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Power in the Digital Age

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Corbyn! Trump! Brexit! Politics has never been more unpredictable, more alarming or more interesting. TALKING POLITICS is the podcast that will try to make sense of it all.

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Another one of my favourite podcasts, but this time it’s totally relevant to this course. Look at the synopsis for it:

synopsis of post

This particular episode looks at the ways in which politics and technology intersect, socio-critical and socio-technical issues around power and surveillance, the dominance of companies, and the impact of the general political outlook of the technologically powerful.

There are two things that I think are really relevant to the themes of the algorithmic cultures block. The first is about data. Data is described as being like ‘the land […], what we live on’, and machine learning is the plough, it’s what digs up the land. What we’ve done, they argue, is to give the land to the people who own the ploughs. This, Runciman, the host, argues, is not capitalism, but feudalism.

I’m paraphrasing the metaphor, so I may have missed a nuance or two. It strikes me as being different from the data-as-oil one, largely because of the perspective taken. It’s not really taken from a corporate perspective, although I think in the data-as-land metaphor there’s an assumption that we once ‘owned’ our data, or that it was ever conceived by us of as our intellectual property. I have the impression that Joni Mitchell might have been right – don’t it always seem to go that you don’t know what you’ve got ’til it’s gone – and that many of us really didn’t think about it much before.

The second point is about algorithms, where the host and one of his guests (whose name I missed, sorry) gently approach a critical posthumanist perspective of technology and algorithms without ever acknowledging it. Machine learning algorithms have agency – polymorphous, mobile, agency – which may be based on simulation but is nonetheless real. The people that currently control these algorithms, it is argued, are losing control, as the networked society allows for them to take on a dynamic of their own. Adopting and paraphrasing the Thomas theorem, it is argued that:

If a machine defines a situation as real, it is real in its consequences.

I say ‘gently approaching’ because I think that while the academics in this podcast are recognising the agency and intentionality of non-human actants – or algorithms – there’s still a sense that they believe there’s a need to wrest back this control from them. There’s still an anthropocentrism in their analysis which aligns more closely with humanism than posthumanism.

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Referencing and digital culture

It’s dissertation season in the Faculty I work in, which means it’s a time of referencing questions a-go-go. Like most things, referencing is a mix of common sense, cobbling something together that looks roughly OK, and being consistent about it. In the past three days I’ve been asked about referencing sourceless, orphan works found in random bits of the internet, live dance performances from the early 20th century, and – in another worlds collide moment – how to reference an algorithm.

A student was basing a portion of their argument on the results of Google’s autocomplete function – this kind of thing:

google autocomplete in action

My colleague and I were stumped. Who owns this algorithm? Well, Google. But it’s also collectively formed, discursively constituted, mutually produced. How do you reference something that is a temporary, unstable representation?

Pickering (1993, 2002) argues that ‘things’ move between being socially constructed via discourse and existing as real, material entities – a performativity which is “temporally emergent in practice” (p. 565), a kind of mangled practice of human and material agency which emerges in real time. This kind of autocomplete text (if ‘text’ is the right word) reflects this completely.

The act of referencing is one of stabilising, as well as avoiding plagiarism or practising academic integrity. When referencing online sources which don’t have a DOI or a stable URL, you are artificially fixing the location of something and representing it via text. You put ‘accessed’ dates to secure oneself against future accusations of plagiarism but also in view of the instability of the digital text. It’s not an ideal process, but it works.

And yet referencing – or indicating ownership of an autocomplete algorithm – seems to take this a step further. It leans towards reification, and it imbues the algorithm with a human and material intentionality which isn’t justified. It ‘essentialises’ what is fleeting and performative. So how, then, do you capture something which is, as Pickering writes it, ‘temporally emergent in practice?’

I suppose I should say what we told the student too, though it may not be right. We suggested that it didn’t need to be referenced, because it constituted their ‘own’ research; you wouldn’t reference the ‘act’ of reading, or the technology used to find, access or cite resources. You’d cite someone else’s published ‘version’ of the algorithm, but not your own. This uncovers another area where digital technology shapes and is shaped by ‘traditional’ practices and performances.

