Tag: fall-2019

Fountain Hughes’s Narrative

For this assignment, I listened to an interview with Fountain Hughes, conducted by Hermond Norwood. Over the course of the thirty minute interview, I was able to notice the value of his experience, and the ways that his experiences did and did not align with my expectations for his narrative.

Here is the timeline that I created based on Fountain Hughes’s interview.

Learning from a narrative

I believe that there is a lot of value within Fountain Hughes’s experiences. Through this interview, historians receive a firsthand account of what it was like to be an enslaved person in the United States during this time. Something that stood out as particularly poignant during the interview was how Fountain discussed slavery in terms of the 1940s. He speculates that some people now “would rather be slaves.”1

Fountain notes that he would rather kill himself than go back to being a slave. He notes that they were treated like dogs, and would never go back to that existence. This directly contradicts the narrative of the jovial slave that was spread by people in the argument that slavery wasn’t as bad as it seemed. Fountain Hughes claimed to be more than one hundred years at the time of this interview and had not been enslaved for more than eighty years. Still, the trauma of slavery stuck with him so strongly that he would not hesitate to resist in the most severe and permanent manner.

Additionally, Hughes’s narrative is valuable as a man who originally lived in the South before moving to Baltimore, leading to a unique construction of southern identity. In particular, his discussion of going to church in a log cabin and being led by an older preacher in the singing of gospel songs, adds to the idea of a southern identity2

I was actually surprised by Fountain Hughes’s reluctance to talk about some of the specific hardships he endured as a result of his enslavement and status both during and after the Civil War. He says he doesn’t like to talk about it because “it makes people feel bad.”3 While the goal of chronicling the narratives of enslaved people as a part of the Federal Writer’s Project was to understand what enslaved people endured4 , one can not account for someone who doesn’t feel comfortable sharing what they went through.

Still, this interview has limitations. As a result of Fountain Hughes’s advanced age, his memory of events may not be as accurate as it once was. Also, it is limited by his previously discussed reluctance to discuss topics that he believes will bring others down. Obviously, there is no reason to believe that Fountain Hughes is intentionally telling any mistruths, but as a result of his age, some aspects of context may have been lost to time. For example, during the interview he describes an occasion when, as a youngster, he and his brother slept in an automobile. But, based on his age, Fountain would have already been in Baltimore by the time automobiles became commonplace in this country. He may have been referring to a more recent event with his brother, or he may have been intending to say they slept in a place that was “like” or “the size of” an automobile.

Finally, I was a bit surprised by how much Fountain Hughes’s depiction of his own life relied on broader events as signposts. Multiple times during the interview, Fountain remembers an event by President Garfield being assassinated, or someone’s death happening during or after the Civil War, or how hard life was when he and his family had just become free. Currently, I don’t remember my own life in these kinds of terms. Then again, I’m 21 and not 101.

Blog Post 4

Algorithms are sets of rules in data that are often used today but these data sets are not free from unfair use or biased use. Cathy O’neil simply defined algorithms as ‘opinions embedded in math’. She states that these algorithms are viewed as objective and unbiased because its math and numbers and those are free from bias but the way algorithms are built allows for those mathematicians and data set builders to implement their own idea of success into the algorithms.3 Because the definition of success is different for every person each algorithm can come up with different results. These algorithms in modern day use allow for inherently racist and sexist ideals to be prevalent in modern media. Search engines, like Google, in particular have been one of the most problematic in having these racist or sexist algorithms as stated in Noble’s book, Algorithms of Oppression.5 The search results for black girls and other minority women come up with results that both objectify them and have a very sexist point of view. This is something that Google’s search algorithm allows for in mass media.


Feminism in the 1930’s was one of the better things about that decade. In a time where the Great Depression struck most people very hard, feminism, coming of the heels of the success of the women’s suffrage movement, progressed forward. The monetary struggles of the 1930’s pushed women to get jobs and provide monetary support for their families which was very rare until this point. This got women out of the house and gave them more freedom socially.


WPA era activists and modern activists share a common goal of pushing for equality for everyone across the board. They both utilize the power of media to get their message out. Granted, modern activists have the luxury of the internet and have easier access to more people and this can aid their movement but WPA era activists had the same intent and utilized their own skills and resources. Algorithm critics are part of that modern activist movement as they are pointing out the biases in these algorithms that most people do not recognize and calling for that change to create a better and more unbiased future.

