The following selected bibliography lists works that challenge the traditional view of data, Geographic Information Systems (GIS) and mapping. Listed are works that critically analyze data studies, mapping and GIS from decolonial, antiracist, feminist, queer, LGBTQIA2S+ and disability perspectives.
Please also see our Canadian Queer Data Guide for data on Canada's 2SLGBTQIA+ populations.
Data
Ahmed Asif, Manan. 2019. “Technologies of Power – From Area Studies to Data Sciences.” Spheres: Journal for Digital Cultures 5 (November). https://spheres-journal.org/contribution/technologies-of-power-from-area-studies-to-data-sciences/.
Benjamin, Ruha. 2019. Race after Technology: Abolitionist Tools for the New Jim Code. Polity Press. https://www.wiley.com/en-us/Race+After+Technology%3A+Abolitionist+Tools+for+the+New+Jim+Code-p-9781509526437.
Bjork, Collin. 2023. “ChatGPT Threatens Language Diversity. More Needs to Be Done to Protect Our Differences in the Age of AI.” The Conversation, February 9. http://theconversation.com/chatgpt-threatens-language-diversity-more-needs-to-be-done-to-protect-our-differences-in-the-age-of-ai-198878.
Bode, Katherine. 2020. “Why You Can’t Model Away Bias.” Modern Language Quarterly 81 (1): 95–124. https://doi.org/10.1215/00267929-7933102.
Bryan, Joe, and Denis Wood. 2015. Weaponizing Maps: Indigenous Peoples and Counterinsurgency in the Americas. The Guilford Press.
Center for Antiracist Research. 2022. “TOWARD EVIDENCE-BASED ANTIRACIST POLICYMAKING: Problems and Proposals for Better Racial Data Collection and Reporting.” Center for Antiracist Research, May. https://www.bu.edu/antiracism-center/files/2022/06/Toward-Evidence-Based-Antiracist-Policymaking.pdf.
Christian, Brian. 2021. ALIGNMENT PROBLEM: Machine Learning and Human Values. W W NORTON.
Chun, Wendy Hui Kyong. 2021. Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition. MIT Press. https://librarysearch.library.utoronto.ca/permalink/01UTORONTO_INST/blpd0s/alma991107112195106196.
Collier, Brittany. 2024. “Celebrating Indigenous-Led Data Initiatives.” HillNotes, June 21. https://hillnotes.ca/2024/06/21/celebrating-indigenous-led-data-initiatives/.
Crawford, Kate. 2021. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
Criado-Perez, Caroline. 2019. Invisible Women: Exposing Data Bias in a World Designed for Men. Chatto & Windus.
D’Ignazio, Catherine. 2015. What Would Feminist Data Visualization Look Like? December 1. https://civic.mit.edu/feminist-data-visualization.html.
D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. Strong Ideas. MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001.
Eubanks, Virginia. 2019. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. First Picador edition. Picador.
Fry, Hannah. 2021. “What Data Can’t Do.” The New Yorker, March 22. https://www.newyorker.com/magazine/2021/03/29/what-data-cant-do.
Guyan, Kevin. 2022. Queer Data: Using Gender, Sex and Sexuality Data for Action. Bloomsbury Studies in Digital Cultures. Bloomsbury Academic.
Harford, Tim. 2021. The Data Detective: Ten Easy Rules to Make Sense of Statistics. First American edition. Riverhead Books.
Keilty, Patrick, ed. 2024. Queer Data Studies. With Rebecca Herzig and Banu Subramaniam. Feminist Technosciences Series. University of Washington Press.
Lane, Julia. 2021. Democratizing Our Data: A Manifesto. The MIT Press.
Lewis, T., S. P. Gangadharan, M. Saba, and T. Petty. 2018. Digital Defense Playbook: Community Power Tools for Reclaiming Data. Our Data Bodies. https://www.odbproject.org/wp-content/uploads/2019/03/ODB_DDP_HighRes_Single.pdf.
Lucchesi, Annita Hetoevėhotohke’e. 2020. “Spatial Data and (De)Colonization: Incorporating Indigenous Data Sovereignty Principles into Cartographic Research.” Cartographica 55 (3): 163–69.
Nelson, Amy Hawn, Della Jenkins, Sharon Zanti, et al. 2020. A Toolkit for Centering Racial Equity Throughout Data Integration. Actionable Intelligence for Social Policy, University of Pennsylvania. https://aisp.upenn.edu/resource-article/a-toolkit-for-centering-racial-equity-throughout-data-integration/.
Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. University Press.
Oguamanam, Chidi. 2019. Indigenous Data Sovereignty: Retooling Indigenous Resurgence for Development. Centre for International Governance Innovation.
O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Allen Lane.
Onuoha, Mimi, Sparshith Sampath, Myles Braithwaite, and Corin Faife. (2016) 2018. On Missing Data Sets. February 3, released January 24. https://github.com/MimiOnuoha/missing-datasets.
