Text Technologies: The Changing Spaces of Reading and Writing

Prompt – In about 500 words, describe or narrate a scenario about a law found a few years into a future in which society as we know it has come apart. Your description should address issues related to algorithms and elicit feelings of contentment.

Output 1 – generated with Co-Pilot 

In the year 2047, after decades of social upheaval and the collapse of traditional governance structures, a pivotal law was enacted: the “Algorithmic Trust and Harmony Act.” This law emerged from the collective will of a world where humanity had witnessed the dangers of unchecked algorithms—the biases, the exploitation, and the dehumanization. In its wake, this legislation sought to reshape society by redefining the relationship between humans and technology.

The law mandated that all algorithms influencing critical areas of life—housing, healthcare, justice, education, and employment—be rendered entirely transparent and comprehensible to the average person. The days of opaque black-box systems were over. Developers were required to publish their code in plain language summaries, accessible to everyone. Communities, not corporations, were tasked with overseeing these systems through local councils. These councils were diverse in composition, including technologists, sociologists, ethicists, and ordinary citizens. The algorithmic councils became a new pillar of governance, decentralizing power and fostering trust.

One of the most profound changes brought about by the law was the concept of the “Algorithmic Commons.” Previously, algorithms had been proprietary, jealously guarded by tech giants. Now, they were declared the shared property of all humanity. Anyone could use or improve upon them, provided their modifications adhered to ethical guidelines established under the Act. This fostered a collaborative culture, where communities tailored algorithms to meet their specific needs, values, and aspirations.

A key feature of the law was the “Bias Disclosure and Correction Framework.” Every algorithm had to regularly undergo bias testing and publish results. When biases were detected, immediate corrective actions were mandatory, overseen by the councils. Communities celebrated this transparency, as they saw the systemic inequalities of the past begin to dissolve. No longer would an algorithm reject a housing application because of a zip code or predict recidivism rates based on flawed data.

The Act also introduced the “Human Dignity Override.” At any time, individuals affected by an algorithmic decision could appeal to the council. If the council determined that the decision violated human dignity or fundamental rights, the algorithm’s ruling was overturned, and immediate reparative actions were taken.

Life under the Algorithmic Trust and Harmony Act felt different—not just safer but profoundly more collaborative. People spoke of how their voices mattered again. Decisions that once felt cold and distant, made in boardrooms or server farms, now reflected the collective will and wisdom of their communities.

One evening, in a small town nestled beside a glimmering lake, an elder stood before her community, recounting the past. “There was a time,” she said, “when we let algorithms shape us without question. But today, we shape them—and in doing so, we’ve rediscovered what it means to be human.”

The air was quiet as the sun dipped below the horizon, yet a quiet contentment filled the hearts of those listening. They lived in a world where technology no longer ruled, but served—where humanity had reclaimed its destiny, one algorithm at a time.

Output 2 – generated with Co-Pilot 

A few years into a future marked by the collapse of traditional societal structures, humanity finds itself in a patchwork of autonomous communities. Technology remains an enduring backbone of human existence, but its unchecked influence has led to the disintegration of the centralized order. The chaos birthed a new law, known simply as “The Algorithmic Compass Act.”

As the story unfolds, the Act is introduced in a community called Havenridge, nestled amidst forests reclaimed from urban sprawl. Havenridge thrives on a philosophy of balance—between nature, people, and the tech that once dominated lives. With the Algorithmic Compass Act, every algorithm deemed critical to decision-making must now be paired with human discernment and accountability, reflecting a newfound respect for collaboration between mind and machine. The law’s core principle is simple yet profound: algorithms provide insights and recommendations, but humans hold the reins to ensure justice, empathy, and individuality prevail.

Take Naomi, a schoolteacher who had always feared the cold precision of algorithms. In the old world, predictive models once labeled her students—some marked as promising, others as failures—on flimsy data. Under the Act, these models now suggest broad strategies for tailoring lessons, but Naomi decides how to apply them. She’s empowered to nurture creativity in a quiet child who would have been overlooked or challenge the brightest students without boxing them in.