Blog Post 4

At a time when the federal government was first seen shifting its priorities to create the basis of a welfare system, there was also the inescapable question of who is deserving of these new rights and benefits. Franklin Roosevelt not only failed to address the difficulties that Black Americans faced, but he also continued the institutionalization of racism with federal government policies. Roosevelt rejected the proposal to abolish the poll tax and declare lynching a crime. But the most visible codification of racism in federal law came with the removal of domestic workers and farm laborers from the provisions of the Social Security Act.6

Although, the Long Civil Rights movement and the popular front impacted the 1930s by helping create a more equitable society for the marginalized when the federal government failed to do so. The activists from the LCRM and the popular front fought to expand the nation’s immaterial boundaries of who is a citizen worth fighting for and I would argue that this is the same struggle that digital activists are fighting today. Marginalized groups are still scrutinized and discriminated against, except this time it’s less identifiable who’s doing the discrimination and how.

Safiya Noble argues that the “traditional misrepresentations in old media are made real once again online and situated in an authoritative mechanism that is trusted by the public: google.”7. Noble’s keyword search of “black girls” represents how algorithms can perpetuate harmful misrepresentations and the dehumanization of people.8 Despite the fact that marginalized groups are the ones who are being exponentially affected by Google, I would argue that this is a human rights issue that everyone should be concerned about. Anyone using the internet today, whether a target of the state or not, is trading in their privacy, personal information, and most importantly their immaterial labor.

As of right now, the general public is not aware of how algorithms are made and there is a common assumption that the mathematical language of the algorithm would be a barrier to understanding it. Yet, the foundation and design of algorithms can be understood without having to learn a mathematical language. According to Cathy O’Neil, to build an algorithm you need a historical data set and a definition of success. At its core, those are the only two things that need to be understood to get a general grasp of algorithms. Algorithmic literacy must be taught to the public in order to transform artificial intelligence into a less problematic and dangerous entity.  

Algorithms and Feminism

I was not aware of the effect of inaccurate algorithms on society. Cathy O’Neil explains algorithms in her talk “The Truth About Algorithms”. She defines an algorithm as basically mathematical opinions and all that is needed for form them is a data set and a definition of success. Cathy makes an interesting point when she explains how her definition of a successful meal differs from her sons definition. Algorithms can be racist, sexist and bias and completely untrue. It is necessary to try to develop a way for these algorithms to be fair. People can lose their jobs, lose money and much more because of a faulty algorithm. Companies are now having the idea of being transparent in their algorithms. Basically so individuals can see how the algorithm is made and on what terms and bases. But in my opinion I don’t see how this is being completely fair. People can now see what they were judged on but also is that judgement is still coming from an opinion? I am open to the idea and I understand how being transparent is better than being left in the dark.

In the 1930s and into the 1940s women were just starting to emerge as workers. The American Yawp explains how during this time women began to take roles in office and Eleanor Roosevelt was a strong first lady who took charge and strived for change. Although even with the few successes of women in office the New Deal programs were essentially built for men. The spark for change was lit in the 1930s and has continued to burn ever since. Women are still fighting for equal rights and I believe will continue until successful. Pay differences in the workplace is one of the main changes I hear about still. 

Blog post #4

The Long Civil Rights Movement (LCRM) was attempting to edit the pervasive and popular narratives about Black Americans that were foundational to their original subjugation in real time. This long running tradition of marginalized communities altering the status quo of the country from racism, sexism, and classism, towards a more inclusive future is what in part undergirds some work that digital activists engage in.

 This new wave of digital activism is publicly interrogating the conceit that the way data and information is displayed for our consumption and use is neutral rather than a direct extension of the reality in which we already exist. Cathy O’ Neil suggests that we should “stop trusting algorithms to be fair”9 because our technology is essentially a mirror of our society. People themselves are rarely fair and tend more towards being self-serving and most interested in preserving their own narratives particularly when profit making is involved.  Rather than believing that what we find first in our searches is most common or most often true, we should read more closely in order to understand “the material conditions on which these results are predicated” 10.

A certain level of interrogation is needed to untangle who has decided what we see first, how we should interact with it, and whether or not it is true. This kind of media literacy is often not taught because we have been fed notions of popularity and neutrality as the obvious default.

