‘These algorithmic systems are not standalone little boxes, but massive, networked ones with hundreds of hands reaching into them, tweaking and tuning, swapping out parts and experimenting with new arrangements…we need to examine the logic that guides the hands…’ (Seaver 2013: 10)
Early on this block, I played with a range of different algorithms, looking at the recommendations served up by my existing activity. I have created a “recommendations feed” for my lifestream, and also made general notes and screenshots in number of posts.
Later on, and in thinking specifically about digital education, and tying the activity with our MOOC exploration, I first looked at my FutureLearn recommendations, and finally, focused on Coursera – where I was able to provide false data for my “profile” and “learning plan” and see the alterations in recommended courses.
Some initial conclusions
Many of the “recommendation engines” I played with, such as SoundCloud, Spotify and Twitter, led me into what I perceived to be a “you loop“, “filter bubble” or “echo chamber”. Google’s autocomplete showed some signs of reproducing existing biases, perhaps amplifying dominant views held by other users. Google may also be making assumptions based on data they hold about me, which may have skewed YouTube search results, although it would be interesting to compare results with other users or in different locations, an approach Kitchin (2017: 21) discusses. I have written a little about ethical concerns and my Google data also.
Moving my focus to Coursera, in its recommendations they appeared to privilege courses with a information technology/computer science focus, although the range of available courses is clearly a factor too. In any case, the founders’ background in computer science, and education at a Western university, shone through despite attempts to tweak my “profile” and “learning plan” (I have written a little about the “profile” ethical issues also). This appears to be a common theme in “apps” or websites” developed by Western companies, and the design processes utilised (whereby products are developed first for the “profile” of the founder(s), and secondarily for others) arguably creates exclusions for some (and a strong sense of “this wasn’t designed for me”) and inclusions for others (notably switching my profile to “software engineer” produced a wealth of “relevant“ results which I could tailor to my choosing).
My artefact: Algorithmic systems: entanglements of human-machinic relations
You can see the Coursera ‘algorithmic play’ posts in a lifestream feed, and also through this CodePen (which brings in the same feed but presents, ranks and orders it slightly differently). How might consuming the feeds through different ranking systems affect your perception, and how might the pre-amble surrounding it have changed your conclusions and what you click on?
You can also access the “code behind” the CodePen. However, even though access is granted to this code, it will not necessarily be easy to make sense of it (Kitchin 2017: 20; Seaver 2013). While presented as ‘open’ source, is it really ‘open’ and who for? What exclusions might this create?
The CodePen (while slightly misleadingly called an “Educational Algorithm”) is a very basic attempt to present the feed of Coursera recommendations in a different fashion for comparison, while provoking some thought around algorithmic systems in general. There is no complex processing, just a little reordering. It also does not store any data (information entered is used within the browser to rank articles, but not stored in an external database), and the text descriptions are fictional – it is just for visual/demonstration purposes.
Reflections on the CodePen
Inspired by Kitchin (2017: 23), I have written a few words reflecting on my experiences, while acknowledging the limitations of the subjectiveness of this approach.
The CodePen was adapted (“forked“) from an existing CodePen, which provided the majority of the base upon which (with my very limited skills!) I could tweak very slightly to add a basic feed of the Coursera recommendations by pasting in another bit of code (which turned out to be old and broken) and then ultimately another bit of example code (this is from a Google-“led” project). It is very much a case of different bits of code “hacked” together, full of little “bugs” and “glitches” and not particularly well designed or written! I was keen to strictly limit the time spent on it, although I know much time could be spent tidying and refining it.
Presumably, similar time constraints, albeit with more resources/testing, affect development of, say, Facebook/Google etc. algorithms and lead to mistakes. After all, Facebook’s internal motto used to be ‘move fast and break things’, although this race to create a “minimum viable product” and “disrupt” (regardless of the consequences) is increasingly criticised.
In any case, this CodePen is like a snapshot of an experimental “work in progress” (which others are welcome to “fork”, use and adapt) and brings to mind the messy, ontogenetic, emergent, and non-static nature of algorithmic systems (Kitchin 2017: 20-21).
Ultimately, my aim is to raise some questions about some details of this process and, since most of the code was “stuck together” from bits of free and ‘open’ source code, how the culture of the individuals/teams is significant too. As Seaver (2013: 10) puts it…
‘…when our object of interest is the algorithmic system, “cultural” details are technical details — the tendencies of an engineering team are as significant as the tendencies of a sorting algorithm…’
…and, given that a large portion of the code came from a Google-led project (and Twitter too), how might the tendencies of those teams have created inclusions/exclusions? Furthermore, as the focus of my artefact is Coursera, whose founders both have experience working at Stanford University and Google, how might the tendencies there have guided Coursera and, subsequently, my CodePen?
Finally, given that Coursera presents itself as a universal educational space, where its vision is ‘a world where anyone, anywhere can transform their life by accessing the world’s best learning experience’, what are the implications of this for digital education? In my initial explorations, my perceptions are that computer science and information technology disciplines from Western universities are prioritised by Coursera, in general and through their algorithmic systems. However, further research is needed to, as Kitchin (2017: 25) puts it, ‘unpack the full socio-technical assemblage’.