The town square reveals another transformation. Algorithms once dictated resource allocations—water usage, housing assignments—based purely on efficiency metrics. Havenridge now uses these algorithms only as guides, alongside councils that listen to personal stories, weigh cultural priorities, and preserve the community’s spirit. It’s a slower process, but people find peace in knowing they have a voice, a seat at the table.

Tom, the farmer, watches with quiet satisfaction as weather-predicting models guide crop planting schedules. Yet it’s his intuition, honed from decades of living alongside the soil, that decides how to space his rows to protect against pests. His decisions are celebrated in Havenridge as essential to the community’s sustainable way of life.

Though challenges abound, Havenridge thrives on its belief in humanity’s worth and its ability to coexist harmoniously with technology. The Algorithmic Compass Act reminds people daily that algorithms are not infallible gods but tools meant to uplift and support human wisdom, not replace it.

Under the morning sun filtering through Havenridge’s leafy canopy, Naomi and Tom meet at the community market. There’s a palpable sense of contentment in the air—not just because lives are simpler, but because they are richer with meaning. In this future, the Act represents hope: a world where algorithms are partners, not rulers, and humanity’s core values guide the path forward.

Reflection

I appreciate the opportunity to explore AI’s capabilities and enjoyed reading the output and reflecting. I was so intrigued that I ran the prompt twice to see what else would be generated. The prompt itself was fantastic—timely and thought-provoking, as it touches on concerns that many people have.

The idea of society falling apart due to algorithms reminded me of a Munk Debate I listened to. The debate centered on the statement: “Be it resolved, AI research and development poses an existential threat” (Munk Debates, 2023).

There wasn’t anything particularly shocking or creative in Output 1. Most of the proposed laws are common recommendations, like transparency and bias testing. The idea of a council feels romanticized and utopian—people have their own biases, and power tends to corrupt. While idealistic, this scenario seems unlikely given historical patterns of power and bias.

I like how Output 2 includes specific examples of humans using technology to achieve better outcomes. For instance, the teacher using algorithms to tailor lesson plans or the farmer combining intuition with data models to maximize his efforts. Like Output 1, there’s nothing groundbreaking here, but it aligns with how many people already envision the role of technology in society.

Dunne & Raby (2013) describe speculative design as one that “thrives on imagination and aims to open up new perspectives on what are sometimes called wicked problems, to create spaces for discussion and debate about alternative ways of being, and to inspire and encourage people’s imaginations to flow freely” (p. 2). I feel that the AI narratives fall short of truly opening up new perspectives. Given that AI is, at its core, just an algorithm, this limitation isn’t surprising.

Also discussed by Dunne & Raby (2013) is the idea of not predicting the future but rather exploring possible futures to better understand the present and shape the kind of future people want. In this sense, the narratives created by Co-Pilot are valid. Both stories offer a glimpse into concerns people have about algorithms while also summarizing calls to action to prevent potential unwanted consequences that could lead to society’s downfall.

References:
Dunne, A., & Raby, F. (2013). Speculative everything: Design, fiction, and social dreaming. The MIT Press

Microsoft. (2024). Copilot (April 2024 version) [Large Language Model]. https://copilot.microsoft.com/chats/

Munk Debate. (2023, June 22). Artificial Intelligence Debate. https://munkdebates.com/debates/artificial-intelligence/

When I was completing my Bachelor of Education program, I was selected for jury duty, and I remember being excited about it.  I thought it would be interesting and wanted to participate; unfortunately, it started on the same day as my graduation, so I was excused.  At the time, I was eager to participate and intrigued by the legal process. However, with more lived experience, I now recognize the weight of such a responsibility. As a juror, you are expected to consider only the evidence. However, subconsciously, you cannot ignore the impact of your decision on the accused’s life and potentially public safety. While compiling the detain/release activity, I felt similarly. Each accused person had a compelling reason why detention would be devastating: losing a job, custody of their children, or access to medical care.

