Communicating Tech for Movement Work: An Intern Retrospective

Hello! I’m Adelaide, a student at the University of Pennsylvania and an intern on the Affiliate & Advocacy Pod of ACLU Analytics! I started as a part-time intern in January and spent the spring working on a report for the ACLU of Rhode Island about disparate outcomes in the state’s traffic stops. After learning so much through that process, I returned as a full-time summer intern, working on projects relating to education equity, prison deaths, electoral organizing, and more! Collaborating with Alex Yurcaba (my wonderful supervisor!), Ranya Ahmed, and Eric Lee on the Affiliate & Advocacy Pod has been one of the most fulfilling experiences I’ve had both professionally and personally. I am so grateful to them! As my internship wraps up, I’ve had time to reflect on what I expected this internship to be, what I’ve learned since, and how it will inform my work moving forward.

Before arriving at the ACLU, my conception of the organization was formed primarily through my interactions with ACLU affiliates in my social justice work. My first context was when I was working as an intake intern at the Immigrant Legal Advocacy Project (ILAP) in Portland, ME in the summer of 2019, just after I graduated high school. At the time, we were welcoming a group of asylum seekers who had just arrived in Portland and making sure each individual and family had legal representation. Lawyers and advocates were increasingly concerned about the low grant rate for asylum seekers in the Boston Asylum Office (which processes all asylum applications from Maine). The ACLU of Maine collaborated with ILAP and other immigrant rights organizations to file a FOIA lawsuit requesting more information on how the Boston Asylum Office was processing cases. Later, they worked together to publish this report, outlining disparate treatment in the Boston Asylum Office. Although I didn’t directly work with the ACLU that summer, I saw its role in holding oppressive systems accountable. I saw that collaboration — at the community and national level — gives power and visibility to advocates.

This glimpse into the ACLU’s capacity to support community-led movements made me believe in its utility and importance. When I started my internship with the analytics team, I was most interested in the relationship between the ACLU’s national office, its affiliates, and community-driven movement work. The key seemed to be communication, and I wanted to learn how to effectively translate the technical work I was doing to the people affected by it. Here are the three lessons I’ve learned:

1. Document, document, and document more
2. Visualize the process, not just the output
3. Transparency builds trust

Lesson #1: Document, document, and document more

At the end of my second week on the Affiliate & Advocacy Pod, I sat down with Alex for my first code review. A code review is when someone on the analytics team assesses your code for bugs, methodological errors, or places where your code could be more efficient. In practice, this meant screen sharing my R script on Zoom as I walked through my steps. I had been working to clean police data for an affiliate project and was stuck. Alex helped me to get unblocked, showed me new functions that made my code more efficient, and also gave me feedback that I should add more documentation.

Coming from the academic context where I learned to code, I thought my comments in my R code were thorough. I briefly outlined what each section of code was doing and defined my variables. However, documentation at the ACLU has very different implications and standards. In my project for the affiliate, my work had the potential to change policy. We needed our analysis to be disciplined and rigorous so that it could stand up to a much higher level of scrutiny than exists in a classroom environment. Specifically, my code needed to be reproducible. Someone else on the team, or more importantly — someone from the communities affected by this policy — should have been able to recreate my analysis. Documenting technical work seems obvious, but as I learned early in my time here, it is a learned skill and central to values of transparency and inclusion in movement work.

If I was to explain how I created the analysis and what it meant, I needed to clearly outline every step in the process. This includes but is not limited to the ask from the stakeholder (in this case the affiliate), the source of the data, any assumption made about what variables mean (remember: this is real life and the data is messy and often without a codebook!), the communication you have internally about the project, and statistical methodology. This should be a living representation of your work for you and your team to reference.

Lesson #2: Visualize the process not just the outcome

One of the key tools to communicate technical ideas to non-technical audiences is data visualization. This can include charts, diagrams, maps, and much more. Before coming to the ACLU, I was most familiar with visualizations that represented data that had already been cleaned and analyzed. An example of this sort of visualization is in this report from the ACLU of Idaho that I helped with this summer.

This chart from page 25 of the report represents racial disparities between white and Latine students in in-school suspension rates in Caldwell School District. Its goal is to represent already cleaned data in a way that makes sense to a non-technical reader. This is important because it makes discrimination visible to a broader audience, hopefully prompting advocacy and change.

However, it is also important to effectively communicate how we got to that outcome — in part so that those affected by these choices get input. In a recent meeting, I was tasked with describing how the stakeholder could collect data over an extended time period to assist their advocacy efforts. This was a question of process, rather than presenting ready results. I decided to show them exactly how the data collection process would look on their end:

In this example, the stakeholder would be responsible for filling in Columns 4 and 5 of the data, while ACLU analytics would supply Columns 1–3. It was important that the stakeholder understand what their role was in the data collection process, and be given the chance to decide if the process aligned with their needs. At this point in the project, there was no outcome to show. In movement work, that’s often the case — especially in the case of missing or poor-quality data. However, as we manipulate data, it is incumbent on us to continually make these decisions visible to those affected by them.

Lesson #3: Transparency builds trust

In order for data analysis to be useful for movement work, it has to be trusted. However, in my experience as someone coming from community-level social justice work, it can be really challenging to trust collaborators when they have much less proximity to the issues you are working on. Transparent (and frequent!) communication is an important tool for building and maintaining trust, and is central to the utility of technical work for advocacy.

This is a skill that Ranya, the Affiliate & Advocacy Pod lead, modeled to me repeatedly during my internship. After I led one of my first meetings, she reminded me to make the stakeholders’ options clear and consistently set realistic expectations. Moreover, when the stakeholder comes with ideas, she said to listen, ask clarifying questions, and only after you listen to everyone, do you offer your ideas. This allows us to comprehensively understand their ask, their thought process & their goals, and it ensures that the stakeholder feels heard. This level of respect and transparency helps us share options and solutions, and fosters trust. That foundation of trust is crucial to the success of any project with our stakeholders.

Final Thoughts
The ability of technical work to make a positive impact on movement work is dependent on trust-building and communication. Our power as data analysts is in our capacity for translation — to break down gatekeeping and make data visible to everyone. In “How Policy Hidden in an Algorithm is Threatening Families in This Pennsylvania County”, writers from the ACLU Analytics and Legal teams describe the AFST, a screening tool used by the child welfare agency in Alleghany County, Pennsylvania. The tool uses an algorithm that could result in disparities between Black and non-Black families as well as between households that include a disabled person and those that do not. The article states, “the AFST creators are doing more than math when building a tool. They also have the ability to become shadow policymakers — because unless the practical impact of their design decisions is evaluated and made public, this power can be wielded with little transparency or accountability.”

The example of the AFST represents the discrimination veiled by technical processes and the power of making these processes visible through effective communication. I am so grateful to have had the opportunity to learn from this team, and I am sure I will learn countless additional lessons about translating tech in movement work!