Yuri's Console Log

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[unsupervisedmachine]

Blog post #3 - Week 4 (late)

Would it be possible to have technology that is coded with no bias and no gendered influence?

In theory, it sounds ideal to imagine technology that is completely neutral and free of bias, but after reading articles from Ruha Benjamin and Safiya Noble, it seems unrealistic. Technology does not exist in a vacuum. It is created by people who live within social systems shaped by race, gender, class, and power. Because of this, the values and assumptions of designers almost always get embedded into the technology they create. Benjamin argues that technology is often presented as objective and progressive, but in reality it frequently reproduces existing inequalities in more subtle ways. This is what she calls the New Jim Code, where discrimination is hidden behind claims of neutrality.

Even when developers try to remove bias, they still rely on data sets that reflect historical inequalities. For example, algorithms trained on past hiring or policing data will learn patterns shaped by racism or sexism, even if those categories are never explicitly included. Noble makes a similar point when she discusses algorithmic oppression, showing how search engines can reinforce harmful stereotypes while appearing neutral. Gender bias also shows up in design choices like voice assistants that default to feminine personas or technologies that assume male users as the norm.

Everett’s work reminds us that marginalized communities have always engaged with technology creatively and critically, even when it was not designed with them in mind. Bias may never be fully eliminated, but acknowledging it allows for intervention, regulation, and resistance. Pretending technology is neutral only makes its harms harder to see and challenge.

Does adding new technologies create more intersectionality between technology and humans and how does this connect to the idea of the cyborg?

As new technologies become more integrated into daily life, the relationship between humans and technology becomes more complex and deeply intersectional. Donna Haraway’s idea of the cyborg helps explain this connection. The cyborg represents a blending of human and machine that challenges clear boundaries between the natural and the artificial. Today, this idea feels especially relevant because technology shapes how people work, communicate, organize politically, and even understand themselves. These interactions are never just technical. They are shaped by race, gender, class, and other social identities.

Intersectionality shows that people experience technology differently depending on their position in society. For example, facial recognition software may work well for white men but fail more often for women or people of color. Social media platforms can amplify some voices while silencing others based on how algorithms prioritize content. These differences demonstrate that becoming more technologically connected does not affect everyone equally. Instead, technology often intensifies existing inequalities while presenting itself as universal.

The cyborg concept helps reveal how power operates within these systems. Haraway saw the cyborg as a potential tool for resistance because it disrupts traditional hierarchies and binaries. However, scholars like Benjamin remind us that cyborg like systems can also reinforce domination if they are built on biased assumptions. New technologies expand the human machine relationship, but they also extend surveillance, control, and data extraction, especially for marginalized groups.

In this sense, intersectionality helps us understand the cyborg not as a futuristic fantasy but as a lived reality. Humans already exist within networks of machines that shape opportunity and exclusion. Recognizing this allows us to question who benefits from technological integration and who is made more vulnerable by it.

Are there examples of the New Jim Code in modern technologies outside of social media platforms?

The New Jim Code appears in many everyday technologies beyond social media, often in systems that claim to be efficient or objective. One major example is predictive policing software. These systems use historical crime data to predict where police should patrol. Because past policing practices disproportionately targeted Black and low income neighborhoods, the data itself is already biased. As a result, these technologies send more police into the same communities, reinforcing cycles of surveillance and criminalization while appearing data driven and neutral.

Another example is automated hiring software. Many companies use algorithms to screen resumes or rank applicants. If these systems are trained on previous hiring decisions that favored white men, they may quietly filter out women or people of color without explicitly considering race or gender. Benjamin argues that this is a key feature of the New Jim Code. Discrimination does not disappear. It simply becomes harder to see and challenge.

Health care technologies also reflect these patterns. Risk assessment tools used to allocate medical resources have been shown to underestimate the needs of Black patients because they rely on cost based data rather than actual health outcomes. This means fewer resources are directed toward communities that already face medical neglect. Noble’s work reminds us that when algorithms are treated as neutral authorities, their harmful impacts are often ignored or justified.

These examples show that the New Jim Code operates through infrastructure, not just platforms. Technologies that manage housing, employment, healthcare, and policing all shape life chances. Because these systems are framed as technical solutions, they often escape public scrutiny. Recognizing them as social tools rather than neutral machines is essential to resisting their harms.

How does memes affect social media algorithms in a way where they don't promote inclusiveness. How does this happen even though memes are supposed to be harmless.

Memes play a major role in shaping how social media algorithms prioritize content, but they often work against inclusiveness rather than supporting it. Algorithms are designed to reward engagement, meaning content that gets likes, shares, and comments is pushed more widely. Memes that rely on stereotypes, shock value, or insider humor often perform well because they provoke strong reactions. However, this can reinforce exclusion by normalizing racist, sexist, or ableist ideas under the guise of humor.

Many memes depend on shared cultural knowledge that centers whiteness, masculinity, or dominant internet cultures. When these memes circulate widely, they can marginalize people who do not fit those norms. The Week 4 lecture emphasizes that culture is not neutral online. Memes are cultural texts that reflect power dynamics. When algorithms amplify certain memes over others, they help define whose experiences are visible and whose are dismissed.

Noble’s concept of algorithmic oppression applies here because algorithms do not simply reflect user behavior. They shape it. If divisive or offensive memes consistently receive more visibility, users learn what kind of content is rewarded. Inclusive or nuanced content may be buried because it does not generate the same immediate engagement. This creates feedback loops where exclusionary humor becomes normalized.

Memes can also be weaponized politically, spreading misinformation or reinforcing harmful narratives while avoiding accountability because they appear playful or ironic. Everett’s work reminds us that digital culture can be used for empowerment, but only when communities actively shape it. Without critical awareness, meme culture can quietly reproduce inequality while algorithms frame it as entertainment rather than harm.

References:

A. Everett - The Revolution will be Digitized: Afrocentricity and the Digital Public Sphere R. Benjamin - Race after Technolog S. Noble - Algorithms of Oppression

[unsupervisedmachine]

Blog post #5 - Week 7 (late)

Is the idea that digital technology is neutral still believable after reading about race and power online?

