For this Detain/Release simulation, I knew I would have bias, yet, being aware and trying to control for that bias I was not very successful. I was unable to see the top of my screen where the “Jail Capacity/Fear” bars were located because of how my browser was configured. I did not become aware of all the “red,” until afterwards. This was done purposefully out of curiosity given my past life experience. I was not surprised by the biases of my results. 

I graduated from university in 1988 with a bachelor’s degree in criminology and corrections and immediately obtained a job with the local county office of probation for juvenile justice. I worked at this job for just 8 months, I was not cut out for it. This job required following a strict set of rules and guidelines to determine people’s lives, I felt that the system was too rigid. There was little room for empathy or understanding in the way that cases were handled, so I left.

At the time, I felt like they just wanted me to be like a computer following an algorithm, more than thirty years later the process is still the same using AI programs. AI program may be able to analyze large amounts of data and identify patterns and trends that could inform decision-making, about recidivism but it lacks the ability to understand the emotional and psychological complexities of human behaviour and the circumstances of each case. 

AI-informed decision-making has significant implications and consequences for the criminal justice system. If AI algorithms are trained on biased data, they may replicate and even amplify existing biases in the criminal justice system. This can result in unfair outcomes, particularly for marginalized groups who may already be disproportionately impacted by the criminal justice system. 

Data! Data! Data! The use of algorithms in law enforcement and the potential for bias and discrimination is not a new idea in the criminal justice system. This is highlighted in the podcast, The Crime Machine, P. Vogt (2018), discusses, young police officers being given quota directives.  Arresting people instead of actually solving crimes and downgrading crime. He goes so far as to emphasise the Summoning of people in impact zones, targeting Hispanic and black young men, and of course, white men we never targeted, all for COMPSTAT data! 

A major concern with AI programs, algorithms, and data is the potential for bias and discrimination. If AI algorithms are trained on biased data, they may replicate and even amplify existing biases in the criminal justice system. This can result in unfair outcomes, particularly for marginalized groups who may already be disproportionately impacted by the criminal justice system.

Another concern is the lack of transparency and accountability in AI decision-making. It may be difficult for defendants and their lawyers to understand how decisions were made and to challenge those decisions if they are based on opaque and complex algorithms. This can compromise due process and the right to a fair trial

Additionally, there is the potential for AI to reinforce existing power imbalances in the criminal justice system. If AI algorithms are primarily developed and controlled by those in power, they may be used to perpetuate existing structures of oppression and further marginalize already marginalized groups.

Overall, while AI has the potential to improve decision-making in the criminal justice system, it is important to carefully consider the potential implications and consequences of its use, particularly with regard to issues of bias, transparency, and power. It is essential that AI is used in a way that is ethical, accountable, and just.

References

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.

The instructions for this task were to navigate through a confusing GUI game – User Inyerface, designed to distract and manipulate the user by employing what Brignull (2011) refers to as “dark patterns,” essentially, deceptive practices involving online interfaces. I gave up at the CAPTCHA after a lot of frustration and much laughter. 

Great user interface design should be invisible and seamless, but when bad design choices arise, it can create frustration and complication in an otherwise simple process. This is perfectly exemplified by User Inyerface, a short web game created by Bagaar, which throws every frustrating element of user interface design into one game. From buttons that don’t work, to dropdown menus that are sorted incorrectly, to devilish pop-up windows that can shut down the entire site if instructions are misread, the game is maddening and brutally funny to those who spend a prodigious amount of time on the internet.

The game only becomes worse as you progress, with each new page highlighting a design element that can be confusing if not designed well enough. For instance, the game’s several CAPTCHAs that need to be filled out are an excellent escalation of this concept. While the purpose of CAPTCHA image recognition tests is to confirm that the user is a human, User Inyerface perverts this challenge by demonstrating how one simple word can complicate the whole function of the test. Players have to choose images with “bows” in them, but since “bow” is an English word that can refer to different things, such as a tie, a weapon, or a greeting, it becomes a nightmare to figure out which images to choose. This is where I gave up out of sheer frustration.

