At the beginning of the seminar I’m taking in Race and Racism, we examined the historical origins of race– smirking at 19th century attempts to neatly classify and digest mankind into square boxes of color, things like Blumenbach‘s 5 categories (Caucasian, Mongolian, Ethiopian, American, Malay) and his offensive descriptions of the capabilities of each, how misguided they were, how clearly politically incorrect they are to us now.
One image stands out in particular– a chart, on the left axis, the races; on the right, a marking in each category of human competence, such that, summing up the checked boxes, one might literally rank different humans against another by racial category. The White man wins, in every grouping. It’s a glaring embodiment of early racist ideology, printed on paper, long since dismissed. It would be improbable to imagine a chart like that printed in a 21st century publication; the author would be fired, no doubt.
Yet the chart seemed eerily familiar to me. At first I couldn’t place it; I hadn’t seen it before. Then I realized: what it reminded me of was my own education, not in sociology or anthropology, but in Computer Science. As a graduate student, with a focus on Machine Learning and social applications, the very core of many of the algorithms I study and write is statistical classification, a major topic of research. What we are doing now– what Google does with its personalized search, what Facebook does with its unsettlingly accurate ads– is automating that same thought process of 19th century anthropologists. We look at a person, here, her technological imprint on the web, the traces she leaves, her purchase history, friends, and log-ins, then say: “what sort of human being are you”? And then, using our charts and tables– no longer printed ones, but weights on variables in our algorithms and databases of records– we first classify her, and after, yes, we rank her (sometimes we rank her first and then classify her, as well).
Users are commonly classified by gender, or what a machine can predict your gender to be, often quite accurately, and yes, different genders are ranked very differently. In the goods-driven world of online advertisement, a woman is worth differently than a man depending on what the product is, sometimes more, sometimes less. This entire pipeline is problematic to say the least; first, in the binary classification of gender; and then, in the ranking of different gendered individuals against one another; but our algorithms only reflect the constructs already driving these approaches.
Algorithms don’t quite yet classify users by race, or at least not outloud, because race is such a charged issue in most countries. But that doesn’t mean that when an algorithm doesn’t label a category outright, it doesn’t profile users. Algorithms, which learn, much like humans, based on history, only reinforce existing social constructs wherever they are used, because that is what they do: digest data, find a pattern, and make predictions according to that pattern. Harvard Professor Latanya Sweeney discovered that searches for racially-associated names were disproportionately causing targeted ads for criminal background checks and records to appear. These searches could go beyond offensive because targeted ads are reinforced by user behavior; if I click on that ad, I tell the machine that it was effective in targeting, reinforcing prejudiced thinking. Automated selection processes, which are beginning to gain popularity, such as automated admissions to schools and credit ratings, could cause real, harmful, physical ramifications.
This isn’t to say that there is anything inherently “evil” in Machine Learning itself; it’s a fascinating field of study, and could become a major tool in public health, disaster relief, and poverty alleviation. The machine is doing nothing new in its actions; it is merely a force multiplier of human behavior. I believe that classification is core to human cognition; and yes, we label others upon contact, always. Whether we like it or not, there is no way to escape being classified and classifying others. It’s impossible to meet someone without assigning some kind of underlying worth to them; it sounds ugly out loud, but our classification are essentially value-laden in order to be useful.
Ultimately, our machines only reflect our selves: it is vital to realize that computers are human, raised on human values, and there is no such thing as objective computation. The question that remains is: what kind of value systems will we feed our algorithms?