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












