This is a really interesting dive into how technology does not “level the playing field” as many think. The author gives examples of algorithms discriminating in various settings like exam grades, loans, and medical diagnoses. At this point, we all know some social media has gotten in trouble for how their algorithms discriminate. This book breaks down how computers get it wrong and how people can combat the errors we build into our tech.

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.

Broussard’s very smart and accessible book has given me new ways of thinking about technology, and new frames for understanding and expressing those thoughts. I was particularly interested in the chapters on disability and on medicine and AI, which are two areas that intersect with my professional training. Broussard covers a lot of ground in other areas too, including racism and other kinds of discrimination in tech, mathematical fairness vs. social fairness, technochauvinism (that computers will fix everything and create utopia), machine bias, AI ethics, cognition, statistics, the justice system, tech regulation, tech and algorithmic auditing, algorithmic justice and accountability, facial recognition and its application and misuse, and a great deal more. She outlines why she believes we should not rely so much on technology, and gives many excellent examples to show where, how and why tech has failed. In that same excellent chapter on medicine and AI, she shares how she used her own medical records to test diagnosis by AI, with intriguing results which have implications for us all.

I learnt a great deal from this book, and I’m so glad I decided to read it, in spite of initially being slightly intimidated by the subject. There are things that will probably be mostly of interest to those working in the fields Broussard covers; however, as tech, algorithms and AI are now part of our daily lives and will only become more so in the future, you will find this a very relevant and timely book.

Meredith Broussard is a data journalist and academic whose work focuses on AI investigative reporting and ethical AI. She is an associate professor at the Arthur L. Carter Journalism Institute of New York University and is research director at the NYU Alliance for Public Interest Technology.

Thank you to MIT Press and to NetGalley for this DRC.

In More than a Glitch, Broussard, who is a data journalist and data scientist, discusses the types of biases that are embedded in technology and various social implications that arise as a result. Broussard uses an interesting analogy to argue that social fairness and mathematical fairness are not the same, which then feeds into Broussard’s concept of technochauvinism.

She explores the history of how biases are embedded in technology, and goes on to analyse examples of how this has had a negative impact and consequences in areas including education, health, law enforcement, and banking.

After watching a memorable Ted Talk by Zeynep Tufeki a few years ago, I knew that I had to read More than a Glitch. While Broussard mentions how technology can be used to our advantage to aid with accessibility, she argues that cutting-edge technology is not the answer. I could really appreciate how Broussard draws on examples of how technology discriminates in various situations that people may not be aware of unless they have experienced it firsthand. I felt that the points that were made by Broussard are timely, and significant to influencing change.

Overall, Broussard discusses most of the concepts in More than a Glitch in simple language that was easy to understand. The only drawback for me was that some of the figures were difficult to understand. Aside from that, I would highly reccomend reading More than a Glitch, and I hope that many people do read it.

I absolutely adored this book. It's an excellent primer on a wide variety of issues facing minority populations in the realm of technology, especially data science. I value that Broussard takes time to discuss intersectionality. She also practices what she preaches, by reaching out to members of each community to understand their perspectives and obtain qualitative data to use in concert with her evaluation of quantitative data. I plan to add this book to my collection as soon as possible and reread it with a fine tooth comb so that I can take advantage of all the references to other thinkers in the field.

The reading of this is *very* hindered by issues with the digital copy provided, ironically. But I was able to skim through and assess what I feel the author's key points and takeaways are. I feel Broussard's book (in better format) would best be suited to a college environment, as this information will likely either be 'known' already or a facet of denial for those that are already in their practice of the field.

Thank you to NetGalley and the publisher for the advanced digital copy.

DNF at 14%, seems interesting but the "FOR PROOFREADING, INDEXING, AND PROMOTIONAL PURPOSES" every couple of paragraphs made for a very annoying reading experience, I'm guessing that with some settings it's supposed to appear at the bottom of the page or something but for me it was in the middle of the page most of the time making it impossible to ignore so it kept "interupting" my reading.