Learning Objective

We will cover approaches for defining fairness within a specific project context, and how equity impacts the creation and use of data through collection and categorization.

Pre-work

Prior to this session, read and watch the following items 75 minutes total

One basic approach to data equity can be found in the intersectional concepts of data feminism, as laid out by Catherine D’Ignazio and Lauren Klein in *Data Feminism* (2020), an introductory text that is available on an open-source basis which we encourage you to check out if it’s of interest to you). In week one we share a bit of the book’s introduction that frames their approach to data.

• [ ] READ: these excerpts from "Introduction: Why Data Science Needs Feminism" 2 minutes

  ❝Data is a double-edged sword. In a very real sense, data have been used as a weapon by those in power to consolidate their control — over places and things, as well as people. Indeed, a central goal of this book is to show how governments and corporations have long employed data and statistics as management techniques to preserve an unequal status quo.

  Data are part of the problem, to be sure. But they are also part of the solution. Another central goal of this book is to show how the power of data can be wielded back.

❝To guide us in this work, we have developed seven core principles. Individually and together, these principles emerge from the foundation of intersectional feminist thought. Each of the following chapters is structured around a single principle. The seven principles of data feminism are as follows:

1. Examine power. Data feminism begins by analyzing how power operates in the world.
2. Challenge power. Data feminism commits to challenging unequal power structures and working toward justice.
3. Elevate emotion and embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world.
4. Rethink binaries and hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.
5. Embrace pluralism. Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing.
6. Consider context. Data feminism asserts that data are not neutral or objective. They are products of unequal social relations, and this context is essential for conducting accurate, ethical analysis.
7. Make labor visible. The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued.❞

Next we will look at principle 4 – Rethink binaries and hierarchies – on counting and classification in the creation and use of data. This chapter also engages with metrics, which are essential to the design and implementation of data projects, and designing metrics to address systemic problems, such as racism and classism, within the domain of your data project.

• [ ] READ: What Gets Counted Counts 40 min

In the appendix of the book, the authors “walk the walk” by demonstrating how they created metrics to address systemic problems within the realm of their data project – writing a book.

• [ ] READ: Our Values and Our Metrics for Holding Ourselves Accountable 13 min

<aside> 💡 Key Point: In writing the book Data Feminism, the authors D'Ignazio and Klein set up metrics to address 8 structural problems in writing the book.

To address the structural problem of colonialism, for example, within their project domain, they defined two aspirational metrics and compared final metrics. Their reflection on the final metrics is instructive so be sure to read that.

• Aspirational: 30% of projects discussed in the book come from the Global South Final: 7% from the Global South
• Aspirational: Example or theorist in every chapter about Indigenous knowledges and/or activism Final: Seven of nine chapters feature an indigenous example and/or theorist

This kind of out-of-the-box thinking can be applied to your project, too! How might you measure progress on these structural problems within the realm of your own work? Remember, an effective metric is something that can be measured! (We’ll talk about this more in the session, hint hint!)

</aside>

Beyond metrics, there are numerous decisions that determine how a data project is conceived, framed, scoped, executed, implemented, and shared. This next reading introduces the concept of Design Justice, by Sasha Costanza-Chock, which gives us a mental model to think about each design choice made in a data project, and how to look at those choices (either ahead of time or retrospectively) through an equity lens.

• [ ] READ: To Truly Be Just, ‘Design Challenges’ Need to Listen to the Communities they Claim to Serve Excerpt from Design Justice **on how assumptions are built into any kind of data-oriented project. 10 min

https://www.youtube.com/watch?v=OVRmiAuG-jM&t=59s

And to help tie this all together, this video gives an overview of data equity concepts and some examples of what it looks like in practice.

• [ ] WATCH: Elevating Data Equity in Practice – 1:00 to 14:00 A panel recording from Data Con LA 2021 with some awesome folks who participated in previous cohorts of this workshop! 13 min

That’s it for this week’s pre-work!

Below you’ll find the materials we’ll use during our first live session.

Discussion Questions