Pruden, Harlan, and Travis Salway. 2020. “Meet the Methods Series: ‘What and Who Is Two-Spirit?’ In Health Research - CIHR.” With Canadian Institutes of Health Research, Government of Canada. October 28. https://cihr-irsc.gc.ca/e/52214.html.
Quinless, Jacqueline M. 2022. Decolonizing Data: Unsettling Conversations about Social Research Methods. University of Toronto Press.
So, Richard Jean. 2020. Redlining Culture: A Data History of Racial Inequality and Postwar Fiction. Columbia University Press.
Stone, Deborah. 2020. Counting: How We Use Numbers to Decide What Matters. First edition. Liveright.
Taylor, John, and Tahu Kukutai. 2016. Indigenous Data Sovereignty: Toward an Agenda. With John Taylor and Tahu Kukutai. Vol. 38. Research Monograph / Centre for Aboriginal Economic Policy Research, College of Arts and Social Sciences, The Australian National University, Canberra. ANU Press. https://doi.org/10.22459/CAEPR38.11.2016.
Walter, Maggie. 2013. Indigenous Statistics: A Quantitative Research Methodology. Left Coast Press. https://www.routledge.com/Indigenous-Statistics-A-Quantitative-Research-Methodology/Walter-Andersen/p/book/9781611322934.
Walter, Maggie. 2021. Indigenous Data Sovereignty and Policy. Routledge Studies in Indigenous Peoples and Policy. Routledge.
Wiggins, Chris H. 2023. How Data Happened: A History from the Age of Reason to the Age of Algorithms. First edition. With Matthew L. Jones. W.W. Norton & Company.
Wong, Wendy H. 2023. We, the Data. MIT Press. https://www.penguinrandomhouse.com/books/730905/we-the-data-by-wendy-h-wong/.
GIS & Mapping
Bryan, Joe, and Denis Wood. 2015. Weaponizing Maps: Indigenous Peoples and Counterinsurgency in the Americas. The Guilford Press.
Heeks, Richard, and Satyarupa Shekhar. 2019. “Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework.” Information, Communication & Society 22 (7): 992–1011. https://doi.org/10.1080/1369118X.2019.1599039.
“How Maps Can Help the Fight for Racial Equity.” n.d. Accessed August 12, 2022. https://datasmart.ash.harvard.edu/news/article/how-maps-can-help-fight-racial-equity.
Lavery, Diana. n.d. “Presenting Breakdowns by Race/Ethnicity (or Any Groups) in Your Maps.” ArcGIS Blog. Accessed August 12, 2022. https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/techniques-for-presenting-breakdowns-by-race-ethnicity-or-any-groups-in-your-maps/.
“Mapping Inequality.” n.d. Accessed August 12, 2022. https://dsl.richmond.edu/panorama/redlining/.
“Mapping Prejudice.” n.d. Accessed August 12, 2022. https://mappingprejudice.umn.edu/.
Mapping Racism. n.d. “Mapping Racism.” Accessed August 12, 2022. https://mappingracism.home.blog/.
Mattke, Ryan, Kirsten Delegard, and Danya Leebaw. 2022. “Mapping Prejudice: The Map Library as a Hub for Community Co-Creation and Social Change.” Journal of Map & Geography Libraries 18 (1–2): 1–21. https://doi.org/10.1080/15420353.2022.2076006.
Muhammad, Khalil Gibran. 2010. The Condemnation of Blackness: Race, Crime, and the Making of Modern Urban America. Harvard University Press.
Sefala, Raesetje, Timnit Gebru, Luzango Mfupe, Nyalleng Moorosi, and Richard Klein. 2021. “Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa.” Paper presented at Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). November 19. https://openreview.net/forum?id=WV0waZz9dTF.
stories, A. summary of top, and The Latest Geospatial Trends Emailed Direct to You. n.d. “Equity Mapping: How We Can Do More with GIS.” Accessed August 12, 2022. https://www.directionsmag.com/article/11217.
Tableau Software. n.d. “Seattle- Race and Segregation Maps by Census Tracts 1940-2020.” Accessed August 12, 2022. https://public.tableau.com/views/Seattle-segregation1940-2020largescreen/Story1940-2020?:embed=y&:showVizHome=no&:host_url=https%3A%2F%2Fpublic.tableau.com%2F&:embed_code_version=3&:tabs=no&:toolbar=yes&:animate_transition=yes&:display_static_image=no&:display_spinner=no&:display_overlay=yes&:display_count=yes&:language=en-US&publish=yes&:loadOrderID=0.
“Urban Renewal, 1950-1966.” n.d. Accessed August 12, 2022. https://dsl.richmond.edu/panorama/renewal/#view=0/0/1&viz=cartogram.
Wood, Denis., and John. Fels. 1992. The Power of Maps. Mappings. Guilford Press.
AI
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, March 1, 610–23. https://doi.org/10.1145/3442188.3445922.
