“Describe or narrate a scenario about a corporation found a decade into a future in which society as we know it has come apart. Your description should address issues related to citizenship and elicit feelings of anxiety” 

The government funneled money into “Sync” a company that focused on bringing people together and creating safer communities. When it first launched, the program was only for criminals. Only people who were considered dangerous or threats to the community needed to have the procedure done. In the trial phase, a small pool of individuals who were out on parol with major convictions qualified. The monitor measured all the user’s biometric data, heart rate, movement, body temperature, etc… and cross-referenced their information with GPS tracking. The devices were intended to monitor and surveil those on parol to ensure they were not partaking in illegal activities or putting others at risk.

The biometric measurements were wildly accurate and even helped some catch health issues at the very early stages. So, Sync released a commercial version, supported by the government, that allowed individuals with medical conditions to get the chips, under the pretense that they were medically assisted devices. Within the first year, Sync was endorsed by the government, celebrities, and serious athletes as the next step towards a longer, healthier life. The general population followed suit, and the model was refined to only require one embedded sensor near the heart. Sync partnered with governments in dozens of other counties and infiltrated global markets; the device became a staple in the lives of billions. Sync was a household name, like Amazon or Google. 

However, as Sync grew, the company expanded the purpose of the monitor – becoming more than just a health device. The page that used to infrequently pop up on people’s smart-watches warning them that they were in proximity to a convicted felon turned into detailed profiles. Profiles of all their users were created with publicly available biometric data that could be accessed by anybody. At first, it was fun sharing your heart rate with your friend. But API integrations created by Sycn and other big tech companies used advanced algorithmic models to predict a person’s mood and mental state. There was even an application that compiled a person’s metadata from their computers, TV, phone, watches, and vehicles and cross-referenced the information to their biological data. This allowed the app to re-create a user’s life in digital form and make extremely accurate, curated predictions of a person’s thoughts, feelings, and future actions.  

That’s when things went south. 

In real-time, people could extrapolate and analyze data from heart rates and body temperature. Meaning, this information could be used to determine if people were telling the truth. Unlike devices of the past, like smartwatches, removing the monitor required surgical intervention. The permanent nature of the device meant these features were inescapable. The government took advantage of the scarily accurate lie detection feature and re-opened nearly all criminal investigations. Headlines started to read “Woman kills husband after using Sync Monitor Lie Detection” and “Another Teen Dies After Trying to Remove Sync Monitor”. Society faced collective panic, living in fear of being watched and constantly exposed. Relationships and lives started to unravel as the Sync monitor forced its users to be completely honest. Secrets that people had hoped to take to their graves were now being trialed by friends, family members, and law enforcement. In a matter of a few months, the world had become an unpleasant, isolating, and hostile place, filled with distrust toward loved ones and a burning resentment toward the government. 

In the book Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Noble she explores how biased algorithms are effectively oppressing people of color, in a deceitful and manipulative way. Within the introduction of the text, she prefaces that these algorithms are impacting users in very legitimate and pragmatic ways. However, they primarily function without investigation from the public and are hidden from everybody except a very few select individuals. So, for the most part, the public has a very limited grasp of the machine-learning functions that dictate these highly-impactful algorithms (Noble, 2018). Its also the case that officials using these algorithms to make decisions, like police officers, are unaware of the parameters and biases that impact the algorithm’s computing method and therefore the results. We see this concept in action through the “Detain and Release Simulation” produced by the Berkman Klein Centre for Internet and Society. 

Unfortunately, this simulation is a crude example of the ways in which algorithms are used to classify individuals (Porcaro, 2019). My initial impression of the game, that the player needs to assess “criminals”, demonstrates that it’s clear this algorithm is being used on marginalized individuals who likely do not have as much credibility to voice their concerns. The algorithm categorizes each case through three separate categories, flight risk, their likely hood to re-offend, and level of violence. Each category is then given a level between low, medium, and high. No information is provided on how these levels are distinguished or why an individual might pose more risk than others. Despite that, the player of the game needs to navigate each case and determine if they are going to release the offender or detain them. 

My assumption is that the algorithm is using information like the offender’s previous criminal record, employment status, income, social background, geographical area, etc.. to determine these low-high rankings. However, these parameters are constructed using overarching generalizations that are likely informed by other biased algorithms or information. As Noble points out, these algorithms feed off one another and perpetuate inaccurate representations of marginalized people (Noble, 2018). The player does not have the opportunity to ask for more contextual information, nor are they encouraged to, as the limited function of the game suggests that the sole purpose of the player is to sort through each file. We could say that the player is acting on their own judgments when making their decisions, and to some degree that might be true, but the player is determining a risk threshold based on the information provided by the algorithm. So, in many ways, the element of human judgment is almost irrelevant considering the algorithm had already determined which cases are more likely to be detained. For example, an ethical person would likely not allow a highly-violent individual to re-enter society so any file with a high level of violence is unlikely to be released. 

The player might release an individual they deem low-risk but later find out through a news article that the offender has gotten into some trouble. The article reads something along the lines of “Judge allows violent offender back onto the streets”. What the headline of this article implies, is that the player, or decision-maker, is at fault for determining this offender is low-risk. However, the language then places the accountability onto the decision maker, which calls into question the player’s judgment. The accountability placed on the decision-maker allows the inaccuracies of the algorithm to go left unchecked. Rather than reassessing the variables that determine the algorithm’s accuracy, the player continues navigating the remaining files with a higher level of scrutiny and apprehension. This comes back to the idea that algorithms are seen as neutral actors, incapable of being swayed by human biases because they are founded on mathematical equations – when that’s simply just not the case. 