After engaging with the readings from Noble, Senft, Benjamin, and Daniels, it becomes very difficult to believe that digital technology is neutral. One of the strongest takeaways from these texts is that technology is not created in a vacuum but is shaped by social values, institutions, and power relations that already exist. Senft and Noble emphasize that race is socially constructed online just as it is offline, meaning that racism does not disappear when interactions move to digital spaces. Instead, it often becomes harder to recognize because it is embedded in interfaces, algorithms, and platforms that appear objective or technical.

Benjamin pushes this idea further by arguing that race itself functions as a technology. She explains that algorithms sort people into categories that reinforce inequality while presenting the illusion of progress and personalization. For example, recommendation systems and targeted advertising claim to show users what they want, but they often reproduce stereotypes and unequal visibility. This creates a situation where discrimination is automated and hidden behind claims of efficiency or neutrality.

Daniels also dismantles the neutrality myth by showing how white supremacist movements have adapted to digital spaces in strategic and intentional ways. The internet is not just a passive platform where racism happens accidentally. It actively enables global connections, misinformation, and the normalization of extremist views. The fact that young people often encounter racist content through search engines or social media without context makes the idea of neutrality even more dangerous.

These readings show that believing technology is neutral allows systems of inequality to operate without accountability. If digital tools are treated as unbiased, then the harm they produce is framed as unavoidable or incidental. Recognizing that technology reflects social power is necessary if we want to challenge racism rather than unknowingly reinforce it.

How can algorithms create the illusion of diversity and inclusion while maintaining existing racial hierarchies?

Algorithms often create the appearance of diversity without producing meaningful change, which is one of the most unsettling arguments made by Benjamin and echoed in the other readings. Benjamin explains that platforms use multicultural representation as a marketing strategy rather than a commitment to equity. For example, streaming services may promote shows featuring people of color to specific audiences, making it seem like representation has increased even when those characters play minor or stereotypical roles. This gives users the feeling that progress has been made while the underlying power structures remain unchanged.

This illusion is reinforced by how algorithms personalize content. Because users see feeds curated specifically for them, it becomes harder to recognize patterns of exclusion or bias at a larger scale. Senft and Noble argue that race is constructed through repeated online interactions, meaning that algorithms help normalize what types of bodies, voices, and experiences are considered visible or valuable. When diversity is reduced to aesthetic representation rather than structural inclusion, racial hierarchies are preserved.

Daniels adds another layer by showing how white supremacy thrives in digital spaces that appear open and democratic. While platforms may highlight diversity in branding, they often fail to intervene effectively against racist misinformation or harassment. This selective enforcement allows harmful ideologies to spread while companies maintain a public image of inclusivity.

The problem is not just a lack of diversity but how diversity is used. When representation becomes a surface level feature rather than a challenge to inequality, it functions as a distraction. Algorithms make racial inequality harder to see by framing discrimination as personalized choice rather than systemic design. This makes it easier for users and platforms to deny responsibility while benefiting from the appearance of inclusion.

How do digital platforms shape how young people learn about race and racism?

Digital platforms play a major role in shaping how young people understand race, often serving as their primary source of information. Daniels emphasizes that search engines and social media have replaced libraries and classrooms for many students, which makes the design of these platforms incredibly important. When racist and antiracist content are presented side by side without context, it creates confusion about credibility and truth. This can lead young users to believe that all perspectives are equally valid, even when some are rooted in misinformation or hate.

Senft and Noble point out that race online is not just discussed but actively constructed. Memes, hashtags, videos, and comment sections all contribute to how racial identities are framed. These repeated representations shape what feels normal or acceptable. When platforms allow racial stereotypes to circulate widely while limiting marginalized voices, they influence how young people internalize ideas about race without realizing it.

Benjamin adds that algorithms amplify certain narratives while hiding others. Because platforms reward engagement, content that provokes strong emotional reactions often spreads faster than nuanced discussion. This means that harmful or misleading representations of race can become more visible than thoughtful analysis. Over time, this affects how young users understand racism as individual conflict rather than systemic inequality.

The danger is that learning about race through digital platforms without critical tools can reinforce ignorance rather than challenge it. Without media literacy and racial awareness, young people may mistake visibility for equality and personalization for fairness. These readings show that digital spaces are educational environments whether we acknowledge them or not, and ignoring their influence allows inequality to shape learning unchecked.

Why is colorblind ideology especially harmful in digital spaces?

Colorblind ideology is particularly harmful online because it disguises inequality under the language of neutrality and fairness. The Week 7 lecture notes explain that colorblind privilege is the ability to ignore race because it does not significantly shape one’s daily experiences. In digital spaces, this ideology is reinforced by claims that the internet treats everyone equally. However, as the readings show, ignoring race does not eliminate racism. It allows it to operate invisibly.

Senft and Noble argue that refusing to see race online makes it easier to deny responsibility for harm. When racist outcomes occur, platforms can claim they did not intend discrimination, and users can claim they were just participating in neutral systems. Benjamin explains that algorithms depend on data shaped by existing inequalities. Pretending race does not matter means these systems are never questioned or corrected.

Daniels highlights how colorblind rhetoric enables white supremacy to spread more easily. When racism is framed as just another opinion rather than a systemic issue, extremist content can blend into mainstream discourse. This makes it harder to challenge because it no longer looks overtly racist. Instead, it presents itself as logic, free speech, or alternative truth.

Colorblindness also affects how users interpret representation. When people see limited diversity online, they may assume it reflects personal choice rather than structural exclusion. This shifts blame away from platforms and onto marginalized groups themselves.

In Conclusion, colorblind ideology in digital spaces protects systems of inequality by making them harder to see. These readings make it clear that acknowledging race is not divisive. It is necessary for understanding how power operates online. Without confronting race directly, digital culture will continue to reproduce the same hierarchies it claims to move beyond.

References: T. Senft and S. Noble - Race and Social Media

R. Benjamin - Race After Technology

J. Daniels - White Supremacy in the Digital Era: An Intro

[unsupervisedmachine]

Blog post #6 - Week 8 (late)

Do you think there would be soon a future where we would need to heavily impose restriction on cyborgs?

When thinking about the future of cyborgs, I do not think the main issue is whether we need heavy restrictions, but rather who gets to define what a cyborg even is. In A Cyborg Manifesto, Donna Haraway argues that humans are already cyborgs because we constantly merge with technology through medicine, communication tools, and digital identities. From that perspective, restricting cyborgs would almost mean restricting everyday human behavior. What seems more realistic is that regulation will appear indirectly through laws about data, surveillance, and bodily autonomy rather than explicit rules about cyborgs themselves.

