Injustice ex machina
There are some things I think about but struggle to articulate, especially in the heat of an argument with a friend. Cory Doctorow succinctly captures one such idea here:
Empiricism-washing is the top ideological dirty trick of technocrats everywhere: they assert that the data “doesn’t lie,” and thus all policy prescriptions based on data can be divorced from “politics” and relegated to the realm of “evidence.” This sleight of hand pretends that data can tell you what a society wants or needs — when really, data (and its analysis or manipulation) helps you to get what you want.
If you live in a country ruled by a nationalist government tending towards the ultra-nationalist, you’ve probably already encountered the first half of what Doctorow describes: the championship of data, and quantitative metrics in general, the conflation of objectivity with quantification, the overbearing focus on logic and mathematics to the point of eliding cultural and sociological influences.
Material evidence of the latter is somewhat more esoteric, yet more common in developing countries where the capitalist West’s influence vis-à-vis consumption and the (non-journalistic) media are distinctly more apparent, and which is impossible to unsee once you’ve seen it.
Notwithstanding the practically unavoidable consequences of consumerism and globalisation, the aspirations of the Indian middle and upper classes are propped up chiefly by American and European lifestyles. As a result, it becomes harder to tell the “what society needs” and the “get what you want” tendencies apart. Those developing new technologies to (among other things) enhance their profits arising from this conflation are obviously going to have a harder time seeing it and an even harder time solving for it.
Put differently, AI/ML systems – at least those in Doctorow’s conception, in the form of machines adept at “finding things that are similar to things the ML system can already model” – born in Silicon Valley have no reason to assume a history of imperialism and oppression, so the problems they are solving for are off-target by default.
But there is indeed a difference, and not infrequently the simplest way to uncover it is to check what the lower classes want. More broadly, what do the actors with the fewest degrees of freedom in your organisational system want, assuming all actors already want more freedom?
They – as much as others, and at the risk of treating them as a monolithic group – may not agree that roads need to be designed for public transportation (instead of cars), that the death penalty should be abolished or that fragmenting a forest is wrong but they are likely to determine how a public distribution system, a social security system or a neighbourhood policing system can work better.
What they want is often what society needs – and although this might predict the rise of populism, and even anti-intellectualism, it is nonetheless a sort of pragmatic final check when it has become entirely impossible to distinguish between the just and the desirable courses of action. I wish I didn’t have to hedge my position with the “often” but I remain unable with my limited imagination to design a suitable workaround.
Then again, I am also (self-myopically) alert to the temptation of technological solutionism, and acknowledge that discussions and negotiations are likely easier, even if messier, to govern with than ‘one principle to rule them all’.