References

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

Porcaro, K. (2019, April 17). Detain/release: Simulating Algorithmic Risk Assessments at pretrial. Medium. Retrieved March 20, 2023, from https://medium.com/berkman-klein-center/detain-release-simulating-algorithmic-risk-assessments-at-pretrial-375270657819

Expectedly, the User Inyerface game designed by Bagaar was extremely frustrating to navigate and quite time-consuming (Bagaar). The game is “designed to induce rage: mislabeled buttons, complicated password rules, nearly impossible to close pop-up windows, slowly scrolling terms and conditions, and annoying CAPTCHA forms” (Gartenberg, 2019). The makers of the game sought to make the navigation of the site as challenging as possible, including everything that goes against strong user interface design. 

I think most of us know when we are moving through a design that is not user-friendly; oftentimes, we feel frustrated that something is not clear or the navigation is not intuitive. This particular game was infuriating, and frankly, I spent a good 10 minutes getting through the first two pages before I gave up and decided to watch a Youtuber play the game instead. Misleading words, colors, italics, and bolded text were among the few design elements that made surpassing the first page challenging. The big green button below the instructions reads “NO” and is not linked to anything. Naturally, I clicked the button, wondering if it was only the lettering and not the shape itself that needed to be clicked; however, the word “HERE” was what needed to be clicked in the line below.

The next page included a form that needed to be filled with the users chosen password, email, and signed user agreement form. Entering information into each part of the form was difficult, everything required several alterations or some degree of troubleshooting. 

As users, we often feel the impacts of a badly designed user interface but what’s more concerning is when an interface uses its design to trick its users (often consumers). These deceptive methods are what Harry Brignull describes as “dark patterns” (2011). Unfortunately, these deceptive methods are not always easily identifiable, allowing certain companies to make additional profits at the expense of the user’s ignorance. 

An area of inquiry that sparks my interest is the controversy surrounding social media user interface design. The documentary The Social Dilemma explores how the objective of these social media companies is to produce users who become dependent on ingesting content. These big tech companies use some extremely deceptive, tactful, and manipulative methods to ensure their users depend on the gratification they receive from social media – to a point where the media impacts the user’s mental well-being. The goal of encouraging users to endlessly and mindlessly scroll plays on the human psyche, in ways that the majority of users can’t begin to conceptualize (think children). TikTok’s user interface and algorithm model are notorious for having young users glued to their phones, for at times, twelve hours a day. This issue was exacerbated by the pandemic when adolescents were spending most of their time online. However, the problem persisted following the lifting of restrictions with children and teens still spending an alarming amount of time on the app. Recently, Tiktok claimed that in upcoming months they will impose a mandatory 60-minute screen limit for all users below the age of 18 (Hart, 2023). Could we say this is TikTok removing dark patterns from its design? 

References

Bagaar. (n.d.). User Inyerface – a worst-practice Ui Experiment. User Inyerface – A worst-practice UI experiment. Retrieved April 9, 2023, from https://userinyerface.com/

Brignull, H., Eagan, C., MacIntyre, J., Clancey, P., Overkamp, L., Brosset, P., & Prater, S. V. (2011, November 1). Dark patterns: Deception vs. honesty in Ui Design. A List Apart. Retrieved April 9, 2023, from https://alistapart.com/article/dark-patterns-deception-vs-honesty-in-ui-design/

Hart, R. (2023, March 2). TikTok sets default daily screen time limit for under 18s. Forbes. Retrieved April 9, 2023, from https://www.forbes.com/sites/roberthart/2023/03/01/tiktok-sets-default-daily-screen-time-limit-for-under-18s/?sh=7a39f2c01bfa 

Networking diagrams can be helpful to visualize the relationship between entities, but oftentimes in these diagrams, we lose context that would otherwise provide the viewer with significant value. My initial thoughts on the diagram are that there are clear indications as to what songs the group preferred. It’s not a surprise to me that some of the American songs are the most picked. Songs like Track 7: Jonny B Goode and Track 14: Melancholy Blues are likely songs many of us in the class had heard before. These songs likely evoke memories or personal experiences because they are commonly played on the radio and in movies. 

We can see that the less selected songs are outlining the perimeter, as they are not acting as nodes with several connections. Some of the less-selected songs include Track 19: Izlel e Delyu Haydutin by Valya Balkanska. This surprised me as this was actually one of my favorite songs on the records, and one I had never heard of before. Another less-selected track is Track 12: Chakrulo, with only one person adding it to their list. I think this aggregation of data does a good job of representing the number of connections or preferences our class might have for a certain song; however, it does not capture the reasoning for why we selected the songs we did.

Interestingly, this type of data reminds me of a previous software we used at my old university called LitMaps. LitMaps is a platform that produces networking maps that demonstrate how frequently related texts are referenced. This tool is helpful when performing literature reviews, but also in assessing the literary canon (LitMaps). 

References

Litmaps. (2023). About Litmaps. Litmaps. Retrieved March 20, 2023, from https://www.litmaps.com/company