Month: July 2024

Speculative Narrative Prompt:

Describe or narrate a scenario about a building found a millennium into a future in which order is deliberately coordinated or imposed. Your description should address issues related to disease and elicit feelings of fervor.

It’s Monday, July 31st, 3024. A group of archeologists is exploring an area of what was previously known as North America…

 

Over the last few centuries, sea levels have impacted the area significantly, with many buildings and cities being decimated. Globally, the impact of climate change has been catastrophic. Extreme weather patterns such as droughts and floods have reduced the biodiversity available. The resulting food and resource scarcity has forced the remaining global populations to relocate. A large part of the population was eradicated by viruses and diseases that ravaged human, animals as well as flora and fauna. There was a dark period of wars fought over resources that ultimately resulted in societies and governments being restructured – there is now one global governing body, a singular global crypto currency, and a military group known as the Galba Unit. The Galba have limited human soldiers, but a vast fleet of humanoids, drones, laser weapons, and autonomous military vehicles to help them carry out their intense surveillance of the remaining populations.

 

The building is a vast structure that spans a large area in the center of the former metropolis. The building, once gleaming white, is covered in spray paint and many of the towers have been reduced to rubble in the wars. As the archeologists step inside, they can see on the walls the credo which reads “These principles we uphold: justice and authority. We dedicate this building to bring order and peace to the country. Order and stability will prevail as we work towards a prosperous and secure future for all citizens”.

 

As the archeologists explore the building, they uncover endless surveillance rooms, with some remaining electronic equipment strewn about, badly damaged from the raids. Some of the rooms are great halls, with seats surrounding a central speaking area. None of the rooms have any windows, and they are adorned with several locking and security mechanisms at the doors. The building has wards dedicated to sanitization, full of biomedical equipment, PPE and medication vials, some still intact. There are robotic operated medical equipment machines throughout, and refrigerated rooms for storing what was once a large supply of vaccines. Now, the building tells stories of a time of turmoil and desperation…

 

Reflection: 

I hope you enjoyed my narrative as much as I enjoyed creating it. I opted to let my imagination go a little wild and included a wide variety of possible future social problems and/or challenges. As Petra Lilja (2021) notes “speculative design, or any related design approach oriented towards the future is to use it as a vehicle to approach complex societal and environmental challenges through design” (p.39). The future I envisioned is dystopian, but my goal was to provide some commentary on the issues of contemporary society and how that might pan out. 

 

Mitrović, I., Auger, J., Hanna, J., & Helgason, I. (Eds.). (2021). Beyond speculative design: Past – present – future. SpeculativeEdu.

 

 

Detain Release

This week’s module highlighted the perils of algorithmic decision-making and its implications for public justice systems. During the “detain and release” activity, I noticed that the cases were becoming repetitive, and I wasn’t sure how I had previously responded. As I continued through the module, the added pressure of the glowing red fear index and jail capacity level created extra angst around my decisions. Initially, I was considering the violence level of the accused as an important factor. However, even though a drug trafficker may not have shown indications of violence, there is a level of violence and societal impact associated with the larger criminal enterprise/network. This quickly becomes a very complex issue, and I understand the desire for a system to make analytical choices based on perceived risks. The question is: how accurate is our reporting of these risks? What metrics can we rely on that will accurately describe a person’s violence level, especially when we are making judgments about the likelihood of an event that hasn’t happened yet?

AI models encode personal biases, and their mass deployment amplifies these biases in ways that can be catastrophic. As we saw in previous modules, the use of these systems leads to a variety of unintended consequences, such as an increase in citations being given as a result of the predictive policing machine (Vogt, 2018). I liked the descriptions of these processes termed by O’Neil (2016) as “math-powered applications that encode human prejudice” and “weapons of math destruction” (2017). Unfortunately, once these models are running, it’s quite difficult for us to know how they are making decisions. This lack of transparency is uncomfortable at a minimum and harmful to society, especially when considering the gravity of a situation such as the criminal justice system. How can we increase the human management of a system that many humans don’t fully comprehend? To ensure the accuracy and reliability of AI systems, it’s essential to understand how they are audited. A growing group of people is increasingly becoming concerned about the use of these tools, highlighting the need for enhanced regulation and safety measures.

O’Neil, C. (2017, April 6) Justice in the age of big data. TED. Retrieved August 12, 2022.