However, the readings also show why this conversation matters. Daniels explains how technology often reproduces existing systems of power, especially around race and gender. If cyborg technologies are developed within systems that already support inequality, then restrictions might end up targeting marginalized people instead of protecting them. For example, medical implants or monitoring technologies could be used more aggressively on certain groups under the justification of safety or efficiency. That kind of control echoes what Haraway calls the border war between bodies and machines, where technology becomes a tool of domination instead of liberation.

Haraway imagines the cyborg as a figure that resists origin stories and fixed identities, which is what makes it politically powerful. Heavy restrictions could undermine that possibility by forcing cyborg bodies back into rigid legal and social categories. Rather than strict control, it seems more important to regulate the systems that produce cyborg technologies, including corporations, governments, and designers. The future likely does not require restrictions on cyborgs themselves, but accountability for how technological power is used and who it benefits.

Does blurring the line between humans and technology in the creation of the cyborg also affect gender based roles too?

Blurring the line between humans and technology absolutely affects gender roles, but not always in the progressive way that Haraway initially imagines. In A Cyborg Manifesto, Haraway presents the cyborg as a feminist figure that breaks away from traditional gender binaries. Because the cyborg has no origin story tied to biology, it opens space for imagining identities beyond woman or man. In theory, this suggests a future where gender roles lose their power and flexibility becomes possible.

However, the other readings complicate this idea. O’Riordan shows that many digital technologies still rely heavily on stereotypical representations of femininity. Virtual assistants, game characters, and digital interfaces are often designed with female voices and bodies meant to appear friendly, passive, and attractive. Even though these figures are technological, they reinforce traditional gender expectations rather than challenge them. This suggests that while the human technology boundary is blurred, gender norms are often carried into digital space unchanged.

Daniels adds another layer by showing how online spaces reproduce gendered power structures connected to whiteness and masculinity. When technology is built within these systems, it tends to amplify dominant gender roles instead of dissolving them. As a result, the cyborg does not automatically free us from gender expectations. Instead, it can become a new surface where old ideas about femininity and masculinity are projected.

Overall, blurring the line between humans and machines has the potential to challenge gender roles, but only if the cultural assumptions behind technology change. Without that shift, cyborgs risk becoming another way that gender norms are reinforced rather than dismantled.

Is there an equivalent problem with in terms of shedding light on toxic masculinity when it comes to technology?

There is definitely an equivalent problem when it comes to addressing toxic masculinity in technology, and it often goes unnoticed. Daniels shows that many online spaces, including extremist forums, are structured around dominance, control, and aggression, which are traits commonly associated with toxic masculinity. These traits are rewarded through visibility, authority, and influence, making it difficult to challenge them from within digital systems.

Technology culture more broadly reflects this issue as well. Tech industries often celebrate ideas like disruption, mastery, and competition, which align closely with masculine ideals of power and rationality. Haraway critiques this tradition by pointing out how Western science is tied to patriarchal control and separation of mind from body. When technology is framed as neutral or objective, it hides the masculine values embedded in its design and use.

O’Riordan indirectly supports this by showing how femininity is often coded into technology as passive or supportive, while authority and control remain masculine coded. This division reinforces the idea that men build and control technology while women are represented as interfaces or assistants. As a result, toxic masculinity becomes normalized in tech spaces because it is rarely named or challenged.

The difficulty in shedding light on toxic masculinity in technology is that it is often treated as a cultural issue rather than a technical one. Until masculinity itself is recognized as something built into technological systems, it will continue to shape digital environments in harmful ways. How does whiteness continue to shape digital bodies and identities in cyborg culture?

Whiteness plays a major role in shaping how digital bodies and cyborg identities are imagined and represented. Daniels introduces the idea of translocal whiteness, showing how white identity is reinforced across online spaces even when users come from different national backgrounds. This helps explain why whiteness often appears as the default identity in digital environments. It is present without being named, which makes it seem neutral or universal.

O’Riordan’s discussion of virtual femininity highlights how many digital women are designed around white beauty standards. Characters like Lara Croft or virtual newsreaders reflect ideals tied to Western femininity, including body shape, facial features, and behavior. These representations suggest that even non human bodies are shaped by racial norms rooted in whiteness.

Haraway’s cyborg was meant to resist these dominant narratives, but the reality shows how difficult that is. If cyborgs are built using biased data and cultural assumptions, they will continue to reflect racial hierarchies. The cyborg then becomes not a figure of liberation, but a mirror of existing inequality.

This shows that digital bodies are not free from race. Instead, they are constructed through social power structures that privilege whiteness. Challenging this requires not just better representation, but a deeper examination of who designs technology and whose identities are treated as default. References: GSS320 PPT K. O’RIORDAN - GENDER, TECHNOLOGY AND VISUAL CYBERCULTURE J. DANIELS - GENDER, WHITE SUPREMACY AND THE INTERNET D. HARAWAY - A CYBORG MANIFESTO: SCIENCE, TECHNOLOGY, AND SOCIALIST

[unsupervisedmachine]

Blog post #7 - Week 10 (late)

How does the methods trolling differ from a troll created from their environment compared to a troll for fun.

Trolling is often treated as a uniform behavior, but the readings show that its causes and forms are shaped by different social and cultural environments. The “Origins of Trolling” reading explains that trolling didn’t begin as malicious it was often a kind of internet performance art used to expose gullibility, irony, or group norms. Early trolls operated in spaces like 4chan or message boards where anonymity and wit were valued, and their actions were often about “for fun” provocation. These trolls prided themselves on clever manipulation and emotional detachment, following an unspoken rule that “nothing is serious online.” However, the readings show that the environment around trolling has changed. Over time, the same online spaces developed cultures that rewarded cruelty, dehumanization, and coordinated harassment. In these environments, trolling became a learned social behavior—one that reinforces belonging through shared hostility.

Danielle Citron’s work on cyber harassment shows how people who grow up or participate in toxic online subcultures are shaped by norms that normalize hate, misogyny, or racism. These trolls aren’t acting randomly or for simple amusement; they are reflecting the values of their digital surroundings, which can glorify dominance and humiliation. For example, in the Leslie Jones case, the trolling escalated far beyond “fun” teasing—it became a coordinated racialized attack designed to intimidate and silence her. This type of trolling depends on reinforcement from others; it’s a product of environment and collective cruelty.