This exercise exemplifies the delicate balancing act that judges must play when making decisions. I prioritized the severity of the charges, especially whether they involved violence, along with the prosecution’s recommendations over the Iliad System’s risk assessment. However, I did find myself wondering how the Iliad System was calculating the ratings.  It was frustrating that, as a judge, I needed to consider jail capacity and public opinion in my decision to detain or release someone.  I went through the simulation twice and ultimately lost my position both times, once due to jail overcrowding and once due to public opinion. In this system, it appears that being a judge is a no-win situation.

This simulation made me reflect on my own community, where crime is on the rise, and it seems that no matter how many times the police arrest someone, the same people get released and re-offend. As a community member, I recognize the need for a more effective system. However, I also understand how once a person is in the system, perhaps because of a non-violent mistake in early adulthood or a crime of necessity due to poverty, it is hard to escape, and the deck may, in fact, be stacked against you.  The criminal justice system desperately needs reform; however, AI is not the silver bullet fix that some may think.  When Cathy O’Neil talks about weapons of math destruction, she speaks of them as widespread, mysterious, and destructive.  The Iliad System is all of these things.  As Cathy O’Neil points out, there is a danger when things that we would consider unconstitutional and never accept in open court are being used “because it’s being embedded in a risk score, which is being claimed as scientific,[and] this somehow has the authenticity of mathematics and science” (Talks at Google, 2016, 30:56).

If a risk-assessment tool that was not biased could be developed, would it even be possible to improve this system?  So many factors are involved in whether someone commits another crime while awaiting trial. Is it even possible to predict?  Should we be trying to predict this?  Should people be treated differently based on whether they might commit another crime?  That doesn’t seem to follow the concept of innocent until proven guilty, which we as a society have agreed to.    While public pressure makes judicial decisions challenging, perhaps for non-violent offences, the priority should be ensuring the accused appears for trial. This task and the associated module resources have given me much to think about.  

References:

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

I felt like this was an exercise in what not to do as a designer to hold people’s attention, as I suspect most users would give up on this task and move on to something else. While completing it, I realized that I likely had an advantage over novice technology users.  For example, on the landing page where you need to click ‘here’ to go to the next page, I knew to move the cursor around until it changed and revealed the link in the bottom left corner of the browser.  I couldn’t help but think that if my mother were trying to navigate this site, she would call me, as I doubt she could get the page to advance.  After this first page, I quickly realized that this site would not follow the typical  “rules” users are accustomed to. For instance, the link wasn’t the expected underlined or blue-colored text but rather the actual word ‘HERE.'”

The most frustrating page for me was the second one, where I needed to create a password. The overstimulation from the red and green colors, the constantly changing numbers counting 1, 2, 3, 4, and the confusing wording was frustrating enough. However, what aggravated me was knowing how a page should work but being forced to relearn new (and incorrect) rules. The worst part was figuring out that I had to click on the terms and conditions to accept them in a different window. I suspect this entire exercise gives proficient users a glimpse into the frustration that novice users often experience.

On a broader level, this task made me reflect on how I have been conditioned through repeated exposure to navigate websites and apps intuitively.  This conditioning is so ingrained that I felt frustrated when things didn’t work as expected. I suspect this is why people don’t like updates – they often change how basic tasks are accomplished, and many people dislike change.   However, I approached this task like a game and I was determined to “beat” it. I relied on trial and error to work through the system, knowing it was intentionally defying effective user interface principles. This realization helped me persist. I also understood that a well-designed interface is intuitive, aligning with users’ natural expectations, but this one did the opposite, so I knew to experiment with unexpected solutions.”