Interestingly enough when companies like Google are reprimanded for the product that they have created, they often relegate the blame towards the public rather than the inherit flaws in their programming and algorithms. This is the same work that oppressive institutions have been doing generations. Although you can be an accomplice in a system of which you are not an architect, the blame cannot only lie with individuals. The LCRM particularly was predicated on the notion of small legal victories to eventually change the tableau of democracy rather that a complete overhaul of the system. This of course is because it is often extremely difficult to dismantle something that you did not build.

If digital activists can continue to apply pressure to the companies that are responsible for enforcing the status quo (in the same way that the LCRM applied legal pressure to the legal establishment) perhaps we will be able to see our technology made more intersectional, accessible, and accurate.

The Question of Algorithms

Algorithms are the equations that helps us throughout our everyday lives. They can be used when we decide what meals to cook upon their reception or what resources to pick depending on how relevant they are to us. The problem with algorithms in this day and age is that they lack transparency. A lot of what algorithms “do” are hidden behind unknown rules and digital curtains which companies do not make us privy to. Louise Matsakis writes that, “…even the people who build them aren’t always capable of describing how they work. ”11 This statement gives credence to the situation; big companies can set up these algorithms, but they cannot determine how they will work. She later espouses the idea that no one really cares about the code the big companies use, they just want the algorithms to be fair. Algorithms can be “sexist” or “racist” because they have no context and adapt to the bias of user relevance and this ties into that question of fairness, how transparent can we make these algorithms and will people be willing to accept that?

It is for this reason that scholars like Cathy O’Neil and Matsakis caution us to become better in how we consume digital media. O’Neil cautions us to not put blind faith into algorithms.12 While Matsakis wants us to question the algorithms we use in our daily lives. I found O’Neil’s description of an algorithm to be particularly interesting. Simply put, you have a historical data-set and your definition of success. The historical data-set are the things your bringing together like the ingredients in a dish, you go over which ingredients you want to use, and those you don’t. Your definition of success can be if your family likes the meal you prepared, then you have succeeded. This process can then be repeated with your successes also being added to the historical data-set. This kind of algorithm works for you and around you. The algorithms companies use and have AIs run pull from the entire culture for the historical data-set, thus a lot of biases of the past can be impressed upon the present. 

Safiya Noble brings up this exact idea in terms of feminism and black people. This historical data-set has access to the data the entire culture can provide. Thus, these algorithms end up carrying the biases of the past into now. Her personal anecdote13 at the beginning of her writing details this effectively in the sense of the sexualizing of women and how that affected her search on Google. She simply wanted to do something with her nieces but was met with pornographic results. She later goes on to discuss how the Google search algorithm has no social context. It show something that’s biased, something racist or sexist and not even have a concern or programmed thought of the consequences. So, in a vein similar to O’Neil and Matsakis, Noble cautions us about how big companies use algorithms and about their biases and fairness, but she also she carries a sense of activism. Noble brings up topics about movements to get companies to change how these algorithms function and investigates their biases. So we need to take O’Neil’s, Noble’s and Matsaki’s accounts into consideration and begin questioning the algorithms too. 


Blog Post #4: The Connections Between Modern Algorithms and The Social Security Act

As this was a very early time for civil rights the Long Civil Rights Movement is a movement brought up in Jennifer Lynn Ritterhouse’s book Discovering the South: One Man’s Travels Through a Changing America in the 1930s 14 that states that while the civil rights movement was at its peak in the 1960s the LCRM can be considered a sort of pre curser to that. What the LCRM did was simply layout the foundation for what was considered the height of the civil rights as well as modern civil rights movements. During the 1930s there was a push for a cultural lift in the United States spear headed by people like Harry Hopkins as well as The New Deal on FDR’s part. However, as discussed in The Living New Deal’s Working Together 15 the New Deal failed to address the hardships of minorities in during the Depression Era and in some ways even heightened the hardships. Therefore, movements like The Popular Front and the LCRM are mostly credited with the advancement of civil rights during the 1930’s. There simply weren’t many things that the U.S. Government was doing for the advancement of minorities. People like W.E.B De Bu Bois, Jane Adams and Marcus Garvey are essentially the first step of the 1960s civil rights explosion and that of modern times. Some contributions such as the books and papers that were written or physical organizations like the NAACP are still at the forefront of civil rights. Also, while they may not have lasted as long, things like Jane Adams’ Hull House 16 were at the time some of the best advancements for class injustice as well as other civil rights issues. With these movements and organizations, the central idea was for basic racial, class, and many other forms of equality. Also, as discussed in The American Yawp