In contrast, “fun” trolls might claim neutrality, arguing that they’re just experimenting with reactions. But the line between “fun” and harm is blurry, as “Don’t Feed the Trolls” explains. Even playful trolling can quickly evolve into abuse when others join in or when the target perceives real harm. Ultimately, trolling for amusement and trolling shaped by environment differ in intent and awareness—but both reveal how online culture teaches people what kinds of cruelty are acceptable.

Would it be possible in the future for the government to step in and impose regulation on "trolling activities?" Does this impose on our free speech?

Regulating trolling raises complex questions about law, speech, and power. The challenge lies in defining trolling. The term can describe harmless pranks, but it also includes organized harassment and threats. Governments can’t realistically outlaw “trolling” in general, because it’s too broad and would risk infringing on protected speech. However, specific behaviors associated with trolling—like stalking, doxxing, or targeted harassment—are already illegal in many jurisdictions. Danielle Citron argues that these acts should be treated as serious civil rights violations, because they limit people’s ability to participate in online spaces safely and equally.

The Leslie Jones incident demonstrates how trolling can cross into criminal territory. When her private information was leaked and she was subjected to thousands of racist attacks, that wasn’t mere online provocation—it was a campaign of intimidation. This shows that while not every trolling act deserves legal punishment, certain patterns clearly fit the definition of harassment and should be enforceable under existing laws. The Online Harassment Report adds that victims often face real-world consequences like job loss, mental health decline, or withdrawal from public life, showing that “online” harm is not separate from offline impact.

The question of free speech remains central. Critics worry that regulating trolling could give governments or platforms too much control over discourse. Yet Citron and others counter that speech laws already draw lines—like against defamation or true threats—so enforcing harassment laws online isn’t censorship but protection. Free speech doesn’t mean freedom from accountability. The key is proportionality: ensuring that the law targets conduct rather than unpopular opinions.

In the future, regulation may rely more on partnership between platforms and policymakers rather than strict government control. Platforms can adjust algorithms and reporting systems to reduce abuse without outlawing speech. In that sense, meaningful regulation is possible, but it must focus on safety, not silencing.

Is it possible to troll "for good"?

The idea of “trolling for good” is controversial, but the readings suggest that intent and direction matter. “Don’t Feed the Trolls” describes trolling as a strategy that can sometimes be used to provoke reflection or expose hypocrisy. Historically, trolling had playful or even satirical roots, acting like the internet’s version of a court jester. Some modern activists use trolling techniques—like parody accounts, memes, or ironic posts—to challenge misinformation or hold the powerful accountable. For example, coordinated “counter-trolling” campaigns have been used to drown out hate speech or disrupt extremist organizing spaces. These efforts echo the older, more performative side of trolling, where provocation was meant to question norms, not destroy people.

However, the line between “good” and “harm” is fragile. As Citron points out, the emotional toll of humiliation or exposure doesn’t vanish just because the troll’s motives seem noble. Even when trolling aims to educate or shame a wrongdoer, it can replicate the same patterns of mob behavior it criticizes. The Leslie Jones timeline illustrates how collective online action can spiral, regardless of intent. What starts as calling out racism can become another form of mass hostility. “Good” trolling risks validating the very tactics that enable abuse.

Yet it’s too simple to say trolling must always be bad. The Origins of Trolling reading reminds us that the practice is deeply cultural—it reflects how communities communicate. If trolling for good means using irony, humor, and subversion to challenge oppressive systems rather than individuals, it can function like digital activism. Still, this approach demands self-awareness and ethical boundaries.

Would trolling be different or even non existent if people using the internet are more exposed? Would be people still troll if the internet had a way to know who you are?

The Origins of Trolling reading shows that anonymity once encouraged creativity and risk-taking online. It allowed people to experiment with identity and humor without fear of social judgment. But as online communities grew, that same anonymity became a shield for cruelty. “Don’t Feed the Trolls” and the Online Harassment Report both note that the ability to act without accountability amplifies harmful behavior. People say things online they’d never say in person precisely because their names and reputations aren’t at stake.

If users were more exposed—say, through verified identities or visible online histories—some trolling would likely decline. Public accountability tends to moderate behavior. Citron’s analysis suggests that exposure could reduce targeted hate campaigns by making it easier to trace perpetrators and enforce consequences. However, exposure is not a cure-all. Even in platforms with big platforms, like Facebook, harassment persists. Many trolls feel emboldened by group dynamics or ideological justification, not just anonymity. When cruelty is socially rewarded, visibility doesn’t necessarily discourage it.

Moreover, removing anonymity can endanger vulnerable groups. Whistleblowers, activists, or marginalized individuals often rely on anonymity for safety. If all users were fully exposed, the internet could become less inclusive and more surveilled. The Leslie Jones case shows that even visible, famous individuals aren’t protected by identity exposure; visibility can make them more of a target.

Therefore, while reducing anonymity might prevent some impulsive or cowardly trolling, the deeper causes—cultural normalization of abuse, lack of empathy, and platform design—would remain. Real change would require shifting online norms to reward respect and empathy, not outrage and mockery. Trolling might decrease with exposure, but it wouldn’t disappear until the online environment itself stops rewarding cruelty.

References: W. PHILLIPS - DEFINING TERMS: THE ORIGINS AND EVOLUTION OF SUBCULTURAL TROLLING K. BERGSTROM - DON’T FEED THE TROLLS: SHUTTING DOWN DEBATE ABOUT COMMUNITY EXPECTATIONS ON REDDIT.COM

D.K. CITRON - HATE CRIMES IN CYBERSPACE GSS320 PPT - Leslie Jones Incident

[unsupervisedmachine]

Blog post #9 - Week 12 (late)

How do we know “identity” being treated like something you have to perform and defend online rather than something you simply “are”?