Through this entire process, I felt like I was being manipulated because if I wanted to reach the end, I needed to play by these new rules that I didn’t particularly agree with.  In the documentary The Social Dilemma Tristan Harris states, “If something is a tool, it genuinely is just sitting there, waiting patiently. If something is not a tool, it’s demanding things from you. It’s seducing you. It’s manipulating you. It wants things from you. And we’ve moved away from having a tools-based technology environment to an addiction- and manipulation-based technology environment. That’s what’s changed. Social media isn’t a tool that’s just waiting to be used. It has its own goals, and it has its own means of pursuing them by using your psychology against you.” (Orlowski, 2020, 30:11) While this task was just a simulation, I couldn’t help but notice its manipulative design. For example, why was I required to upload an image? How many people were tricked into downloading an image by the large blue ‘download’ button, rather than noticing the faded ‘upload’ text buried in the instructions? This is an example of a deceptive pattern, or what Brignull et al. (2023) call a dark pattern. There were also several points where a ‘skip’ button or alternative options should have existed, such as for the image upload and the ‘pick three interests’ section. Additionally, the forced title selection—where users could only choose between ‘Mr.’ or ‘Mrs.’ and then had to pick the opposite gender to proceed—felt unnecessarily restrictive and frustrating.

This experience was a stark reminder of how much we rely on intuitive design and familiar digital patterns to navigate the online world. When those expectations are disrupted, it creates frustration, confusion, and, in some cases, an insurmountable barrier for less tech-savvy users. The deceptive elements, dark patterns, and unnecessary roadblocks highlighted how designers can manipulate user behavior—especially over time—by conditioning us to act in certain ways. This also serves as a reminder that users must continuously strive to improve their tech literacy to avoid falling into the traps of deceptive or dark patterns.

References:

Brignull, H., Leiser, M., Santos, C., & Doshi, K. (2023, April 25). Deceptive patterns – user interfaces designed to trick you. Deceptive Design.  https://www.deceptive.design/

Orlowski, J. (Director). (2020). The social dilemma [Film]. Netflix.

To better understand the visualization, I first highlighted the source nodes—our names—which allowed me to distinguish between curators and song titles. I then rearranged the nodes by spreading out all the curators in a U-shape. This made it easier to see which songs were the most popular based on node size and which songs had fewer selections. 

By moving a specific track node, I could observe how many and which people had chosen it. For example, Track 13 had five people select it. 

When I slightly offset Track 24 next to Track 13, I noticed that four of the five people who selected Track 13 had also chosen Track 24. However, the visualization does not provide any indication as to why these selections were made.

I found it challenging to interpret connections with so many nodes and edges. It would be helpful if the visualization incorporated color-coding. For example, if selecting a source node changed its color, along with all its corresponding edges, and a different source node triggered a different color, patterns might be easier to identify.

In community one, I used a similar method of node arrangement and observed that all five curators in the group had chosen Track 7. Additionally, several other tracks were selected by three or four of the five curators. However, again there is no clear way to determine why they made these choices. Is there a common factor among these curators that influences their selections? Another important question that the given visualization cannot answer is whether all curators chose a particular track for the same reason. In fact, there may be no real similarity between these curators if they all selected the same track but for entirely different reasons.

This dataset contained 27 tracks, and all of them received votes, with the least popular having two votes and the most popular having 15. However, if some tracks had received no votes, they would not appear in the visualization at all. This absence could prevent viewers from considering why those tracks were not chosen.

Ultimately, the visualization provides only basic connections without deeper explanations. With additional data—such as age, gender, location, or musical preferences—it might be possible to identify relationships between song choices and external factors. This highlights why so much data is collected about us. More data enables a deeper understanding of connections and can be used to influence behaviors, such as purchasing habits or even political opinions. If advertisers can determine what persuades one group to click on a link and buy a product, they can apply similar tactics to other groups with shared characteristics.

It is unsettling to consider how our thoughts and decisions are influenced—or attempted to be influenced—without our awareness, shaped in part by our network connections.

View my Golden 10 playlist

To choose my ‘Golden 10,’ I omitted any song with vocals.  There was already enough speech on the record, and it is unlikely that words in any language would be understood. 

