Linking Assignment: Speculative Futures

Task 12: Jocelyn Fung

This was a super fun speculative future to think about! I really enjoyed how she used an animated video that she voiced over to tell the story of her speculative future. This was a completely different approach to my Triage flyer.

Some points Jocelyn made that stood out to me were how AI and automation of certain tasks can impact jobs and industry. I wonder about the moral crumple zone in education? How will we know if a students learning is being adequately met with their AI powered teacher, and will this be enough to meet their needs? We are well aware that education and learning is a highly social process, and our teachers are often valuable role models for us. How would that change how we think and interact if the majority of our educational lives are lived with AI? Will we be less empathetic? Will we be smarter?  How does AI change the dynamic of collaborating human to human?

I appreciated how we both took this opportunity to speculate about our individual fields of work, and how AI could potentially automate it and impact it. AI and automation and the loss of jobs as a result is always in the news. Is this just click-bait? Or is there something real we need to be worrying about? I would imagine we are a long ways away from seeing too many impacts to job security as a result of AI… I HOPE that AI is well understood and the issues of repeating bias, racism, sexism etc are eradicated before it becomes mainstream use.

Linking Assignment: Attention

Task 10: Liana Ranallo

I really enjoyed reading Liana’s take on the attentional task. She really hit it home when she identified our expectation of technology being in perfect working conditions, be optimally functional, as well as user friendly. If not this quickly leads to frustration. I definitely see this when working on our new EMR system when requests are made of the software to function as smoothly and seamlessly as an iPhone, and when we are not able to deliver on this, the end-users become frustrated and disillusioned. This point also emphasizes Liana’s discussion on the need to remain open minded and patient!

I was quite impressed with how she took her time and was patient with the task, paying more attention to details of the poor UI design than myself. I found I got frustrated because I couldn’t get passed a certain place, therefore I YouTubed how to ‘win’ the game. Liana did an excellent job of capturing the problematic UI design and itemizing each issue out. She was able to identify how inappropriate use of formatting, such as font size, color and location, made it distracting to know how to navigate the site.

Later, we both discussed ‘dark patterns’ of UI design. Despite not knowing the term for this practice, we were both acutely aware of it and could recount ways we have experienced it as consumers of the internet.

Linking Assignment: Network Assignment

Task 9: Joseph Villella

For this linking assignment, I am going to compare and contrast my work to Joseph’s. Surprisingly, he found it fairly easy to use Palladio to look at different groupings of data. Personally, it was difficult for me, and figuring out how to use the site took some time and investment. However once I figured it out, I could better manipulate the data looking for meaning.

I appreciated how Joseph untangled the data and his attempt at interpreting it and giving what he is seeing meaning. I could not help but notice as I was reading his interpretations, I was questioning them. I wondered how he was able to come to the conclusion that a group chose certain songs due to their impressive sounds. Near the end of his analysis, he shared how meaning making with data that lacks context was extremely difficult, and the explanations he has provided would likely be wrong or incomplete. It was here that Joseph and I had the same epiphany. We both came to the conclusion that it is impossible to derive true meaning from data without more context. Additionally, we both identified how our online activity, when tracked, can create data points. This plethora of data points is then analyzed by algorithms, looking to derive meaning from them for the purposes of selling us a product.

Linking Assignment: Mode Bending

Task 7: Erin Duchesne

I loved Erin’s mode bending use of Tik Tok!! So I decided to do the same thing! This is another example of how another peers creativity gave me ideas for my own task!

Similarly to Erin, I also see the amazing and creative ways content creators engage with Tik Tok and wanted to go out on a limb to try and make my own Tik Tok! It is interesting how new forms of technology can be intimidating to learn. I am grateful to learn that mode-bending also got Erin out of her comfort zone, and she felt similarly to me!