Across the readings, identity shows up less like a stable label and more like an ongoing project that people build, explain, and protect especially under pressure. In Lee’s chapter, Black women’s hair becomes a site where “professionalism,” “respectability,” and “acceptable womanhood” get enforced through institutions like schools and workplaces. The examples of Ashley Davis, Tiana Parker, and Vanessa Van-Dyke show that the issue isn’t just personal preference; it’s that Black hair in its natural state gets framed as a problem that must be corrected, and Black girls/women are singled out for punishment. That means identity (Black womanhood, embodiment) is being regulated, not just expressed. What makes the “virtual homeplace” idea powerful is that it reframes identity as resistance. Hair blogs aren’t only about styles, they become spaces where Black women share knowledge, rebuild self-definition, and push back against the message that their bodies are “unprofessional” or “distracting.” The community helps people rehearse and reinforce a counter-story: my natural body is enough. Gamergate adds a different identity dynamic: “gamer” identity is described as something people felt needed defending when the culture shifted and when critiques of sexism/racism entered mainstream gaming discussion. The explainer shows how the movement publicly claims “ethics in game journalism,” but it begins with intense harassment of a woman developer and then “pivots” into a more socially acceptable justification. That pivot is a kind of identity defense strategy: it converts raw hostility into a moral narrative (“we’re the ethical ones”) that tries to protect a threatened in-group status.

What do these readings say about our “attention hungry" society and how do attention, profit, and harm feed into each other?

McLaine is the most direct about this. He explains how ethnic online communities (EOCs) get evaluated through measurable engagement metrics: visitors, time on site, page views, posting ratios, and so on. He even notes the pride around being “sticky” (users spending lots of time on the site). But he argues that this creates pressure to prioritize what’s profitable and measurable over what’s socially meaningful. Purpose (empowerment, advocacy) is harder to quantify than ad revenue, so the business incentives can quietly reshape the community’s priorities. Lee shows similar tension inside a “safe space” model. Hair blogs can be healing and supportive, but they can also become sites where Black women are targeted as consumers. She notes how the search for “natural/organic” products can become expensive and how marketing inside the blog space can contribute to economic exploitation, while DIY techniques sometimes push back against that pressure. So even nurturing communities can get pulled into attention-driven consumption. Gamergate shows attention operating as amplification of harm. The harassment campaign becomes coordinated across platforms, and the story emphasizes how it escalates through visibility and repetition: conspiracy narratives circulate, doxxing happens, threats intensify, and the target is forced to leave her home. Once a harassment wave becomes “content,” it spreads because it generates reactions regardless of accuracy or ethics. Taken together, the readings point to a feedback loop: when attention gets rewarded, platforms and communities start chasing whatever generates the most clicks and engagement. But what spreads fastest is often outrage, conflict, and consumer hype so the environment becomes more vulnerable to harassment, manipulation, and exploitation. That’s why the Week 12 deck pushes you to question the boundary between profit and purpose: it isn’t only about “good vs. bad intentions,” it’s about the incentive structure and what behaviors online systems are designed to amplify and pay off. How might the same tools used for community-building (hashtags, sharing, networking) also enable coordinated harm?

The same tools that help people find each other can also help people target each other. Hashtags, sharing, and networking reduce friction: they make it easy for supportive communities to mobilize quickly which also means it’s easy for harassment groups to mobilize quickly. Hashtags are basically discovery engines. A hashtag can gather resources, stories, and solidarity in one place, but it can also act like a beacon for attackers: it tells them where the conversation is happening and who is participating. That’s why marginalized people can become visible in ways that are empowering and risky at the same time. Sharing and reposting work the same way. Sharing can amplify a cause, but it can also amplify outrage and misinformation, or “broadcast” a target to a wider hostile audience. Once a post becomes a screenshot or a quote-tweet pile-on, it can turn into a recruitment tool for harassment (“everyone go look at this person”). Networking features (followers, recommendation systems, group invites) can create supportive networks, and can also create coordinated networks of attackers. In harassment campaigns, coordination often doesn’t need explicit planning; it can happen through signals (“this person is the problem”) and platform mechanics that route attention to the target. So the core issue isn’t that the tools are “good” or “bad.” It’s that they scale coordination. If the platform rewards high-arousal engagement, those tools will be used to scale harm. If the platform builds in friction, protects targets, and refuses to monetize outrage, those same tools are more likely to scale support instead.

If I had to design a healthier community platform based on these readings, what would I build differently and why?

From these readings, I would build the platform around safety and dignity first These readings show that community spaces can be healing (Lee’s virtual homeplace idea) but also easily turned into harassment networks (Gamergate), and they can get pulled toward extraction when profit and data become the core logic (McLaine). So the design changes would focus on incentives and power. First, I’d minimize data collection and make privacy the default. If the business model depends on detailed profiling, the community starts treating people like market segments. A healthier platform would collect only what’s needed to function and give users real control over discoverability and visibility (especially important in identity-based spaces). Second, I’d design against dogpiles and harassment virality. That means friction for mass targeting: rate limits on replies to a single person, restrictions on brigading behavior, and strong anti-doxxing enforcement with fast response tools. The goal is to prevent “attention” from automatically amplifying harm. Third, I’d make moderation community-led but well-supported. The readings imply that safe spaces don’t just “happen”, they’re maintained. So I’d provide transparent rules, appeals processes, and tools that reduce moderator burnout (queue triage, shared mod teams, and the ability to lock threads when piling-on starts). Fourth, I’d redesign metrics so the platform rewards constructive participation. Instead of optimizing for engagement volume, I’d reward things like helpfulness, accountability (citations, context), and conflict de-escalation. If what the system “pays” users with is visibility, then visibility should be earned through community benefit. Finally, I’d support “bounded spaces” intentionally: private or semi-private groups where marginalized users can control membership and norms without being forced into public exposure. That keeps the “homeplace” function possible without turning the whole platform into a surveillance-heavy gated community. References: L. Lee - Women of Color and Social Media Multitasking Blogs: Virtual Homeplace

S. MCLAINE - ETHNIC ONLINE COMMUNITIES: BETWEEN PROFIT AND J. HATHAWAY - WHAT IS GAMERGATE, AND WHY? AN EXPLAINER FOR NON-GEEKS

[unsupervisedmachine]

Blog Post #8 - Week 11 (late)

Would the Black Twitter community still find a way to get their voices heard if the app "X" didn't exist? What other means would they use?

