Study Guide
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Applied Examples
Listed below are some practical examples of using data to "study up".
- Criminal Court Data Dashboard: Takes millions of court dispositions from some of the largest counties in Texas, and makes them easy to access and understand. Developed by the Texas Criminal Justice Coalition.
- Anti-Eviction Mapping Project: A resource and crowdsourcing tool to fight landlord technologies in our homes and neighborhoods.
- Turkopticon: Helps the people in the 'crowd' of crowdsourcing watch out for each other—because nobody else seems to be.
- Court Watch (MA, NYC): Shifts the power dynamics in our courts by opening the courtroom to public scrutiny.
- Advisory Circular: A network of twitter bots that detect and tweet in real time whenever they detect police and military aircraft flying in circles.
- Mapping Police Violence
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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.
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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.
- Mimi Onuoha, On Missing Datasets
- Currie, Morgan, et al. "The conundrum of police officer-involved homicides: Counter-data in Los Angeles County." Big Data & Society 3.2 (2016): 2053951716663566.
- Irani, Lilly, and M. Six Silberman. "From critical design to critical infrastructure: lessons from turkopticon."Interactions 21.4 (2014): 32-35.
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?
- Dourish, Paul, and Edgar Gómez Cruz. "Datafication and data fiction: Narrating data and narrating with data." Big Data & Society 5.2 (2018): 2053951718784083.
- Benjamin, Ruha. "Assessing risk, automating racism." Science 366.6464 (2019): 421-422.
“It is vital to develop tools that move from assessing individual risk to evaluating the production of risk by institutions so that, ultimately, the public can hold them accountable for harmful outcomes.”
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?
- Chelsea Barabas. 2020. "To Build a Better Future, Designers Need to Start Saying 'No'"
- Feminist Data Manifest-no
- Graeff, E. 2020. "The Responsibility to Not Design and the Need for Citizen Professionalism." Computing Professionals for Social Responsibility: The Past, Present and Future Values of Participatory Design.
- Tuck, Eve, and K. Wayne Yang. "Unbecoming claims: Pedagogies of refusal in qualitative research." Qualitative Inquiry 20.6 (2014): 811-818.
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Challenges to Studying Up: Lessons from Anthropology
We can learn a lot from anthropologists’ prior attempts to study up.
.- Hugh Gusterson. 1997. Studying up revisited.PoLAR: Political and Legal Anthropology Review 20, 1 (1997), 114–119.
- Daniel Souleles. 2018. How to Study People Who Do Not Want to be Studied: Practical Reflections on Studying Up." Computing Professionals for Social Responsibility: The Past, Present and Future Values of Participatory Design. PoLAR: Political and Legal Anthropology Review 41, S1 (2018), 51–68.
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:
.For more on the role of technology in reproducing and naturalizing social inequities, check out these key concepts:
- Benjamin’s (2019)“New Jim Code”
- Browne's (2015) “racializing surveillance” and (2010) "digital epidermalization" .
- Broussard’s (2018) “technochauvinism”
- Buolamwini’s (2016) "coded gaze"
- Eubanks’ (2018) "digital poorhouse"
- Noble’s (2018) "technological redlining"
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.”
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.”