Data justice is a concept that has emerged from the increasing amount of data each of us unavoidably generate as we move through the world, and the realization that these data may reinforce or exacerbate existing advantages and disadvantages as they are used to make decisions about us without our input or even knowledge. Data justice requires reflection on how people are made visible (or invisible), represented, and treated as a result of their digital data (Taylor, 2017).
Much of the conversation around data justice has centered on personal data and “big data.” This may be data required to receive services (see the Our Data Bodies project’s “What’s in your wallet?”); or personal information contributed to social media and other sites in exchange for services (often without full knowledge of how long data will be stored or with whom it may be shared). It may also be systems in which participation is not voluntary and which make decisions that affect participants’ lives or may expose them to harm (somewhat less than half the population of the United States was affected by the 2017 Equifax data breach, and the Aadhaar national identification scheme in India has led to cases not only of fraud but even death when people were unable to get inaccurate data corrected).
These concerns are applicable to all of us in our everyday lives as global citizens; they are also relevant to the collection and sharing of research data, as well as interactions between researchers and communities who are subjects of research–especially where these communities may already be disadvantaged or vulnerable. Do subjects or subject communities have input into what happens with their data? Will they have access to the results of the research in a form that is useful to them? By thinking about “what counts as data, what data are collected, and whose interests they serve” (Lindsey et al., 2017), we see that data are contextual and acquire meaning through interpretation.
- Dalton, C. & Thatcher, J. (2014). What does a critical data studies look like, and why do we care? Retrieved from http://societyandspace.org/2014/05/12/what-does-a-critical-data-studies-look-like-and-why-do-we-care-craig-dalton-and-jim-thatcher/
- DeBrusk, C., (2018). The Risk of Machine-Learning Bias (and How to Prevent It). Retrieved from https://sloanreview.mit.edu/article/the-risk-of-machine-learning-bias-and-how-to-prevent-it/
- Dencik, L., Hintz, A., & Cable, J. (2016). Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data & Society, 3(2), 2053951716679678. Retrieved from https://doi.org/10.1177/2053951716679678
- Heeks, R., & Renken, J. (2018). Data justice for development: What would it mean? Information Development, 34(1), 90-102. Retrieved from https://doi.org/10.1177/0266666916678282
- Lindsey, D., Dawn, W., Nicholas, S., Vivian, U., Megan, M., Sara, W., Environmental Data and Governance Initiative. (2017). Environmental Data Justice and the Trump Administration: Reflections from the Environmental Data and Governance Initiative. Environmental Justice, 10(6), 186-192. Retrieved from https://doi.org/10.1089/env.2017.0020
- Neff, G., Tanweer, A., Fiore-Gartland, B., & Osburn, L. (2017). Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big Data, (5)2. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/28632445
- Noble, S.U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press: New York, USA.
- Panah, A. S., & McCosker, A. (2018) Five projects that are harnessing big data for good. Retrieved from https://theconversation.com/five-projects-that-are-harnessing-big-data-for-good-104844
- Petty, T., Saba, M., Lewis, T., Gangadharan, S.P., & Eubanks, V. Our Data Bodies: Reclaiming Our Data. (2018). Retrieved from https://www.odbproject.org/wp-content/uploads/2016/12/ODB.InterimReport.FINAL_.7.16.2018.pdf
- Reid, G., (2018). Big Brother Is Watching, And Counting: Law and Order In The Age Of Big Data. Retrieved from https://newmatilda.com/2018/10/30/big-brother-watching-counting-law-order-age-big-data/
- Tauberer, J. (2017). I’m not organizing Open Data Day DC this year — these three reasons won’t surprise you. Retrieved from https://medium.com/civic-tech-thoughts-from-joshdata/im-not-organizing-open-data-day-dc-this-year-these-three-reasons-won-t-surprise-you-7611935e84e8
- Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 2053951717736335. Retrieved from https://doi.org/10.1177/2053951717736335
- Toobin, Jeffrey. (2014). The Solace of Oblivion: In Europe, the right to be forgotten trumps the Internet. The New Yorker, Sept. 29. Retrieved from https://www.newyorker.com/magazine/2014/09/29/solace-oblivion
- Tufekci, Z. (2018). Facebook’s Surveillance Machine. The New York Times. Retrieved from https://www.nytimes.com/2018/03/19/opinion/facebook-cambridge-analytica.html
Why is this important? If data are our surrogates or parts of ourselves (Petty et al., 2018), our real-world access to services, fair treatment, and resources can be impacted by decisions made based on data that may itself be biased, or reflect a biased world. In the end, data justice is about the right not to have our individual realities or identities erased and replaced by incomplete representations; to be treated as more than a compilation of data points.
As our data become more interconnected, data breaches are becoming more common and wider-reaching, for example, almost every U.S. voter registered in the past ten years had data and analytics on them exposed, as discovered in 2018.
- What is your approach to personal data disclosure?
- Do you take steps to mitigate risk or feel that exposure is inevitable?
- What would having ownership of your data look like to you?
- If you reconstructed yourself, purely from the data that you could access about yourself, do you feel that this would be an accurate representation of yourself?
Activity #1: Are there examples from the research that your organisation does where you could apply any of the following 7 principles?
- Situating ‘big data’ in time and space
- Technology is never as neutral as it appears
- ‘Big data’ does not determine social forms: confronting hard technological determinism
- Data is never raw
- Big isn’t everything
- What can Geographers do? What is our praxis?
Activity #2: Facilitate a discussion based on the article “How insurance companies invented the data-mining of personal & medical information” and this ProPublica piece.
- Example questions: Do you see ways in which the medical and insurance decisions discussed in these articles might impact you, or people you know? What asymmetries of power might be reinforced by the practices described? What types of safeguards might help prevent abuse of this data? You may want to reflect in light of this definition of “Data Bodies” from the Our Data Bodies project:
- “‘Our data bodies are discrete parts of our whole selves that are collected, stored in databases, the cloud, and other spaces of digitally networked flows, and used to make decisions or determinations about us. They are a manifestation of our relationships with our communities and institutions, including institutions of privilege, oppression, and domination.’”
Activity #3: Request your data from Twitter, Facebook, Google, LexisNexis (a company that “uses 442 nonmedical personal attributes to predict a person’s medical costs”) or another service. Individually or in a group, take some time to examine what is provided.
- Example questions: Is there anything included that you did not realise was being collected? Does the company make any statements about whether what you receive upon request includes all data they have on you, or who else they may share the data with? What types of assumptions or decisions might someone with access to this data make?
- For those working in a research context: How might these questions be integrated as part of the research design process, keeping in mind and collaborating with communities on whom research data is collected? E.g., research methodology, data management, data sharing and publication considerations.
Activity #4: In small groups, brainstorm how a data ecosystem in which data justice has been realized might differ from our current state of affairs. For either personal or research data,
- Who would have access to data? Who would make decisions about access?
- What would data be used for, or not be used for?
- What types of services might cease to exist, or what new types of services might arise?
Draw a diagram of this ecosystem representing the flow of data between actors, and share your brainstorms with the larger group. (This may be most useful building on one of the two previous activities, or after another activity reflecting more deeply on the current data economy).
Activity #5: What’s in Your Wallet? Download the worksheet from Our Data Bodies and use the cards in your wallet to fill out the sheet. Reflect on the questions on the sheet, and share in a group. How do your reflections and realisations from this discussion make you feel? If desired, download the “Tips for Protecting Our Data” sheet for ideas on how to take steps to protect your personal data.