References

Jackson, A. Y. (2013). Posthumanist data analysis of mangling practices. International Journal of Qualitative Studies in Education, 26(6), 741–748. https://doi.org/10.1080/09518398.2013.788762
Pickering, A. (1993). The Mangle of Practice: Agency and Emergence in the Sociology of Science. American Journal of Sociology, 99(3), 559–589. https://doi.org/10.1086/230316
Pickering, A. (2002). Cybernetics and the Mangle: Ashby, Beer and Pask. Social Studies of Science, 32(3), 413–437. https://doi.org/10.1177/0306312702032003003

 

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Goodreads and algorithms, part the definite last

Good recommendation algorithms are really (really!) difficult to do right. We built Goodreads so that you could find new books based on what your friends are reading, and now we want to take the next step to make that process even more fruitful.

This quotation is from the Goodreads blog, a post written by Otis Chandler, a Goodreads CEO. The “next step” to which he refers is Goodreads’ acquisition of the small start-up, Discovereads, which was developing algorithms around book recommendations. The algorithms used by Discovereads were multiple, based on book ratings from millions of users, and tracking data patterns of how people read, how they rate, the choices they make, what might influence them.

It’s roughly based on the sorts of algorithms that drive Netflix, though there’s an obvious difference between the two platforms, and it’s not the type of content. Goodreads isn’t a publisher nor a producer of its own content; it isn’t promoting its own creations but rather can influence the user to spend money in a way that Netflix, which works to a different economic model, may not. Chandler admits this: one of the goals in adopting the Discovereads algorithm is that it will improve marketing strategies, ensuring that sponsored content (books promoted to users) will be more up their street.

Given this, then, it’s possible to say that the way recommendations work in Goodreads is based on at least three things:

  1. The ratings provided by an individual at the point they sign up – part of the process of getting a Goodreads account is adding genres you’re interested in, and “rating” a (computer-generated) series of books
  2. The algorithms at play are monitoring human patterns of reading and rating and, presumably, analytics and big data collected on what might encourage a person to add a recommended book to their lists (and perhaps, too, to their shopping basket)
  3. The Amazon connection: the fact that Goodreads isn’t providing its own content, and that it’s owned by Amazon, makes a particular sort of economic link. Not only does it incentivise Goodreads promoting specific economic content, but it means that Goodreads can influence how and where consumers’ money is spent. Presumably analytics based on how often Goodreads’ recommendations leads to a purchase is fed back into the recommendation system to improve upon it.

Knox (2015) suggests that actor-network theory might account for the “layers of activity involved” in the complex, often hidden, and often automated ways in which humans and non-humans interact in the development and deployment of algorithms. One of the principal benefits of this approach (and there are many) is that it inherently assumes that the human and non-human are working together. This is not always self-evident, and the quotation at the top of this post suggests that the two are seen to be in opposition. The incorporation of the Discovereads algorithm, it is implied, will lead to a fundamentally different way of generating recommendations. It signals a move from human-generated recommendations (what your friends are reading) to computer-generated ones, based on this algorithm.

The responses to the blog post written by Chandler suggest that this binary is presupposed by Goodreads users as well. The posts below, for example, clearly espouse the benefits of both ‘routes’ to recommendations. But they suggest that recommendations are either human- or computer-generated: there’s no indication that non-human interference in extant friend-generated recommendations, nor any human influence in the computer-generated ones. It’s a code-based version of the binary we’ve encountered lots in the past eight weeks: the perception that the options of technological instrumentalism and technological determinism are the only ones.

The reality, of course, is that it’s a false binary. It’s not a choice of human or non-human but – as Knox outlines – both are present. The difference, then, to which Chandler refers, the change heralded by the acquisition of Discovereads, isn’t necessarily in the source of the content, but in the perception of that source. It’s in the perceived transparency or hiddenness of the algorithm.

