Best Data Resources of 2020

As the year winds down, we’re putting together a list of our favorite data-related resources and books from 2020 that help readers reflect and think critically about how they work with and present data. Take a look and let us know some of your favorites!

• • Data Feminism by Catherine D’Ignazio and Lauren F. Klein (March 2020)
    • In this book, Catherine D’Ignazio and Lauren F. Klein use feminist thought to frame data science and big data, pointing out the ways in which big data is overwhelmingly–and problematically– white, male, and capitalistic. One of the things I love about this book is the section “Our Values and Our Metrics for Holding Ourselves Accountable.” In this section, the authors share their values and metrics for how their book is addressing the structural problems they have critiqued. This is such a wonderful example of how theory meets practice, how academia meets activism/community needs. For example, in addressing the structural problem of cissexism, they list a few aspirational metrics to meet; one of these metrics is “example or theorist in every chapter from a transgender perspective.” In their final metric of this aspirational value, they list that nine of nine chapters feature a transgender example and/or theorist. 
  • Coded Bias by Shalini Kantayya (November 2020)
    • In this documentary, Shalini Kantayya explores how machine-learning algorithms reproduce gender-, class-, and race-based inequities. Kantayya makes the case that machine-learning algorithms, the pervasiveness and influence of it in our everyday lives, has a human cost and is therefore one of the most serious modern-day threats to civil liberties.
  • Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin 
    • In this book, Benjamin defines the New Jim Code as “the employment of new technologies that reflect and reproduce existing inequities but that are promoted and perceived as more objective or progressive than the discriminatory systems of a new era.” Drawing on examples of racist algorithms, robots, and AI trained on data gathered through institutionalized racism, Benjamin deconstructs and examines different forms of discriminatory design and it ends with a survey of approaches, projects, and tools that are attempting to hold systems and technologies accountable. 
  • A Comprehensive Guide to Accessible Data Visualization by Amanda Miller (November 2020)
    • This resource gives in depth information on how to make sure that your data visualizations are accessible for all, including the visually impaired. It breaks down examples of accessible graphs and offers tips on visual best practices such as following color contrast guidelines, labeling data points directly, separating elements with whitespace, and avoiding complex tooling and hover overlays.

• • Excavating AI: The Politics of Images in Machine Learning Training Sets By Kate Crawford and Trevor Paglen
    • Excavating AI dives into how training sets inform AI and perpetuate social issues through the algorithms we build through the data we feed them. The article explores this in depth through the case study of ImageNet, one of the most commonly used training sets.