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the central message was to give the people clearly being discriminated against some form of control in their government. The example given in the book states that even though the heavily African American community of agricultural workers still had to pay taxes they would not receive the benefits of the Social Security Act. The reason for this being that “…(white southern lawmakers were) afraid that economic security would allow black southerners to escape the cycle of poverty that kept them tied to the land as cheap, exploitable farm laborers…” 18. This clearly was wrong and many of the civil rights leaders of the time vouched for some representation as many white southern lawmakers only advocated for discrimination.

In many ways this has not changed today with other issues that are at the forefront of politics. The way that many people saw the treatment of African Americans in the 1930s specifically regarding the Social Security Act is largely the same when regarding biased and unfair algorithms today. Much like the little control the African Americans had over their right to social security many today argue that there is too little understanding of what happens inside of an algorithm. This, much like the little representation in social security, results in zero control over large part of the algorithm

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. Overall while people think that algorithms are something that is unbiased it has been shown that many can give unintentional bias or even intentional. It has been exposed that many of the algorithms that can decide very crucial things in people’s lives are unreachable in how they are made. This results in people not actually knowing how or why they were denied insurance, for example 20. One example that has recently plagued many early detection police algorithms has to do with African American crime rate. Obviously African American are not more likely to commit murder. However, due to the statistic of African Americans making up 13% of the population while making up 50% of all homicides in the United States many algorithms have mistakenly understood this as “African American are more likely to commit crim.” Clearly this is not the case, this is a biased and bigoted thought but because the algorithm only sees the statistic it has been known to sort of “let go” of other races. This clearly is a problem and many situations like this is what has caused many activists to call for access to these crucial algorithms. The lack of access and thus lack of control has proven to be a problem time and time again and thus needs to be addressed in the future.

Unfair Algorithms

Algorithm criticism is based on the idea that an algorithm is not in fact neutral as most people believe, but is actually “ opinion embedded in math”21 . Because algorithms are built by humans they have the same biases and prejudices that we have. Scholars and researchers like Cathy O’Neil and Louise Matsakis are calling for greater transparency and standards in the world of algorithms and artificial intelligence. In her article “What Does a Fair Algorithm Actually Look Like?” Louise Matsakis details that many people don’t understand how an algorithm works 22. They may have been declined a bank loan, or health benefits based on a computer algorithm meant to weed out potentially more at risk applicants. But by making the process that goes into designing these algorithms more open it would help people to better understand what they could do to succeed in or appeal those decisions. 

Safiya Noble’s Algorithms of Oppression: How Search Engines Reinforce Racism excerpts illustrate how people of color are being oppressed in Google’s search results in a parallel to how they were treated in the early part of the 20th century 23. We saw musicians like Lead Belly dressed in costumes meant to reinforce ideas of how people of color were perceived as “less than” their white counterparts. In a similar matter when Noble entered the phrase “black girls” into the Google search bar the autofill suggestions were tantamount to porn results. Again the racial perception being that when searching for anything related to “black girls” the searcher would be looking for less than desirable traits, or behaviors. The sexualization of black women is propagated with the expectation of promiscuous behavior associated with them. This is racism that began early in American history and has been carried down through generations. 

So how can we change this? O’Neil says that people need to “question the algorithms themselves and  build ethics into them”24. Matsakis states that if people knew that by changing one small factor- or if the people using the algorithms understood which factors are playing into the decisions- the outcomes of these decisions could be changed. The outcomes would be more fair. The idea that someone’s zip code alone would preclude them from obtaining a loan is ridiculous, but it happens. I think that if consumers better understood these things they could more easily demand transparency, and if that demand isn’t met take their business to someone else. If there was a standard for ethics in algorithmic programming things like this would happen less often. I think that modern digital consumers are helping to change the attitudes and practices in their algorithmic designs. As more of our lives are intrinsically intertwined with computers the idea of using a computer program to weed through data is common. Thinking back to when Tony Guidone 25 came to talk to us about key word searches, I feel like there is a connection between using the right kind of keywords when searching for information and creating algorithms. We may not be automating them and turning our decisions into code but we are still using a process of elimination in the same way that algorithms do. 

