarte also expresses the importance of “cultural sovereignty” among elders and younger members of reservation communities, writing that “tribal geopolitics—political boundaries, physical geography, seasonal cycles, [and] self-governance procedures” all determine the usage of ICTs (49).
The image in this blog post describes the broadband services found within the To'Hajiilee Indian Reservation. The sign is posted outside the "To'Hajiilee Senior Citizen Center," marking the beginnings of Covid-19. https://morningconsult.com/2021/11/22/tribal-lands-broadband-deployment-data-feature/#:~:text=The%20efforts%20of%20Indigenous%20leaders,percent%20of%20the%20overall%20population.
