Study Guide
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Applied Examples

Listed below are some practical examples of using data to "study up".



Speculative Examples

Sometimes when we flip the script, it’s not to implement an applied solution to a problem, but to reveal the harmful or insistent or stigmatizing logics embedded in the technology.
Data work is profoundly shaped by a politics of access. When we “study up,” the conversation regarding our ethical engagement as data scientists and designers must expand to account for the specific challenges faced when holding the lens up to powerful people and institutions. Below are some resources and tactics that we found helpful in navigating the political economy of data science.

Filling the Gaps

Sometimes we have to build infrastructure to fill the gaps where data has not been collected.


Interpreting and Labeling Data

How might existing data be re-interpreted and re-purposed to support accountability for those with the most power and agency in a given system?


Refusal as a First Step

How might we engage in the art of refusal to fundamentally re-imagine the terms of our engagement with the gatekeepers of data?


Challenges to Studying Up: Lessons from Anthropology

We can learn a lot from anthropologists’ prior attempts to study up.


Beyond Studying Up: Other Useful Concepts

The role of data science in reproducing oppression
The call to “study up” is based on the observation that data scientists often reproduce social inequities by focusing the algorithmic gaze exclusively on the poor and marginalized. We are doomed to reinforce the status quo when our “solutions” to social problems turn a blind eye to the powerful.

For more on the role of technology in reproducing and naturalizing social inequities, check out these key concepts:

Liberatory Frameworks for Socio-technical Design

"Studying up” is just one of many useful frameworks for grappling with power, the algorithmic gaze and struggles for justice. Here are some other resources we've found useful for developing critical and generative approaches to data science."

Challenges and Pitfalls of Reforms
This document was compiled by Mariame Kaba and provides a set of questions we can use to reflect on whether a proposed reform or intervention reproduces harmful systems of oppression or makes way for liberatory possibilities to emerge.

Abolitionist Tools for the New Jim Code
“An abolitionist toolkit... is concerned not only with emerging technologies but also with the everyday production, deployment, and interpretation of data. Such toolkits can be focused on computational interventions, but they do not have to be. In fact, narrative tools are essential.”
  • Benjamin, Ruha. Race after technology: Abolitionist tools for the new jim code. John Wiley & Sons, 2019.

  • Postcolonial Computing
    “Postcolonial Computing is a bag of tools that affords us contingent tactics for continual, careful, collective, and always partial reinscriptions of a cultural–technical situation in which we all find ourselves.”
  • Philip, Kavita, Lilly Irani, and Paul Dourish. "Postcolonial computing: A tactical survey." Science, Technology, & Human Values 37.1 (2012): 3-29.