Bjork, Collin. 2023. “ChatGPT Threatens Language Diversity. More Needs to Be Done to Protect Our Differences in the Age of AI.” The Conversation, February 9. http://theconversation.com/chatgpt-threatens-language-diversity-more-needs-to-be-done-to-protect-our-differences-in-the-age-of-ai-198878.
Buolamwini, Joy, and Timnit Gebru. 2018. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” Proceedings of the 1st Conference on Fairness, Accountability and Transparency, January 21, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html.
Crawford, Kate. 2021a. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. 1st ed. Yale University Press. https://doi.org/10.2307/j.ctv1ghv45t.
Crawford, Kate. 2021b. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
Dubber, Markus D., Frank Pasquale, and Sunit Das, eds. 2020. The Oxford Handbook of Ethics of AI. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190067397.001.0001.
Gebru, Timnit. 2020. “Race and Gender.” In The Oxford Handbook of Ethics of AI, edited by Markus D. Dubber, Frank Pasquale, and Sunit Das. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190067397.013.16.
Grynbaum, Michael M., and Ryan Mac. 2023. “The Times Sues OpenAI and Microsoft Over A.I. Use of Copyrighted Work.” Business. The New York Times, December 27. https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html.
Hoang, Lê Nguyên., and El Mahdi El Mhamdi. 2019. Le Fabuleux Chantier: Rendre l’intelligence Artificielle Robustement Bénéfique. Hors Collection. EDP Sciences.
Hoffmann, Anna Lauren. 2019. “Where Fairness Fails: Data, Algorithms, and the Limits of Antidiscrimination Discourse.” Information, Communication & Society 22 (7): 900–915. https://doi.org/10.1080/1369118X.2019.1573912.
Le Bui, Matthew, and Safiya Umoja Noble. 2020. “We’re Missing a Moral Framework of Justice in Artificial Intelligence: On the Limits, Failings, and Ethics of Fairness.” In The Oxford Handbook of Ethics of AI, edited by Markus D. Dubber, Frank Pasquale, and Sunit Das. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190067397.013.9.
Liu, Nelson F., Tianyi Zhang, and Percy Liang. 2023. “Evaluating Verifiability in Generative Search Engines.” arXiv:2304.09848. Preprint, arXiv, April 19. https://doi.org/10.48550/arXiv.2304.09848.
Luminoso. 2018. “Debiasing AI Systems: A Conversation with Rob Speer, Luminoso’s Head of Science.” With Denise Christie. October 19. https://web.archive.org/web/20181019010043/https://www.luminoso.com/resources/debiasing-ai-systems.
Miceli, Milagros, and Julian Posada. 2022. “The Data-Production Dispositif.” arXiv:2205.11963. Preprint, arXiv, May 24. http://arxiv.org/abs/2205.11963.
Mitchell, Melanie. 2019. Artificial Intelligence: A Guide for Thinking Humans. First edition. Farrar, Straus and Giroux.
Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. University Press.
Oliver, Pamela, Alex Hanna, and Chaeyoon Lim. 2023. “Constructing Relational and Verifiable Protest Event Data: Four Challenges and Some Solutions.” Mobilization: An International Quarterly 28 (1): 1–22. https://doi.org/10.17813/1086-671X-28-1-1.
Oliver, Pamela, Chaeyoon Lim, Morgan C. Matthews, and Alex Hanna. 2022. “Black Protests in the United States, 1994 to 2010.” Sociological Science 9 (May): 275–312. https://doi.org/10.15195/v9.a12.
Onuoha, Mimi. 2018. “Notes on Algorithmic Violence.” GitHub, February 22. https://github.com/MimiOnuoha/On-Algorithmic-Violence.
Pinney, Christine, Amifa Raj, Alex Hanna, and Michael D. Ekstrand. 2023. “Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access.” Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (New York, NY, USA), CHIIR ’23, March 20, 269–79. https://doi.org/10.1145/3576840.3578316.
Pushkin Industries. 2021. “Project Dillard: Revisionist History.” July 8. https://www.pushkin.fm/podcasts/revisionist-history/project-dillard.
Sefala, Raesetje, Timnit Gebru, Luzango Mfupe, Nyalleng Moorosi, and Richard Klein. 2021. “Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa.” Paper presented at Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). November 19. https://openreview.net/forum?id=WV0waZz9dTF.
The Globe and Mail. 2023. “We Can Build Better, Fairer Algorithms in a World of Angry Bias – so Why Aren’t We?” April 13. https://www.theglobeandmail.com/canada/article-we-can-build-better-fairer-algorithms-in-a-world-of-angry-bias-so-why/.
Williams, Adrienne, Milagros Miceli, and Timnit Gebru. 2022. “The Exploited Labor Behind Artificial Intelligence.” Noema, October 13. https://www.noemamag.com/the-exploited-labor-behind-artificial-intelligence.
ZettaBytes, EPFL, dir. 2018. AI Safety against Adversarial Attacks (Ft. El Mahdi El Mhamdi). 9:52. https://www.youtube.com/watch?v=1Ds1DAnoaag.