Earlier this summer, a group of us started meeting weekly to discuss Jer Thorp’s new book, Living In Data. The book is built on Jer’s experiences throughout his career as an acclaimed designer and data scientist—his work has included residencies with The New York Times, National Geographic, and the Library of Congress.
But while the stories of Jer’s various projects and expeditions made for an entertaining page-turner of a book, we spent the majority of our discussions dwelling on the bigger questions Jer confronts within his work. What is our relationship with data, what should it be, and how can we work toward a practice that empowers rather than exploits data’s subjects?
You won’t find any concrete solutions in Living In Data (or anywhere else), but we did find dozens of questions and reflections that nudged us toward a more holistic and humane perspective. In this blog, we collected some of the more surprising, challenging, and inspiring insights from our group.
Embracing the tension
As someone who enjoys working with data, the more complex the better, I’ve also tried to understand the ethical issues that frequently arise. Questions around who owns the data, whether the subjects of the data consented to its collection, whether they were aware of who would become the audience for that data—it’s become more and more crucial to ask these questions and more at every step. Living in Data doesn’t necessarily provide easy answers; in fact, I think I’ve come away with even more questions to ask than before. But it encourages me—and anyone who works with data—to embrace the tension.
Good data analysis requires the analyst to be able to generalize and recognize patterns across the whole data set… but also be able to identify interesting outliers and dig down into why a single point is located where it is. One of the ideas I took away from the book is how balancing those two viewpoints on data reflects this ethical tension. The power of data lies in its multiplicity, the aggregate, which can really be empowering for illuminating system behavior that is hard to understand from anecdotes alone. But we can’t forget that the data are collected from individuals with distinct contexts, and that we risk erasing nuance when we seek those larger patterns.
— Hana Lee
Warnings, and reasons for hope
In a time when institutions are warming up to the repatriation of artifacts once stolen from their original lands, there too exists the dangers of not properly attributing, involving, and collaborating with those from whom data is collected, so that we don’t repeat history. Collected datasets, particularly public ones, are ripe with potential to be exploited for use by the “white knight,” building yet another product that uses solutions from their culture, to solve problems of others who may not have the same values, cultural practices, or worse, the desire for such a problem to be “fixed” at all. From a design perspective, we know this approach is common and is as dangerous as skipping a process of user research and discovery. We see this show up to the world in a white, modern “refresh” of mahjong; or this story of a high school told that they were the “saddest spot in the US”, which had already made the rounds before they discovered the data was incorrect.
The book came to highlight a variety of warnings, but also some hopeful initiatives. One is a data sovereignty law by the Maori people, stating that data is a Taonga—or a tangible or intangible item or matter of special cultural significance—and therefore the data collected about the Maori belongs exclusively to them. I think the details in their approach especially hold a lot of strong potency for pushing back against the current colonialist approach to data.
Another hopeful project is the work by the organization Digital Democracy. The Earth Defenders Toolkit presents a tool that puts data collection and agency in the hands of those looking to improve local ecologies. This ultimately allows communities to own and manage their own data, and is a super interesting project that I know I will follow for years to come.
One last note about the group: We had such great conversations that really connected to our own personal experiences, sharing our childhoods to travel adventures. The tangents we went on when discussing this book were ultimately the way we reframed and materialized the concepts from the book to something relatable, and I just want to say I really appreciate everyone who showed up!
After reading Living in Data, I realize just how messy data is and how there’s no clear solution on how to handle data perfectly. Jer presents contrasting examples of problematic data collection alongside somewhat idealistic methods of data usage, while reiterating that there will always be issues. There is no panacea for the problems that data and its sharing and usage present, but I think all we can do is be mindful of what data we have, what we do with it, and who benefits from it. Knowing that data is not something that’s just floating around waiting to be collected—and that even when you do collect it, it doesn’t necessarily belong to you—is one of the most important messages that Jer presents. Living in Data is more of a series of philosophical musings and reflections, rather than a prescriptive lesson on how to deal with the issues surrounding data collection.
From extraction to inclusion
Whenever we’re dealing with a buzzy topic like data, there’s a natural tendency to fixate on its most exotic elements—how can Big Data cure cancer, reduce poverty, and transform society? And while Living In Data is packed with page-turning anecdotes and reflections on these topics and more, the lasting impression I’ve taken away is the importance of investing in the mundane.
The more experts push ahead with new advances in data engineering, the more work we have to do to improve not just access, but the technical literacy and infrastructure required to let everyone participate in this new ecosystem. The book really highlighted the need for advocates to “close the loop,” as Jer says, and transition our approach from processes of data extraction to ones of outreach and inclusion. It could only help everyone.