Black Twitter has never been defined only by the technology it uses but by the social, political, and cultural needs that made the space a main platform in the first place. Even if X/Twitter disappeared tomorrow, the functions Black Twitter fulfills would still demand a home. As Lee argues, Black Twitter operates as a “digital homespace,” a place where Black users challenge harmful media portrayals and reshape public narratives about Blackness. That kind of collective meaning-making is not bound to a single platform; it is rooted in the long history of Black communities creating alternative channels when dominant institutions fail or misrepresent them. Historically, marginalized groups have always developed new communication networks when excluded from mainstream media—whether through community newspapers, listservs, encrypted digital forums, or independent online publications. Online dissident groups have used alternate networks to bypass censorship and controlled media systems, showing that the impulse to create counterpublics emerges whenever dominant media fails to serve marginalized voices. Similarly, Black Twitter users today already demonstrate platform fluidity: conversations migrate across TikTok, Instagram, Spill, Threads, and Bluesky depending on context, features, and community needs. The reason the term “Black Twitter” persists is not because of Twitter itself, but because of the shared cultural practices—hashtagging, collective storytelling, humor, political critique, and resistance to bias—that anchor the community. If anything, the decline of X has already shown how quickly Black users adapt, continuing to build spaces where cultural expression and political response can thrive without dependence on one corporation. Therefore, Black Twitter would continue to exist in function even if the original platform vanished; its community, practices, and purposes are what define it, not the app hosting it.

How does Black Twitter illustrate Vegh’s three forms of online activism: awareness/advocacy, organization/mobilization, and action/reaction?

Black Twitter provides a vivid example of Vegh’s three-part model of online activism: awareness/advocacy, organization/mobilization, and action/reaction. In the awareness stage, Black Twitter functions as a counter-narrative engine, challenging biased or incomplete media portrayals. Hashtags like #SayHerName, #BlackLivesMatter, and #APHeadlines help expose systemic racism, reinterpret news stories, and highlight injustices that mainstream outlets often frame through stereotypes. Lee’s discussion of Black Twitter shows how users use humor, critique, and reframing to “speak back” to media narratives, creating alternative interpretations that resist centuries-old depictions linking Blackness with criminality. This aligns with Vegh’s observation that online activism often begins with distributing information outside traditional media channels to raise public consciousness. Moving to mobilization, Black Twitter has shown its capacity to convert online dialogue into coordinated communal action. Users employ hashtags not only to discuss issues but also to organize protests, mutual aid efforts, and fundraising campaigns. This parallels Vegh’s description of how activists use digital tools to coordinate large numbers of people efficiently, leveraging communication networks to prepare for offline or hybrid actions. The action/reaction stage becomes visible when institutions or media outlets respond directly to pressure generated by Black Twitter. The Associated Press changing its misleading headline after widespread critique, as Lee documents, demonstrates the power of collective online resistance to force institutional accountability. Vegh notes that such actions can influence public narratives and trigger broader social consequences, even if they do not always achieve large-scale structural reforms. Black Twitter’s ability to generate viral responses shows how online communities can use digital platforms to create meaningful pressure. Black Twitter represents all three dimensions of Vegh’s framework, demonstrating how online spaces can serve as sites of awareness-raising, coordination, and resistance. Does social media create movements, or does it amplify deeper conflicts?

The #NoDAPL movement demonstrates both the transformative power of social media and the limitations of relying on digital activism for long-term structural change. On one hand, Parkhurst’s account shows how hashtags like #NoDAPL, #StandWithStandingRock, and #MniWiconi (“water is life”) connected people across geographic, cultural, and political boundaries. Social media enabled the movement to reach far beyond mainstream news outlets, which initially underreported Indigenous perspectives and the environmental impacts of the Dakota Access Pipeline. As Parkhurst describes, online visibility fueled global solidarity, drawing thousands of supporters to the Oceti Sakowin camp and helping Indigenous activists reclaim their narrative on their own terms. Social media served not just as a communication tool but as a mechanism for disrupting colonial narratives and challenging governmental processes that ignored tribal sovereignty. This reflects one of the platform’s primary strengths: its ability to create alternative publics where marginalized voices can challenge dominant narratives. However, Parkhurst also highlights significant limitations. Social-media-driven movements often face attention cycles that rise quickly and fade just as fast. Despite global visibility, political institutions and corporate interests were not fully swayed; the pipeline ultimately moved forward, demonstrating that awareness does not guarantee policy change. Parkhurst suggests measuring success not by whether the pipeline was stopped but by the ways the movement disrupted colonial systems, raised consciousness, and centered Indigenous worldviews. This shift in perspective acknowledges the reality that digital activism can generate massive symbolic power while still encountering structural barriers. In addition, online spaces are vulnerable to misinformation, surveillance, and harassment—issues that Indigenous activists faced during #NoDAPL. While social media enabled solidarity, it also exposed activists to state monitoring and digital attacks. Therefore, the movement illustrates both the potential and the constraints of digital activism: it can build powerful visibility and solidarity but cannot, on its own, overcome entrenched political and economic systems. How do movements like Black Twitter, the Arab Spring, Occupy, and #NoDAPL use communication power, and how does platform control shape their effectiveness?

Across social movements, communication power refers to the ability to shape narratives, coordinate collective action, and challenge dominant interpretations of events. Movements like Black Twitter, the Arab Spring, Occupy, and #NoDAPL rely heavily on this power to counter mainstream media, mobilize participants, and expose injustices. In Black Twitter, communication power emerges through the rapid creation of hashtags, collective commentary, and narrative reframing. Lee shows how users deploy humor, critique, and direct challenges to biased media, turning the platform into a space where marginalized voices can reconstruct their own representation. During the Arab Spring and Occupy, as Fuchs explains, activists used social media not as the origin of protest but as a tool to publicize state violence, coordinate demonstrations, and energize networks of dissent. The #NoDAPL movement similarly used communication power to bring Indigenous sovereignty and environmental justice to the global forefront, leveraging social media as a way to challenge colonial systems and document state repression. Yet the effectiveness of this communication power is shaped by who controls the platform. Social media sites are owned by corporations whose priorities do not always align with activism. Algorithms can deprioritize or hide content, disproportionately affecting marginalized groups. Government actors can use these platforms for surveillance, while corporations may perceive certain activist strategies—such as hacktivism, according to Vegh—as security threats. These power dynamics complicate digital activism: while platforms enable unprecedented visibility, they also impose structural constraints on which voices are amplified or suppressed. Movements become vulnerable to algorithmic bias, content moderation policies, misinformation campaigns, and the commercial interests of platform owners. Thus, communication power is both a resource and a site of struggle: activists can shape narratives and coordinate resistance, but their ability to fully leverage digital tools is limited by corporate governance and state intervention. The success of these movements often depends on how effectively they navigate these intertwined systems of empowerment and restriction.