Talks at Google. (2016, November 2). Weapons of math destruction | Cathy O’Neil | Talks at Google. [Video]. YouTube

Vogt, P. (2018, October 12a). The Crime Machine, Part I (no. 127) [Audio podcast episode]. In Reply All. Gimlet Media.

Vogt, P. (2018, October 12b). The Crime Machine, Part II (no. 128) [Audio podcast episode]. In Reply All. Gimlet Media.

Task 10: Attention Economy

 

 

 

 

 

 

Here’s a non exhuastive list of some of the dark patterns that I noticed in ‘User Inyerface”:

1. “Click here to go to the next page” – difficult to know where to click due to the imagery, size and location of buttons
2. Ridiculous password settings
3. Double negatives “I do not accept the terms and conditions”
4. A help bar that repeatedly popped up
5. “Hurry up, time is ticking” stressing me out
6. “Select 3 interests” and the location of the ‘deselect all’ button is difficult to find due to the way we read text

It went on like this for a while and I nearly ran out of the patience to complete the game. It’s clear that the motivation behind these deceptive practices is to wear you down a bit and cause you to reduce the amount of attention you are paying to what you are doing. They are also intentionally designed to cause us to take actions that we might not be aware of the consequences of. Since completing this game I have become hyper aware of these ‘dark patterns’ (Brignull, 2011) once during an attempt to cancel my doordash account, and again when attempting to order from the screens at a McDonalds in France. The latter experience made me realize how difficult these tactics are for people with low literacy (digital or print) levels / people who speak another language as we rely so heavily on the way that information is visually organized on the screen.  We are naturally inclined to click on the text that is highlighted in a green (or sometimes red) button. People with low levels of digital literacy are particularly vulnerable to these tactics as they may not be expecting to be deceived, might not understand the implications of their actions or be aware of the potential harms of sharing certain information or granting access levels.

In her TedTalk, Zeynep Tufekci made a compelling argument about the dangers of persuasive technology when she noted that it seems relatively innocuous in the beginning (being inundated with the same product you were searching) until we gain an understanding of the ways that algorithms are shaping and controlling our perceptions of the world around us (Tufekci, 2017). The internet can be a difficult place to regulate, and it is even harder to decide at which point these persuasive architectures shift from being simply persuasive and cross over into manipulative territory. As both Harris (2017) and Tuekci (2017) noted, the intent behind these structures were not to be unethical or cause harm, but there are a variety of unintended consequences. Clearly, we need further regulation of the internet, but I think that change will be a much slower and difficult road than it has been to develop these technologies. This is in part because many people do not yet understand the ethical implications of these attention technologies.

References:

Brignull, H. (2011). Dark patterns: Deception vs. honesty in UI design. A List Apart, 338.

Harris, T. (2017). How a handful of tech companies control billions of minds every day [Video]. TED.

Tufekci, Z. (2017). We’re building a dystopia just to make people click on ads [Video]. TED.

 

 

 

Golden Record Data Set Analysis:

The visual representation of the data set created in Palladio offers up some insights into the connectivity of choices made by students in our class. In the data set below, you can see my choices as well as the two students with which I had the most overlap with.

The data visualization reveals that Robyn, Duncan and myself have 4 overlapping songs, and that I had an additional overlap of 2 songs with Robyn and 1 with Duncan, so 60% and 50% respectively. While the Palladio program was able to group students according to the connectivity of their choices, it fails to offer up any additional data that would be pertinent to offer up a deeper analysis.

Some examples of missing information that may have impacted peoples musical choices are: age, gender identity, race, cultural background, musical taste, languages spoken, and geographical location. Without these pieces of distinguishing information, it’s difficult to discern what the members of my group actually have in common, and why we might have made our choices. The palladio program offers up a quantitative mapping of connectivity, but lacks any qualitative data that would allow us to make meaning of these nodes.

Also lacking from the data set are all of the null choices, which is a much larger group of songs than the choices that were selected. It would be interesting to see which songs were most frequently not chosen, and what those songs might have in common. For example, is there a particular genre that is often left out, or style of music (instrumental etc.) that led people to deselect it more frequently? Within my group, the program was able to offer up the songs that we did not have in common, but as with the connected nodes, it is difficult to make any meaning of the null choices,  particularly as they are all different. Sociocultural factors have a huge impact on how we engage with the world, and without any data on who the people are behind the dataset, the analysis of these musical choices falls flat.

References:

Systems Innovation. (2015, April 18). Graph theory overview. [Video]. YouTube.

Systems Innovation. (2015, April 19). Network connections. [Video]. YouTube.