Review of More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech by Meredith Broussard

Often people ask me what I would recommend if I am no longer recommending Invisible Women. Usually my response is the unhelpful, “Dunno, figure it out.” But really, the amount of books I read? There must be more books about technology and bias out there, especially in the four years since that one was published. So when I heard about More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech, I was excited to receive an eARC from NetGalley and publisher MIT Press.

Meredith Broussard brings her decades of experience as a data scientist and a Black woman in America to discuss design and data bias in tech, not only along the axis of gender but also race and (dis)ability. As the title implies, the book’s thesis is that the bias we can detect and quantify in tech (and in the social systems, such as companies, that build and maintain our tech) is not present by accident. It’s not just a “glitch” or a bug that we can squash with some crunch and a new release. It’s baked into the system, and solving the problem of bias will require a new approach. Fortunately, in addition to pointing out the problems, Broussard points to the people (herself included) doing the work to build this new approach.

When it comes to the problems outlined in this book, a lot of this was already familiar territory to me from watching Coded Bias, reading Algorithms of Oppression and Weapons of Math Destruction, etc. Broussard cites many high-profile examples, and often her explanations of how these systems work start from a basic, first-principles approach. As a result, techies might feel like this book is a little slow. Yet this is exactly the pace needed to make these issues accessible to laypeople, which Broussard is doing here. With systems like OpenAI’s ChatGPT making a spectacle, it is imperative that we arm non-tech-savvy individuals with additional literacy, inoculating them against the mistaken argument that technology is or can ever be value-neutral. Broussard’s writing is clear, cogent, and careful. You don’t need any background in computing to understand the issues as she explains them here.

What was new to me in this book were the parts where Broussard goes beyond the problems to look instead at the solutions. In addition to her own work, she cites many names with which I’m familiar—Safiya Umoja Noble, Timnit Gebru, Cathy O’Neil—along with a few others whose work I have yet to read, such as Ruha Benjamin. In particular, Broussard enthusiastically endorses the practice of algorithmic auditing. This procedure essentially upends the assumption that machine learning algorithms must be black boxes whose decision-making processes we can never truly understand. Broussard, O’Neil, and others are working to create both manual and automated auditing procedures that companies and organizations can use to detect bias in algorithms. While this isn’t a panacea in and of itself, it is an important step forward into this new frontier of data science.

I say this because it’s important for us to accept that we can’t put the genie back in the bottle. We are living in an algorithmic age. But much as with the fight against climate change, we cannot allow acceptance of reality to turn into doom and naysaying against any action. Broussard points out that we can still say no to certain deployments of technology that can be harmful. Facial recognition software is a great example of this, with many municipalities outlawing real-time facial recognition in city surveillance. There are actions we can take.

The overarching solution is thus one of thoughtfulness and harm reduction. Broussard directly challenges the Zuckerberg adage to “move fast and break things.” I suppose this means a good clickbait title for this review might be “Capitalists hate her”! But it’s true. The choice here isn’t between algorithms or no algorithms, AI or no AI. It’s between moving fast for the sake of convenience and profit or moving more slowly and thoughtfully for the sake of being more inclusive, equitable, and just.

I like to think of myself as “tech adjacent.” I don’t work in tech, but I code on an amateur level and keep my pulse on the tech sector. I think there is a tendency among people like me—tech-adjacent people invested in social justice—to write off the tech sector as a bunch of white dudebros who are out of touch. We see the Musks and Zuckerbergs at the top, and we see the Damores in the bottom and middle ranks ranting about women, and we roll our eyes and stereotype. When we do this, however, we forget that there are so many brilliant people like Broussard, Benjamin, Noble, O’Neil, Gebru, Buolamwini, and more—people of colour, women, people of marginalized genders, disabled people, etc., who care about and are part of the tech ecosystem and are actively working to make it better. They are out there, and they have solutions. We just need to listen.