[unsupervisedmachine]

Blog post #4 - Week 6

How do digital platforms hide the human labor behind algorithmic recommendations?

The ethical landscape of digital labor becomes clearer through Black Mirror’s episode “Nosedive,” where social interactions turn into a constant performance for merit approval. This mirrors what Kolko, Nakamura, and Rodman describe as the illusion of neutrality in cyberspace, where race and class seem distant but are deeply structured into online interactions. Just as Lacie in Nosedive must curate every gesture for a high rating, invisible human workers curate, moderate, and filter digital content for global corporations, yet remain unseen. Nakamura’s work on racialized labor online reminds us that ethical invisibility is itself violence when the data workers behind AI systems, are hidden from the platforms they monitor.

In what ways does invisible digital labor reveal modern forms of digital exploitation?

Invisible digital labor reveals that exploitation in the digital era no longer requires that it hides inside code, platforms, and screens. Platforms like YouTube, TikTok, and ChatGPT rely on vast networks of unseen workers who train AI models, filter disturbing content, or tag images so that algorithms “learn.” This labor is often outsourced to workers in the Global South under low pay and psychological stress, yet it sustains what users perceive as “automated” intelligence. Lisa Nakamura describes this as the illusion of “frictionless technology,” where convenience masks the racialized labor behind digital systems.

How do or can digital technologies empower voices through algorithmic bias?

Ow’s critique of the “yellowfaced cyborg” warns how being mixed becomes colonizing under white control. But by reclaiming hybrid spaces through community-driven moderation, feminist AI design, or algorithmic audits marginalized creators assert agency over how they’re represented. For example, feminist coders use algorithmic literacy to expose and rewrite biased systems. Bias becomes a tool for ethical redesign, not just for being on one side. Once bias is recognized, it can be redesigned.

How do companies’ marketing narratives hide the systems that profit from unpaid or underpaid digital labor?

Platforms like Instagram and YouTube rely on user-generated content where millions of unpaid creators producing entertainment, data, and engagement. Their work fuels advertising revenue, yet they rarely share in the profit. Lisa Nakamura’s research on “cybertyping” shows how this model racializes invisibility. People of color often produce culture that corporations monetize but don’t credit. In this sense, “free expression” becomes a form of unpaid labor.

[unsupervisedmachine]

Blog post #2 - Week 3

1. How do algorithmic systems function as technologies of racialized domination that reproduce anti-Black racism across multiple institutional domains, and what does this reveal about the relationship between technological "progress" and structural oppression?

The three sources provide overwhelming evidence that algorithmic systems operate as "the surveillance of Blackness" - not neutral tools but mechanisms of racialized control embedded across healthcare, policing, employment, and social services. The video's healthcare example is particularly revealing: the Optum algorithm used future healthcare spending as a proxy for illness, systematically deeming white people "more ill" because they spent $1,800 more annually on healthcare than Black people. This algorithm reproduces health disparities without even explicitly using race as a variable, demonstrating how structural racism gets encoded into seemingly race-neutral metrics.

The policing examples across sources show escalating algorithmic violence. Chicago's "heat list" and Strategic Subject List marked residents, including children, as likely criminals before any crime was committed. Police made visits to inform these predominantly Black citizens they were "being watched" - a digital scarlet letter based on algorithmic suspicion. Combined with the facial recognition failures that led to Nijeer Parks, Robert Williams, and Michael Oliver's wrongful arrests, we see algorithms operate in what Brown calls "racist Minority Report" - predictive policing that criminalizes Blackness itself.

The insurance algorithm in Eubanks' work adds another layer, showing how automated systems police the boundaries of "deserving" and "undeserving" populations. When Jason and his partner triggered fraud investigation for accessing emergency care after a hate crime, the algorithm transformed their vulnerability into suspicion. As Eubanks argues, these systems create a "digital poorhouse" that manages poverty through surveillance rather than addressing its causes. The video speaker's crucial insight that "algorithms are merely an efficient abstraction of our society's values and beliefs" reveals these aren't technological failures but faithful reproductions of anti-Black logics. When HireVue uses AI trained on "overwhelmingly white and male" datasets to evaluate job candidates, or when Ring and NextDoor enable digital mob surveillance of "suspicious people who don't belong," technology doesn't eliminate bias - it scales it up, makes it more efficient, and wraps it in false objectivity.

2. What ideological work does the myth of "objective data" and "neutral algorithms" perform in obscuring the fundamentally political nature of automated decision-making, particularly regarding anti-Black racism?

The sources expose how claims of algorithmic neutrality function as what the video speaker calls a "cloak" for anti-Black bias, allowing institutions to perpetuate discrimination while denying responsibility. The insurance company's insistence that denying coverage was merely "a technical glitch" - "missing digits in a database" - exemplifies this ideological maneuver. Technical language depoliticizes fundamentally political decisions about who deserves care, transforming systemic denial into unfortunate accidents. When customer service representatives repeatedly tell the author "the system said" coverage hasn't started, they invoke the algorithm as an unquestionable authority, obscuring human choices about system design and implementation.

Law enforcement's claim that facial recognition provides only "a clue" that "will not lead directly to an arrest" is exposed as ideological cover by Parks's experience. Once the algorithm suggested a match, human actors rubber-stamped its decision - the detective "agreed it was the same person," a single Hertz employee's confirmation sufficed for arrest. The ideology of human oversight masks how algorithms shape what humans can see and validate. As the video notes, predictive policing scores appear on police dashboards during traffic stops, but citizens don't know their scores or how they're calculated because algorithms are "proprietary black boxes." This opacity isn't a bug but a feature - it prevents challenging the system's logic.

The healthcare algorithm reveals perhaps the most insidious ideological work: using "proxies" that appear race-neutral while encoding racial hierarchy. By using healthcare spending to measure illness severity, Optum could discriminate against Black patients while claiming colorblindness. The video speaker's observation that "big data doesn't make better data" challenges the ideology that more information automatically produces better outcomes. Similarly, Daniels' critique of cyberfeminism exposes how even liberatory discourses can serve ideological functions. When Haraway romanticizes "cyborg women making chips in Asia," she transforms exploitation into hybrid futures, using technological utopianism to obscure material suffering. The persistent claim that technology will "eliminate human bias" serves what the video identifies as society's refusal to "address anti-blackness" - if we can't acknowledge racism in the analog world, we certainly won't recognize it in the digital abstraction of that world.