References

Chandler, O. (2011). Recommendations And Discovering Good Reads. Retrieved 11 March 2017, from http://www.goodreads.com/blog/show/271-recommendations-and-discovering-good-reads
Knox, J. (2015). Critical Education and Digital Cultures. In M. Peters (Ed.), Encyclopedia of Educational Philosophy and Theory (pp. 1–6). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-287-532-7_124-1
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Goodreads and algorithms, part the fourth

In this (probably) last instalment of experimenting with the Goodreads algorithm, I’m particularly playing with specific biases. Joy Buolamwini, in the Ted talk I just watched (and posted), says this:

Algorithmic bias, like human bias, results in unfairness.

It would be hard, I think, to really test the biases in Goodreads, and especially insufficient to draw conclusions from just one experiment, but let’s see what happens. I’ve removed from my ‘to-read’ shelf all books written by men. I’ve added, instead, 70 new books, mostly but not exclusively from lists on Goodreads of ‘feminist’ books or ‘glbt’ books [their version of the acronym, not mine]. Every single book on my ‘to-read’ shelf is written by someone who self-identifies as female.

And after a little while (processing time again), my recommendations were updated:

Of the top five recommendations, 1 is written by a man (20%); of the fifty recommendations in total, 13 are written by men (26%).

I then reversed the experiment. I cleared out the whole of the ‘to-read’ shelf, and instead added 70 books, nearly exclusively fiction, and all written by people who identify as male.

And again, a slight pause for processing, and the recommendations update. Here are my top five:

Two of the top five books recommended are written by women, and of the 50 in total 7 were by women (14%).

So when the parameters are roughly the same, and with the very big caveat that this may be a one-off, it seems that Goodreads recommends more books by men than by women. Is this bias? Or just coincidence? Probably quite difficult to tell with just one experiment, but it may be worth repeating to learn more.

Finally, one weird thing. In both experiments, there were two books that appeared on the full recommendations list. One is by Anthony Powell, A Dance to the Music of Time which, given the general gravitas of the books I added in both experiments, is fairly understandable. The other, though, is this:

 

Bill Cosby’s ‘easy-to-read’ story, aimed at children, is included because I added John Steinbeck’s East of Eden? Unfortunately I have no idea why it was in the women-only list, because I didn’t check at the time, but that feels like a really, really peculiar addition.

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Goodreads and algorithms, part trois

So far, the Goodreads recommendations based on my ‘to-read’ pile haven’t been that great, so I’ve done a few more experiments.

First, I removed from my ‘to-read’ list anything that didn’t strictly fall into the category of literary fiction or reasonably highbrow non-fiction, and I added to it six books, along a similar theme: Ulysses by James Joyce, Finnegan’s Wake by Joyce too, Infinite Jest by David Foster Wallace, The Trial by Kafka, A la recherche du temps perdu by Proust (the French version, no less), and The Brothers Karamazov by Dostoevsky.

And not much changed. Mainly because it doesn’t update automatically – again I’m noticing a delay in the algorithm working. But I noticed something else when deleting things from the list. Goodreads automatically ranks the books you add to the list, in the order that you’ve added them. This makes complete sense – I expect many people choose their reading in a far less haphazard way than I do. And in any case, this explains why books about climate change were so prominent in the recommendations – This Changes Everything was first on my list.

Goodreads also allows you to edit the ranking, so I’ve moved the two James Joyce books I added to positions #1 and #2, and I’ve moved the climate change book to #20.

Again, nothing happened. The recommendations were still based on books that I had now removed from the list. I refreshed the page, logged in and out, and no change. So I went back, and added a 7th book: Robin Stevens’ Murder Most Unladylike, which is aimed at 10 year olds. And new recommendations appeared.

ALL of them are based on the items I added earlier (not the most recent addition) – you can see the first two are about Proust, and yet NONE of them are based on the James Joyce books I moved to top ranking on the list.