Erin put quite a bit of thought into her Tik Tok, carefully curating and matching her short clip to the appropriate sound bite to invoke a mood. I took a bit more of an informal approach with the goal of incorporating more humor. Erin did a great job of putting transitions into her clip, between each item, while adding a funny comment with voice over text. I opted to showcase what was in my bag by filming what a ‘day in the life of a MET student’ looks like. I also used the text to speech functionality to make it more ‘trendy’ and ‘viral’.

Linking Assignment: Emoji Story

Task 6: Jocelyn Chan

I felt somewhat stumped when it came to this task. Therefore I sought out other classmates blog’s to see how they interpreted and approached this assignment. Jocelyn’s page was phenomenal and it was a great example for me to draw from. It helped deepen my understanding of my own thinking and how I would approach creating an Emoji story. I believe her approach resonated with me because we have similar styles in thinking and organizing information. Very logical and systematic. We also approached conveying our emoji story with a similar logic and flow. So it comes as no surprise that she linked to my Emoji story task as well!

Her site offers and demonstrates far more digital and technological literacies than mine. For starters, I am extremely narrative in my approach to work and expressing ideas. This term I felt like I utilized more authoring tools to visually display my thinking and approach to assignments. Whereas Jocelyn went above and beyond in her post to visually display her thinking.

She has a robust mind map, as well as text that is color coded and numbered to compare and contrast her thoughts to my own. This is incredible. I wish I knew how to incorporate the level of detail Jocelyn is capturing in her site, within my own. She has also figured out how to incorporate rich formatting, which I haven’t even figured out how to do on my blog site! This allows her to structure and organize the content on her page, making it more meaningful and interesting to learn. Whereas my site can be quite text heavy and daunting for the reader!

Linking Assignment: Twine Task

Linking Assignment

Throughout the duration of the course, we were encouraged to visit our classmates blog spaces to engage with their content and find linking relationships between our content. I am very grateful for the opportunity to see my classmates work before, during and after I have completed my own because it broadens and extends my understanding of the concepts. Below I have listed 6 other classmates who’s work either grew my understanding, confirmed my understanding, or who challenged my understanding of the concepts. There were other classmates blog sites that I visited which aren’t represented in this list. Sometimes I left comments on my peers blog sites as well. The headings are hyperlinked to each peers post!


Task 5: Jennifer Guth 

Jennifer and I both created a Twine game geared towards healthcare. I really enjoyed seeing her interpretation of Twine and how this could be utilized for gamified learning.

Her game was incredibly informative! I was especially impressed with how she was able to provide additional, outside learning resources via hyperlinks. I was not able to figure out how to do this and therefore didn’t think it was possible. Due to the hyperlinks she incorporated, I could supplement my learning, based on my own learning assessment, while working through the game. Seeing how she executed her game has given me more knowledge about the affordances of Twine.

I do agree with Jennifer’s cautious outlook on the affordances of the game overall. It is very text heavy and is easy to click through without actually taking in the text you are reading. I also appreciated the points she made about Twine not being portable, hindering ubiquitous learning as well as how challenging it is to collaborate on the same Twine game. Additionally, we both discussed the issue of usability around using this platform for gamified learning, the challenges surrounding the requirement of HTML coding knowledge as well as the limitations of learning itself. To my surprise, when I went back to her site for this part of the assignment, I saw she also linked to my Twine task!

Task 7: Mode-Bending

Mode-bending was so difficult! Perhaps I need to work on my own digital literacy. While TikTok was incredibly user friendly and easy for me to generate a short video clip, what I struggled with most was trying to get this video posted to my blog site. Not only did I have to choose the correct permissions but I had to make sure the file type was correct. I had to convert the mov file into an MP4 file in order for it to work. WordPress is a really tough site for me to figure out how to use. I have also noticed that other classmates have done a great job of linking to other’s works and embedding content seamlessly into their posts, while I am not able to! Even the size of the video I have uploaded in this post is bigger than I had wanted and I don’t know how to make it smaller!