3. How does the relationship between algorithmic surveillance and embodied resistance reveal contradictions in both cyberfeminist theories of liberation and mainstream narratives of technological progress?

The sources reveal a fundamental dialectical tension: technology simultaneously promises to transcend bodily limitations and social categories while intensifying surveillance and control of racialized bodies. The video's examples of facial recognition demonstrate this perfectly - these systems claim to identify individuals objectively while systematically failing to "see" Black faces accurately. Joy Buolamwini's finding of 35% error rates for dark-skinned women reveals the contradiction: Black bodies are simultaneously invisible to the algorithm (it cannot recognize them) and hypervisible to the system (they face increased surveillance and false arrests). This technological failure doesn't protect Black people from surveillance but subjects them to its violence more arbitrarily.

Daniels' critique illuminates another dimension of this dialectic. While cyberfeminists celebrate "disembodiment" and "identity tourism" as liberation, actual practices reveal the persistent centrality of embodied experience. Pro-ana communities use technology not to escape their bodies but to share "thinspiration" and maintain bodily control. Trans women like Anita use "tranny hormone lists" and surgical photos to transform their material bodies, not to play with virtual identities. The insurance algorithm case shows how Jason's brutalized body - jaw broken, eye sockets shattered - generates the very data (emergency visits, pain prescriptions) that algorithms interpret as fraud. The dialectic reveals bodies never disappear online; they're transformed into data weaponized against those whose suffering it represents.

The video adds crucial complexity by showing how communities use the same surveillance technologies for resistance. Black Lives Matter and #MeToo movements used social media for organizing, while the welfare rights movement deployed consumer credit as what the speaker calls "Black feminist technology." Yet these resistance strategies operate within severe constraints. HollaBackNYC's use of phones to photograph harassers represents creative resistance that potentially reinforces surveillance of minority men. Chicago residents on the heat list couldn't opt out of their algorithmic marking. The dialectical tension isn't between pure domination and pure resistance but between different configurations of power within technological systems. As the video concludes, "we can't hack our way out of anti-Black racism" - technology neither saves us nor simply oppresses us but reconfigures existing power relations in complex ways.

4. What forms of collective resistance emerge from communities targeted by algorithmic violence, and how do these struggles expose contradictions in the political economy of surveillance capitalism while prefiguring alternative technological futures?

From the readings, they reveal diverse resistance strategies that expose system contradictions while brainsstorming alternatives. Individual resistance appear in long battles against algorithmic decisions - Parks and Oliver filing lawsuits, the author fighting insurance denials for a year, Chicago residents attempting to get off the heat list. These struggles reveal the exhausting nature of fighting automated systems designed to outlast humans. They also produce critical knowledge: Parks's case exposed facial recognition failures, the insurance battle revealed algorithmic red-flagging, Chicago's Strategic Subject List was eventually abandoned when "grant money ran out," revealing these systems' dependence on continued funding for surveillance.

The video highlights how Black women in the welfare rights movement treated consumer credit as technology for economic justice, showing how marginalized communities have always repurposed tools of capitalism for survival and resistance. Contemporary movements demonstrate similar innovation: Black Lives Matter using hashtags and livestreaming to document police violence, #MeToo creating databases of perpetrators, Scholar Strike organizing academic boycotts.

The political economy underlying these struggles reveals fundamental contradictions. The video notes algorithms are "cultural products" reflecting "society's values and beliefs" - but whose society, whose values? The Optum algorithm serves insurance companies seeking to minimize payouts. Predictive policing serves a carceral state requiring bodies to fill prisons. HireVue serves corporations maintaining white male workforce dominance. These aren't public goods but private products selling discrimination as efficiency. Yet resistance must use these same platforms - activists need Facebook to organize against Facebook, communities need GoFundMe to survive algorithmic denial of services.

The video's insight that "before we address anti-blackness in the digital we have to at the very least acknowledge its existence in the analog" points toward necessary preconditions for technological justice. Communities are already prefiguring alternatives: mutual aid networks replacing algorithmic welfare systems, community-controlled databases countering proprietary black boxes, scholarly exposure of algorithmic violence creating pressure for regulation. These aren't complete solutions but demonstrations that other technological worlds are possible - ones that serve human flourishing rather than reproducing domination.

[unsupervisedmachine]

Blog Post Week 2

Why Didn't Technology Live Up To The Hype?

Since the birth of technology, its main purpose was a to simulate 'physical brain' where people can refer to and connect similar ideas/concepts with contraptions such as the Memex. Nowadays, with technology becoming more accessible to people around the world as long as they have a network connection. It's original purpose began to slowly fade away as society began to see it as a means to gain more influence over people. Not only that technology evolved into a much more robust tool some people are now seeing it as a another means of entertainment and thus, technology was not seen as tool but more as a business. As a result, more "internet" jobs began to appear such a livestreaming or content creating where creators can make more than doctors. As time went on, this new frontier had diluted the reliability of the internet, as people began to create more memes and posts as more of a means of entertainment than for gaining knowledge. People began to not take the internet for granted because of the lack of credibility it showed. For example, our generation (Gen Z) was born around the early 2000 so we were in a place of technology just evolving into something bigger than just a big database. That was when youtube and facebook emerged and then memes began to appear. I believe this turning point changed the way we looked at the internet. Looking ahead, we can expect a continuous cycle of new technologies and the consequence of exploitation and commercialization. When corporations see an opportunity in emerging technologies such as AI, they typically move quickly to capitalize on them. However, enforcing regulation and policy often lag significantly behind technological development, creating a gap where new innovations operate with minimal oversight. This regulatory delay contributes to public skepticism and hesitation around adopting new technologies, as users lack clear guidelines about safety, privacy, and ethical implications. Technology's main purpose has completely changed since the beginning and it will always change depending on how people and companies and see the potential consumer impact it can have.

[unsupervisedmachine]

I'm Surprised...

With AI taking over the world by storm, I haven't been seeing any movement in the personal robot sector. I want my own Jarvis..