… or just creepy.
The all-girl middle school I went to was obsessed with rhythmic gymnastics. It was out of character for a school in such a modest catchment area, but we had this one lady teacher who was responsible for the entire dance-related curriculum, and apparently she was a big shot when she was younger.
We also had a random ‘sister partnership’ with a school in Russia. A small group of girls around our age would come for a few days in summer and practise gymnastics in our facilities. I seriously doubt that our gym was worth the yearly trip, but again rumour had it that it was a reputation thing.
We would sometimes go watch the practice because it was mesmerising. One day, as I still vividly remember, one of the girls took a bite of an apple, and as soon as that happened, their coach smacked her full force across the face that she literally flew a little. Mind you, we were no stranger to the concept of corporal punishment in school, but that day seems to have left such a lasting visual imprint on me. Or rather, I didn’t realise it did, but the memory resurfaces every time I read an article like this one.
- 100 ways to slightly improve your life without really trying (The Guardian, 2022)
- 100 ways you can make the world a better place (The Guardian, 2000)
Reading these two articles, published 22 years apart, side by side has given me many feelings. I am no stranger myself to tirelessly making such lists and being all hopeful (e.g. here, here, here, here, and here). For this year, however, I have a much toned-down one.
On the slow mend from a sickly December. The past few days have been particularly bad. At first I couldn’t believe that my precious winter break was disappearing through my fingers just like that, but according to my 사람 잡는 다이어리™, it looks like this has indeed been a yearly pattern and I have been refusing to acknowledge it.
Now the additional complication is that when noticing a “bodily glitch“, I can’t quite be sure if it is simply a part of ageing (and hence to be embraced), or if it is something to act upon, Covid or otherwise.
Anyway, I have had no choice but to lie down and ‘recharge’ for a week straight. Perhaps there is some goodness to that.
Tech protection at Kanda Myojin shrine. (December 2015)
Generally I feel a little hesitant with seasonal academic humblebrags, but here is one from me this year. It fits this category perfectly too, as I first pitched the idea to the journal in Feb 2020, just a few weeks before COVID-19 was declared a pandemic and the first wave of lockdowns around the world kicked in, and it finally came out in the world last week. In other words, the whole cycle of production coincidentally reflects the pandemic timeline and how we somehow soldiered through it, although none of us thought we would still be in the woods…
The collection sheds light on the digital experience of people on the move or caught between nation-states and ultimately poses the question of how we can create more inclusive models of digital democracy in Asia and beyond. Obviously we did not anticipate the passing of the Nationality and Borders Bill in the UK, nor did we schedule the issue to be released around the International Migrants Day, but anyway, here we are.
I would like to thank all the authors, reviewers, and the Managing Editor and other colleagues in Asiascape for their invaluable contributions. Now, to paraphrase Sheldon, everyone needs to read it. 🙃
A colleague once described my work as “interweaving warm-blooded humans and the screens“, and I was delighted with that description. At that confluence of the social and the technical, I am becoming more and more mindful of the unequal power dynamics between disciplines in the face of new methodological developments such as computational social sciences, biosocial research, and digital humanities. This post is intended to be a little space here for my own continuous reflection.
Recently at a workshop on digital tools for the humanities, a Stanford graduate student rather poignantly noted that oftentimes collaboration with computer scientists felt more like colonization by computer scientists. This statement, even if not true, is far too sharp to ignore. Frankly, I think it is true. Not long after that workshop, I attended a THATCamp, where I spent my time teaching folks how to use Gephi, and I tried to spend some time telling them that the network they create is the result of an interpretive act. I don’t think they cared, I think they just wanted to know how to make node sizes change dynamically in tandem with partition filters. This is an issue that has concerned me for some time: the way wholesale importation of digital tools, techniques and objects into humanities scholarship tends to foster a situation where rich, sophisticated problems are contracted to fit conveniently into software.
If we are creating a mess by generating so many haystacks of big data that we are losing all the needles, then we need to figure out a different kind of way of doing things, as we cannot sew new cloth without any needles. Whatever else we make of the ‘big data’ hype, it cannot and must not be the path we take to answer all our big global problems. On the contrary, it is great for small questions, but may not so good for big social questions. Social scientists need to find a way not to be complicit in the new wave of struggle over the politics of method that is intrinsic to what big data brings.
But as Hanna Wallach writes in this great article in Communications of the ACM, the methodology is often instead: “Why not use these large-scale, social datasets in combination with the powerful predictive models developed by computer scientists”… and see what we get? (I guess you can replace “powerful predictive models” by any (for social scientists) non-standard method.) So “computational social science” has come to mean something slightly different from what it sounds like.
Within computing we have generally only focused on the wondrous and have ignored the terrifying or delegated its reporting to other disciplines. Now, with algorithmic governance replacing legal codes, with Web platform enabled surveillance capitalism transforming economics, with machine learning automating more of the labor market, and with unexplainable, non-transparent algorithms challenging the very possibility of human agency, computing has never been more deinon. The consequences of these changes will not be fully faced by us but will be by our children and our students in the decades to come. We must be willing to face the realities of the future and embrace our responsibility as computing professionals and academics to change and renew our computing curricula (and the worldview it propagates). This is the task we have been given by history and for which the future will judge us.
We reject the vague conceptualization of the discipline of ML as value-neutral. Instead, we investigate the ways that the discipline of ML is inherently value-laden. Our analysis of highly influential papers in the discipline finds that they not only favor the needs of research communities and large firms over broader social needs, but also that they take this favoritism for granted. The favoritism manifests in the choice of projects, the lack of consideration of potential negative impacts, and the prioritization and operationalization of values such as performance, generalization, efficiency, and novelty. These values are operationalized in ways that disfavor societal needs, usually without discussion or acknowledgment. Moreover, we uncover an overwhelming and increasing presence of big tech and elite universities in highly cited papers, which is consistent with a system of power-centralizing value-commitments. The upshot is that the discipline of ML is not value-neutral. We find that it is socially and politically loaded, frequently neglecting societal needs and harms, while prioritizing and promoting the concentration of power in the hands of already powerful actors.