This exposure to digital technologies during the MET has been very beneficial for me as an individual and as an educator. It is aiding in the development of my own technological literacy. Being more literate in this area allows me to make informed decisions about the use of educational technology in my program, and empowers me to support and encourage my learners to venture into this world as well. One type of hypermedia I often use to help deliver educational content is YouTube. There are many doctor’s on YouTube that create their own educational channels where they break down complex concepts into more manageable sizes. Being the more knowledgeable other, I try to guide my learners to the videos and channels that I think do a good job of explaining certain concepts. One of the issues with a hypermediated, highly networked environment, is that the learner who is new to these concepts, can get lost and disoriented easily (Dobson & Willinsky, 2009). Hopefully with my guidance, I can bring hierarchy and structure to their YouTube tutorial navigation. Why YouTube tutorials are so effective is because you can listen to someone explain a concept while drawing it out. The other benefit of YouTube is how it’s algorithms show you more videos based on your search, therefore you can see multiple people explain a concept in different ways. As well, videos can be paused, replayed, and saved for later. This level of control over one’s learning puts the power back into the hands of the student, breaking down barriers to knowledge sharing, and promoting autonomy in ones learning (Dobson & Willinsky, 2009).  It also promotes critical thinking and provides immediate access to educational material.

References

Dobson, T. M.,& Willinsky, J. (2009). Digital literacy.

Task 12: Speculative Futures

I have never heard of speculative fiction/futures before so this was an interesting module! It was fascinating to learn how the act of speculating and being creative with our thinking can open us up to new opportunities for invention and problem solving. One of the topics frequently mentioned in this weeks readings and podcasts was that of medicine, and its speculative future and what role AI will play in it. It comes as no surprise that AI is being tested in all types of industry, from education to law, so why not medicine as well? And it has been shown that AI can diagnose certain diseases, exceptionally faster and with more accuracy than physicians. So I thought that I would take a stab at speculating the role AI could potentially play in the future of medicine.

Welcome to your new and improved hospital! Equipped with the latest and greatest in technology. With advancements in our health informatics and the latest in machine learning software, we are happy to introduce to you, the first of its kind, TriageMe! Think of TriageMe as your virtual nurse. They will welcome you in the ER waiting room, guiding you through the triage process. To get you started, read this flyer, follow the directions and someone will be with you shortly!

This was an interesting activity and it makes me wonder what is currently being thought of and designed for the future state of healthcare. A couple thoughts I had about AI in medicine relate to the moral crumple zone, accessibility, and bias.

In the Bellwether podcast hosted by Sam Greenspan, a valuable point was made about the moral crumple zone. He described this as the space between AI error and failure of human intervention, resulting in harm. Then, the potential outcome of humans taking the fall and being held criminally liable for errors in AI judgement. I could not help but think about the many scenarios where a moral crumple zone like this could occur in health care, if a doctor or nurse were to fail catching and intervening when AI makes errors in judgement. AI would never be able to function without human observation and ensuring accuracy, but as Sam points out, maintaining hypervigilance 100% of the time, as a human, is not possible. To engage with AI safely in medicine, it would need to be used as a tool, an adjunct, rather than a replacement to doctors and nurses.

The other thought I had was how this technology would either make healthcare more or less accessible. Could AI combat current staffing issues we are seeing across the nation by streamlining certain things? Would it worsen it due to healthcare providers time being taken away from direct patient care to manage software and technology?

Lastly, we are acutely aware of how AI can be racist and sexist. AI essentially mirrors the current state of society, since that is where datasets are extrapolated from. Therefore there is a considerable risk for AI powered healthcare software to replicate these social injustices we are trying to move away from. How could AI in healthcare further disadvantage those who are marginalized or oppressed? Phillips-Beck et al. (2020) summarize some of the key issues related to racism within Canada’s healthcare system. While their list is not exhaustive, they mention one prevailing issue related to the outright exclusion of First Nations because healthcare is not made accessible to them. How would a dataset, that excludes a key population be able to train AI without implicit bias? It would be very difficult. The addition of AI into industry cannot be done without extreme caution and intention because it can have catastrophic effects.