Despite being a simple marketing tool for a design agency, User Inyerface effectively showcases how much effort goes into making every online interaction as smooth as possible. Clever designers use a host of patterns to encourage users to move through their websites and apps with ease, making the experience seamless. However, as User Inyerface proves, one bump can unravel the whole system, and designers should be mindful of this when creating user interfaces.

Overall, User Inyerface is an excellent example of a game that uses UX design principles of what you are not supposed to do. 

References

Bagaar. (2019). User Inyerface [web game]. 

Brignull, H. (2011, November 1). Dark Patterns: Deception vs. Honesty in UI Design. A List Apart. https://alistapart.com/article/dark-patterns-deception-vs-honesty-in-ui-design/

Describe or narrate a scenario about a beverage found a century into the future in which order is deliberately coordinated or imposed. Your description should address issues related to agriculture and elicit feelings of awkwardness.

In the year 2123, a new beverage emerged that was taking the world by storm. It was called “ChronoJuice,” and it was rumoured to have the ability to slow down the aging process. People were willing to pay exorbitant prices just to get their hands on a bottle of this mysterious drink.

However, what most people didn’t know was that the production of ChronoJuice was tightly controlled by a group of powerful corporations that had formed a monopoly on the market. They had developed a new strain of genetically modified crops that could produce the key ingredient of the drink, a rare molecule called “Chrono-X,” in large quantities.

To ensure the quality and consistency of their product, the corporations imposed strict regulations on the farmers who grew these crops. They had to follow a strict set of guidelines on how to cultivate and harvest the crops, and any deviation from these rules would result in severe penalties.

As a result, the farmers were forced to abandon their traditional farming methods and adopt a highly regimented and mechanized approach. They had to use specialized equipment and techniques to ensure that every step of the process was executed with precision and accuracy.

Despite the economic benefits of producing ChronoJuice, many farmers felt awkward and uncomfortable with this new way of farming. They missed the days when they could work with their hands and feel connected to the land. They felt like they were just cogs in a giant machine, following orders without any sense of autonomy or creativity.

But they also knew that they had no other choice. The corporations held all the power, and any farmer who refused to comply with their regulations would be left without a market for their crops. So they continued to work, day after day, hoping that someday they would be able to return to a more natural way of farming.

As for the consumers who enjoyed ChronoJuice, they had no idea about the struggles and sacrifices that went into producing their favourite beverage. They just drank it and marvelled at its supposed anti-aging properties, never realizing the awkwardness and discomfort that lay behind the scenes.

The visualizations created by Palladio left me feeling like I was in the middle of the Pacific without knowing exactly what I am looking for. I encountered some new terms, as I navigated through graph theory: nodes, edges, multigraphs, directed and undirected graphs, and finally multiplex networks, this was a learning curve for me.

The image above shows the members of the Golden Record Community represented by the dark gray nodes. Each edge shows a network that is present in the community. The light gray nodes represent the tracks on the Golden Record and are sized according to the number of network members who shared an interest in the same piece of music. The edges, therefore, represent the networks that share community members. It also appears that Amy and I are outliers on opposite sides of the community. It shows the relationships between tracks and community members, not just the tracks chosen by the same community members but all of the songs they chose, including those that are not shared between community members.

This data shows the degree of connectivity that I am most connected to in the network community. Nicole, Phi, and I seem to have the most connections within the graph. Not much other data appears to be encoded into the graph which could be frustrating if trying to look at the data for deeper analysis or compare different attributes beyond tracks and network members.

These visualizations may be able to reveal patterns and similarities in participants’ music choices, but they do not necessarily capture the reasons behind these choices. There could be a variety of reasons why people choose certain types of music, such as personal preferences, cultural background, social context, mood, and emotional states.

While the visualizations provide insight into the overall patterns of music preferences and the strength of connections between participants based on these preferences, they cannot necessarily explain why these patterns exist.

Overall, while visualizations can provide valuable insights into patterns and similarities in music preferences, they are limited in their ability to capture the complex and multifaceted reasons behind those preferences without additional data and analysis.