All the problems with our paper, “Racial categories in machine learning”

by Sebastian Benthall

Bruce Haynes and I were blown away by the reception to our paper, “Racial categories in machine learning“. This was a huge experiment in interdisciplinary collaboration for us. We are excited about the next steps in this line of research.

That includes engaging with criticism. One of our goals was to fuel a conversation in the research community about the operationalization of race. That isn’t a question that can be addressed by any one paper or team of researchers. So one thing we got out of the conference was great critical feedback on potential problems with the approach we proposed.

This post is an attempt to capture those critiques.

Need for participatory design

Khadijah Abdurahman, of Word to RI , issues a subtweeted challenge to us to present our paper to the hood. (RI stands for Roosevelt Island, in New York City, the location of the recently established Cornell Tech campus.)

One striking challenge, raised by Khadijah Abdurahman on Twitter, is that we should be developing peer relationships with the communities we research. I read this as a call for participatory design. It’s true this was not part of the process of the paper. In particular, Ms. Abdurahman points to a part of our abstract that uses jargon from computer science.

There are a lot of ways to respond to this comment. The first is to accept the challenge. I would personally love it if Bruce and I could present our research to folks on Roosevelt Island and get feedback from them.

There are other ways to respond that address the tensions of this comment. One is to point out that in addition to being an accomplished scholar of the sociology of race and how it forms, especially in urban settings, Bruce is a black man who is originally from Harlem. Indeed, Bruce’s family memoir shows his deep and well-researched familiarity with the life of marginalized people of the hood. So a “peer relationship” between an algorithm designer (me) and a member of an affected community (Bruce) is really part of the origin of our work.

Another is to point out that we did not research a particular community. Our paper was not human subjects research; it was about the racial categories that are maintained by the Federal U.S. government and which pervade society in a very general way. Indeed, everybody is affected by these categories. When I and others who looks like me are ascribed “white”, that is an example of these categories at work. Bruce and I were very aware of how different kinds of people at the conference responded to our work, and how it was an intervention in our own community, which is of course affected by these racial categories.

The last point is that computer science jargon is alienating to basically everybody who is not trained in computer science, whether they live in the hood or not. And the fact is we presented our work at a computer science venue. Personally, I’m in favor of universal education in computational statistics, but that is a tall order. If our work becomes successful, I could see it becoming part of, for example, a statistical demography curriculum that could be of popular interest. But this is early days.

The Quasi-Racial (QR) Categories are Not Interpretable

In our presentation, we introduced some terminology that did not make it into the paper. We named the vectors of segregation derived by our procedure “quasi-racial” (QR) vectors, to denote that we were trying to capture dimensions that were race-like, in that they captured the patterns of historic and ongoing racial injustice, without being the racial categories themselves, which we argued are inherently unfair categories of inequality.

First, we are not wedded to the name “quasi-racial” and are very open to different terminology if anybody has an idea for something better to call them.

More importantly, somebody pointed out that these QR vectors may not be interpretable. Given that the conference is not only about Fairness, but also Accountability and Transparency, this critique is certainly on point.

To be honest, I have not yet done the work of surveying the extensive literature on algorithm interpretability to get a nuanced response. I can give two informal responses. The first is that one assumption of our proposal is that there is something wrong with how race and racial categories are intuitive understood. Normal people’s understanding of race is, of course, ridden with stereotypes, implicit biases, false causal models, and so on. If we proposed an algorithm that was fully “interpretable” according to most people’s understanding of what race is, that algorithm would likely have racist or racially unequal outcomes. That’s precisely the problem that we are trying to get at with our work. In other words, when categories are inherently unfair, interpretability and fairness may be at odds.

The second response is that educating people about how the procedure works and why its motivated is part of what makes its outcomes interpretable. Teaching people about the history of racial categories, and how those categories are both the cause and effect of segregation in space and society, makes the algorithm interpretable. Teaching people about Principal Component Analysis, the algorithm we employ, is part of what makes the system interpretable. We are trying to drop knowledge; I don’t think we are offering any shortcuts.

Principal Component Analysis (PCA) may not be the right technique

An objection from the computer science end of the spectrum was that our proposed use of Principal Component Analysis (PCA) was not well-motivated enough. PCA is just one of many dimensionality reduction techniques–why did we choose it in particular? PCA has many assumptions about the input embedded within it, including the component vectors of interest are linear combinations of the inputs. What if the best QR representation is a non-linear combination of the input variables? And our use of unsupervised learning, as a general criticism, is perhaps lazy, since in order to validate its usefulness we will need to test it with labeled data anyway. We might be better off with a more carefully calibrated and better motivated alternative technique.

These are all fair criticisms. I am personally not satisfied with the technical component of the paper and presentation. I know the rigor of the analysis is not of the standard that would impress a machine learning scholar and can take full responsibility for that. I hope to do better in a future iteration of the work, and welcome any advice on how to do that from colleagues. I’d also be interested to see how more technically skilled computer scientists and formal modelers address the problem of unfair racial categories that we raised in the paper.

I see our main contribution as the raising of this problem of unfair categories, not our particular technical solution to it. As a potential solution, I hope that it’s better than nothing, a step in the right direction, and provocative. I subscribe to the belief that science is an iterative process and look forward to the next cycle of work.

Please feel free to reach out if you have a critique of our work that we’ve missed. We do appreciate all the feedback!