I thought that algorithms could only be processed by computers, but now I think that we are also computers and process them constantly.

Blog Post 3: African American Sharecropping in 1940s Rural Georgia

Although the image I chose for my image analysis was taken in June of 1941, the implications of the Great Depression and the New Deal policies were still clearly in effect. This is essentially how I decided to choose my photo. I saw these African American men and women farming the fields of Georgia for cotton and the despair of the image drew me to it. I figured that even though it was taken in 1941, it could’ve easily been an image from the enslavement era because the realities of it were the same – African Americans were not afforded any type of economic or social opportunities. This is laid out in the American Yawp section, “XII. Equal Rights and the New Deal,” which states that, “Franklin Roosevelt did little to directly address the difficulties black communities faced.” 24 Additionally, due to the passing of some exclusive laws and acts that particularly only benefited white men in the south, African American citizens couldn’t receive economic aid from the Social Security Act, and the Agricultural Adjustment Act “displaced black tenants and sharecroppers, many of whom were forced to return to their farms as low-paid day labor or to migrate to cities looking for wage work.” 25

A historical question I would explore using this image is how the exclusion of African Americans in New Deal efforts helped promote, or begin, the Great Migration? Is there any correlation? The American Yawp article hints at the answer being yes, but I think it would be worthing exploring at what rate did African Americans from this rural communities travel to cities like Chicago, Milwaukee, New York City, Detroit, etc. Furthermore, did the treatment of African American laborers shift during World War II, especially since the photo is from June 1941, right on the brink of American involvement.

I was skeptical of the glitching process before I actually used it. I didn’t understand why the same conclusions couldn’t be drawn by looking at original photos? After my fourth attempt at glitching my own photo, I started to become a believer. As shown in my item and exhibit, I think it’s interesting how there’s a black line perfectly over all of the faces of the sharecroppers, so that all you can see are their legs and equipment working the field. In my mind, I couldn’t think of a better representation of the time. Lawmakers and Southern white men took advantage of the bodies and labor of African Americans, but when it came to giving them rights, seeing them as people with families and necessities, it was impossible. They chose what they wanted to see – and it wasn’t anywhere close to economic equality.

Blog Post 3

When I began searching for an image, I was intent on finding something that showed the harshness of the Great Depression on people much like the famous Migrant Mother photograph from the 1930’s. The image I chose had the opposite kind of intent as that photo. The photograph I chose showed how some of these art programs helped people escape the harshness of the Great Depression and give some relief to those in need. This image specifically caught my eye because it was a young child and the effect of the Great Depression on the children specifically is not something I would normally think about, I would normally think about the effect on adults or families as a whole.


With this image, I’m asking questions like “How did the children of the Great Depression deal with the stress of the times?”. I think that this image in particular shines a light on the positives of the arts programs and how they impacted people’s attitudes. The monetary impacts of the Great Depression sent people into a state of panic or great sadness and these kind of programs had a significant impact on mood and morale especially for the youth. 26


I’ll admit that I don’t see the purpose of the glitched images as much as the next person but this process definitely helped me see the potential in it. The article by Kramer helped me see some of the potential applications for glitching an image but it wasn’t until glitching my own image that it started to click. The article even states glitching as ‘an unlikely candidate for historical inquiry’. 27 At first my glitched image made no sense to me and didn’t help me see the image in a different way at all until about the third time when it blacked out and distorted most of the image except for the child’s face. The face is what drew me to the image and the glitched image puts a larger emphasis on the intensity of that face.


These methods force us to look at these images in different ways that we wouldn’t have originally. Putting emphasis on certain aspects or blocking out the background can shine a light on a whole new meaning of an image. The glitching in particular helps with the blocking out of the less important parts of images and focuses the viewers attention to certain aspects and parts of an image.


I don’t think these methods helped me answer any questions as much as they sparked my interest for a new topic or raised different questions. This image in particular forced me to think about the effect of the Great Depression on the children adn how they coped rather than focusing on the adults.

Links: http://jessicadoeshistory.com/cnd/items/show/196

http://jessicadoeshistory.com/cnd/exhibits/show/arthur-smith–a-young-boy-name/image-annotation

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