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Goodreads and algorithms, part 2

Earlier today I went through all fifty recommendations based on my ‘to-read’ list, and tidied them up: things that genuinely suited my interested I added (seven books in total), and things that didn’t suit, I deleted.

Since then – and it’s been about four hours – my ‘to-read’ recommendations have vanished.

I’m guessing I fall into the last category here, and they’re ‘in the process of generating recommendations’. I would have expected the algorithm to work instantaneously, desperate to populate, but clearly it’s a slower process than that.

So, anyway, I then went and added three more items to the list, bringing the total up to twenty. And immediately it came back.

None of the titles above are listed here based on the three new books I just added, and three of them are a result of the same book, Under the Udala Trees by Chinelo Okparanta.

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Goodreads and algorithms

I’ve been using Goodreads to track what I’m reading for the past three years, and I thought I’d investigate the algorithm that drives its recommendations. I’ve added almost 300 books to Goodreads since I started using it, nearly all of which I have read and rated, so there’s a lot of data there on my reading habits. However, I don’t use Goodreads to plan what I’m going to read next – I don’t use it as a wishlist but as a way to record things. Subsequently I currently have just ten items on my ‘to-read’ list:

It’s not the most eclectic list of literature – two non-fiction (on Russian history and climate change), and eight novels which would probably just about fall into the genre of ‘literary fiction’ (as meaningless as that is). But I feel, at least, that this list roughly reflects my reading habits.

The recommendations right now are based on three of the ten books listed: This Changes Everything by Naomi Klein accounts for three of the books, The Romanovs by Simon Sebag Montefiore is the reason why there’s now a picture of Stalin on this blog, and Amy Tan’s The Joy Luck Club is responsible for the fifth one. Interestingly (maybe), I’ve only heard of one of these books. I’m an English Literature librarian, my partner is an English Literature teacher, and my idea of a fun day out is to a bookshop.

Goodreads lets you know why the recommendation is included, which is pretty helpful.

And it also gives some guidance on how to improve your recommendations.

 

The trouble is that I’m not really that interested in reading any of these books. My goal, using the guidance above, is to get my top five recommendations to actually be helpful, to suggest books that I want to read right now (I can think of at least a dozen off the top of my head). I’m going to try to fix it so that the algorithm reflects what I want, rather than the other way around.

With my librarian hat on, it might also be useful to compare how Goodreads recommends books to how a discovery layer (also known as a library catalogue) can recommend articles and other titles – if I have time I’ll look at that too.

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What I’m reading

Algorithms in library catalogue results

Vaughan, J. (2011). Chapter 5: Ex Libris Primo Central. Library Technology Reports, 47(1), 39–47.

 

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March 10, 2017 at 07:31AM
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Included because – from my perspective as an academic librarian – the way in which library catalogues (or discovery layers) order results is absolutely crucial. I have a lot of anecdotal evidence to suggest that if it isn’t in the first few results, students will assume we don’t have it; decent relevancy rankings has a genuine impact on students’ ability to research, and clear and ostensible implications for their learning.

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Identity, Power, and Education’s Algorithms

Late Friday night, Buzzfeed published a story indicating that Twitter is poised this week to switch from a reverse-chronological timeline for Tweets to an algorithmically organized one.

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An oldie, but one packed with interesting discussion of the relationships and intersections between algorithmic culture, freedom, marketing, values, discriminations. This is the crucial quotation:

 

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From Wired magazine

Interesting article from Wired, 25th February 2017

In this article, Henri Gendreau traces the growth of ‘fake news’ – fake trends on Facebook, the concept of ‘fake news’ becoming known and publicised, the rise of Donald Trump and the rise of his engagement with ‘fake news’.

It’s a(nother) good example of the ways in which algorithms can shape culture, and again in a wholly negative way. It tends to be negative, doesn’t it? Or at least, it’s the less positive stories that are making the news.

So how do we deal with it? As a teacher of information literacy, these are both golden and worrying times – the need for critical information literacy has never, ever been greater.