References

Phillips-Beck W, Eni R, Lavoie JG, Avery Kinew K, Kyoon Achan G, & Katz A. (2020). Confronting racism within the canadian healthcare system: Systemic exclusion of first nations from quality and consistent care. Int J Environ Res Public Health. doi: 10.3390/ijerph17228343. PMID: 33187304

Task 11: Algorithms | Detain Release

This week I decided to dive into the world of algorithms seeing as this topic is gaining traction the more we become aware of their pervasive use in everyday life. Knowingly, I engaged with an algorithmic, simulated activity called ‘Detain/Release’. The premise of the activity is to engage with a simulated, pretrial algorithmic assessment tool. You review the cases of 25 defendants, choosing to either detain or release them based on their risk assessment and some other limiting pieces of information. Here is an example of what information you are given to make this decision:

During the activity, I quickly realized I am not cut out for law enforcement! First off, I found myself leaning towards trusting the defendants testimony and opted to release them more often than detain them. Maybe it is the nursing background, but, I really did not like making the decision to detain someone. Secondly, I knew that an algorithm was making suggestions about the potential risk of releasing these individuals. With that knowledge, and knowledge of algorithmic limitations in judgement, I found myself less trusting of the AI’s recommendation than the individual. Due to these conflicting thoughts, and an enormous lack of context, I struggled to make a decision to detain or release the 25 suspects.

Kate Crawford does an excellent job of highlighting the limitations of algorithms and AI in her book (image links to a video), Atlas of AI. She discusses in detail how algorithms reinforce oppression due to embedded bias in datasets and uses a dataset of mugshots to make her case. Crawford (2021) identifies how these images are stripped from their context and are taken during times of extreme vulnerability without consent. These images then become data sets and there is this presumption that what the machine ‘sees’ is neutral. Crawford (2021) argues that these images are anything but neutral. “They represent personal histories, structural inequities, and all the injustices that have accompanied the legacies of policing and prison systems in the United States” (Crawford, 2021, p. 94).

I watched another TedX video by Hany Farid on the danger of predictive algorithms in law enforcement. I would recommend starting the video at 4:49. Hany Farid (2018) does an excellent job of proving Crawford’s points mentioned above. He walks the audience through a study they did which demonstrates how racism can be embedded in predictive modelling software as a result of systematic inequities. In the US, an African American is more likely to have a criminal record due to long standing societal and systematic injustices. Therefore, when prior criminal records are used as a data point for predictive modeling to determine risk of re-offence, the algorithms will be inherently biased to choose African American individuals. His study also demonstrated how these so-called advanced technologies have the same predictive abilities as a random person from the street. Thus, algorithms which are supposed to be overcoming these social issues are merely mirroring and replicating them. “Big data, data analytics, AI, ML, are not inherently more accurate, more fair, less biased than humans.” So why do we use them?

Anecdotally, I contacted a friend of mine who works in law enforcement on Vancouver Island to share with him the Detain and Release activity along with the article about this type of policing. I wanted to hear what he had to say from his perspective. To my pleasant surprise, he was quite aware of these tools and their limitations, he continued to explain that it isn’t used at his detachment but stated larger detachments might use it. However, he did acknowledge how minorities and poor communities experience higher rates of crime and how these tools can perpetuate that cycle of over policing. He said if we were to look at what is being done with the justice system in Canada with decriminalizing drug possession for personal use and petty crimes, and the culture of the judge not staying charges or not enforcing conditions of release, this becomes a beginning place to start combating these inequities.

References

Crawford, K. (2021). The Atlas of AI.

Farid, H (2018). Retrieved from YouTube: https://www.youtube.com/watch?v=p-82YeUPQh0

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