During my Ph.D., I spent a lot of time reading about how technology has reinforced and amplified social injustice. Technology is often sold to us as neutral and unbiased, but in reality, because humans design technology, it has its human creators’ biases. My favorite books on this topic were the ones that pointed to a path forward, offering constructive advice in addition to critiquing current practices.
Here are six recommendations for books on data, technology, and social (in)justice that have influenced my research over the last four years:
More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech by Meredith Broussard
This book is my number one recommendation to anyone who feels like they don’t understand how Artificial Intelligence (AI) or Machine Learning (ML) technologies work. It’s short (less than 200 pages) and approachable, opening with a summary of how the algorithms behind AI and ML work at the start of the book. Then, she provides examples from her and others’ reporting as a data journalist that shows how these technologies are not neutral. In fact, they often perpetuate or amplify existing social biases (structural injustice). The idea of the “glitch” that Broussard critiques with this book is that the racist, sexist, abilist, and otherwise biased behaviors of AI and ML technologies are not one-off errors that have a quick fix. The biased behaviors are actually a result of biases encoded into the data that powers these technologies, and engineered into the design of the algorithms that interpret that data. (This idea is something Ruha Benjamin, among other authors, has written about as well; see Race After Technology below.)
What I most appreciate about this book is that Broussard is not all doom and gloom; More Than a Glitch points the reader to a path forward. Rejecting “technochauvinism”, the idea that automating everything with technology is always the best solution, Broussard encourages us to take a different approach to creating AI and other data-driven technologies that incorporates more people into the creation process.
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks
My next recommendation focuses on a different area of social injustice: bias against people living in poverty. Warning: this isn’t a light book. The stories Eubanks reports are horrifying and heart-wrenching. She opens the book with a personal experience and then focuses the next few chapters on examples from different parts of the Unites States of how data-driven technologies trap people in poverty. Still, as Broussard does, Eubanks ends the book by pointing to a path forward that would lead to greater social justice. The key for Eubanks is to re-incorporate people into decision making processes. Towards the end of the book (page 168), the authors writes,
“I find the philosophy that sees human beings as unknowable black boxes and machines as transparent deeply troubling. It seems to me a worldview that surrenders any attempt at empathy and forecloses the possibility of ethical development. The presumption that human decision-making is opaque and inaccessible is an admission that we have abandoned the social commitment to try to understand each other.”
I couldn’t agree more.
For more books along these lines (books that explain why we need different approaches to AI, ML, and big data), I recommend:
Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Noble
Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin
The Dawn of Everything: A New History of Humanity by David Graeber and David Wengrow
Though not explicitly about data or technology, I’ve included The Dawn of Everything in this list because it has implications for data-driven technologies. The authors, an archeologist and an anthropologist, revisit dominant and marginalized historical narratives in the Western world. They point out underlying assumptions that have shaped the way we talk about the past, asking why we put emphasis on certain periods of time rather than others. For me, having focused my Ph.D. research on gender bias over the last few years, I was especially interested in the parts of the book that discussed the ways historians have written out the contributions of women from historical narratives, and of records of societies that were matriarchal and heterarchical (neither patriarchal nor matriarchal).
This book is a looooong read but I found myself fascinated at every page. The person who recommended this book to me said it upended everything they’d been taught about history, and once I began reading it, I felt the same way! It expanded my imagination about the type of societies that are possible for us to organize into and left me feeling optimistic about attaining greater social justice in the future.
Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Perez
In this book the author summarizes researchers’ work in other fields to demonstrate how prevalent the man-as-default assumption is. She references work in archeology, linguistics, anthropology, literature, movies, phone manufacturers, social media, human-computer interaction, science, medicine, news, education, video games, music, banking, history, and politics that are biased in favor of men. Importantly, the author states that she’s not trying to claim that the gender biased work she writes about are examples of work where people have “deliberately set out to exclude women. It just means that what may seem objective can actually be highly male-biased” (page 17). In my opinion this is an especially valuable book for women because it reminds us not to be overly-trusting of supposedly well-researched products and services. Although women’s rights have certainly improved on the whole, women still are often excluded or inadequately represented. It reminded me to trust my own body and my own experiences, and to study statistics and reports critically rather than accepting them at face value.
Data Feminism by Catherine D’Ignazio and Lauren F. Klein
If you work with data, or are interested in working with data, this books is a great place to start. D’Ignazio and Klein propose principles for a new approach to data science that draws on feminist theories. Feminist theories view knowledge as situated and multiplicitous, saying that there is no single universal or objective point of view. We each have our own perspective on the world that informs how we gather and interpret data and information. Consequently, data are always partial representations of the world that reflect a particular perspective. All datasets will always be incomplete and biased, so we need to work with data as things that are situtated in a particular time and place. When describing examples of projects that embody the principles of data feminism, the authors look to grassroots and communtity projects in addition to industry and academia, making an effort to draw on a range of perspectives that differ from their own throughout the book. Though Data Feminism touches on the dangers of many existing approaches to data science and data-driven technologies, its focus is on providing advice for how to undertake data-oriented work in a more critical, inclusive manner.
The Candy House by Jennifer Egan
This is a fictional story that exists in a similar-but-different world to our own, set slighty in the future and with a twist on the invention of social media. The challenges of misinformation, deep fakes, the ethics of sharing data, and trust in technology are all present in this novel, but without political agenda. The author doesn’t try to categorize certain technologies or uses of technology as simply good or bad (this isn’t a dystopian novel). The narrative Egan writes around social media in the fictional future of The Candy House is more nuanced. As the reader, you’re brought into this fictional future and are left to decide for yourself how you feel about memories becoming data, among other things.
Note: you don’t need to read A Visit from the Goon Squad first, even though some of this book’s characters appeared in that book first! When I saw Jennifer Egan speak at the International Book Festival in Edinburgh a couple years ago, she even said she’d probably recommend people read The Candy House first, now.
I’ll end with two books that I haven’t read yet but are high on my reading list:
I’ve read the introduction to this, along with some of Mar Hicks other writing, but I’m eager to read the entire story. Hicks suggests that there’s a lot Silicon Valley could learn from Britain when it comes to sexism in the technology industry.
Imagination by Ruha Benjamin (to be published in 2024)
Recently, I’ve been thinking about the importance of speculative design and science-fiction for imagining potential futures we as a global society can work towards. Black Mirror freaked me out too much for me to watch every episode, but I did appreciate the overall message of caution when it comes to rapidly creating and deploying new technologies. This forthcoming book by Ruha Benjamin seems to fall into that category of speculative, future-thinking work, but with a more positive vision.