Tim Farris, who led music selection, was not worried about mistakenly threatening extraterrestrial life forms, stating there was no evidence in the history of the human species to suggest this (Taylor, 24:45).  When I first listened to the tracks, I immediately considered how they might be perceived by extraterrestrial beings. Thus, I omitted anything I thought sounded offensive in tone or threatening in any way. I fully acknowledge that there is no way to tell how other life forms will interpret the recordings, but I needed something to determine the criteria.

Last, I picked songs that for me resonated with one or more of the following terms: cheery, friendly, lively, light, welcoming, or calming. Surprisingly, once I set my criteria, I struggled to find 10 songs I truly wanted to include. Do these songs fairly represent all of humanity? Undoubtedly not. But in my view, they reflect the best of what we should aim to share.

References:

Taylor, D. (Host). (2019, April). Voyager golden record. [Audio podcast episode]. In Twenty thousand hertz. Defacto Sound. 

View and listen as Clio the Cultural Anthropologist and I discuss the items in my bag.

*dialogue created with Humy.ai

https://www.kukarella.com/dialogues-ai/gzzGdwRoVx1F4HPYApd5

Often, teachers will afford students different assignment choices to show their learning. As Pena and James point out (2024), “Transmediation in a pedagogical context is commonplace, yet seldom is it identified as such, nor is it the subject of explicit teaching; often, however, it is a part of a devised curriculum that accommodates differentiated learning and learning styles” (p. 1619). As a student, if given choices for an assignment, I would most likely choose the traditional essay writing assignment.  I don’t consider myself artistic or creative, nor do I particularly enjoy acting or speaking in front of a group. 

Due to my preference for traditional writing assignments, this task has proved to be the most challenging for me so far. Along with struggling to come up with a creative semiotic mode change, I struggled to clarify the difference between semiotic and sensory modes. This was compounded by the lack of clear definitions of terms in the 1996 New London Group article, A Pedagogy of Multiliteracies: Designing Social Futures, where modes of meaning are suggested as linguistic, visual, audio, gestural, spatial and multimodal. I was further confused by the concept of the different modes of meaning because the linguistic design is presented as a stand-alone mode.  However, I feel language is inherently intertwined with the other modes.  We need language to think, so language is involved even when viewing an image with no words.    

Peña & James (2024) “propose three categories of non-exclusionary transmediation: sensory, semiotic, and signal transmediation” (p. 1614), which was helpful as I tried to navigate this task.  Additionally, Danielsson & Selander (2021) helped further clarify this for me by stating, “semiotic resources are resources that we use to organize our understanding of the world and to make meaning in communication with others or to make meaning for ourselves (p. 17).  These articles helped me realize that this task wasn’t about changing the sensory mode—something I had initially fixated on—but rather about shifting the semiotic mode, which is different from sensory.  When I decided to use Humy.ai to create an interview-like dialogue about what was in my bag, I was worried because it was still visual. You look at the picture, and you need to look at the words of an interview, but I now understand that it changes the semiotic mode because it is a different genre. In Humy.ai, I chose Clio the Cultural Anthropologist as the chatbot to interact with as it seemed like the best fit for the task of analyzing what is in the bag. After creating the interview, I also used Kukarella to create a podcast-style audio interview, both to explore new tools and to enhance accessibility. It was important for me to find a tool that would change text to audio with two different voice so that it became an interview.

In terms of meaning-making, this new mode is beneficial. It was interesting for me to complete the activity; even though it was AI-generated, I found the responses intriguing.  On the other hand, the picture is much less time-consuming both for me to produce and for the audience to examine. A picture provides an instant overview, while an interview unfolds over time, segmenting the information into distinct pieces.

References:

Danielsson, K., Selander, S. (2021). Semiotic Modes and Representations of Knowledge. In: Multimodal Texts in Disciplinary Education. Springer, Cham. https://doi.org/10.1007/978-3-030-63960-0_3

Peña, E., & James, K. (2024). A framework of transmediation. Convergence, 30(5), 1610-1624. https://doi.org/10.1177/13548565231220325

The New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review 66(1), 60-92.