Notes on “Data Science for Social Justice in the Mathematics Community”
This page contains notes on the paper “Data Science and Social Justice in the Mathematics Community” (Jones et al. 2023). The purpose of these notes is to fill in additional discussion and references not permitted by the constraints of our prospective publication venue.
Section 1
| p. 1 | QSIDE’s website is https://qsideinstitute.org. |
| p. 1 | See Flores González vs USA , 2022 for another legal brief by PRACDL supported by data analysis. |
Section 2
| p. 2 | Data Science For Social Good’s website is https://www.datascienceforsocialgood.org/. |
| p. 3 | Following decision of the Associated Press, we capitalize “Black” and do not capitalize “white” when referring to racial identities. |
| p. 4 | For more on the origins of the slogan “Nothing about us without us,” we refer the reader to Charlton (2000). |
| p. 4 | For more on critical theory, especially in its connection to liberatory pedagogy, see Freire (1996). |
| p. 4 | The disproportionate representation of white men in professional mathematics is highlighted in many places, for example this article. |
| p. 4 | Ho (2011) considers epistemic humility in another relationship involving experts, the relationship between physicians and disabled patients. Epistmic humility is also related to cultural humility, a concept popularized by Tervalon and Murray-Garcia (1998). |
| p. 5 | N. N. Alexander, Teymuroglu, and Yerger (2021) offer further illustrations of interrogating privilege in practice. |
Section 3
| p. 5 | For more on Ida B. Wells, we refer the reader to her story in her own words (Wells 2020). |
| p. 5 | One online database of police violence is the Mapping Police Violence project led by Samuel Sinyangwe. |
| p. 5 | Ciocanel et al. (2020) obtain a large data set of federal criminal sentencing records, recorded on QSIDE’s webpage. This data was further analyzed by Smith et al. (2021). |
| p. 5 | One data set on cultural representation of artists in major US museums was compiled and released by QSIDE. |
| p. 5 | Mercier and Sperber (2011) offer a theory of reasoning as primarily rhetorical rather than deductive, and includes a survey of work studying confirmation bias and other mechanisms that hinder our ability to change our minds in response to evidence. |
| p. 5 | Moon Duchin offers a popular description of collaborations between metric geometers, legislators, and legal experts in a recent article in Scientific American. |
| p. 5 | Here is one amicus brief submitted by the Metric Geometry and Gerrymandering Group to the Supreme Court. |
| p. 6 | Pierson, Corbett-Davies, and Goel (2018) offer additional work on large-scale statistical tests for bias. |
| p. 6 | Garibay (2015) discusses justice orientation as a promoter of student engagement. |
| p. 6 | For further suggestions on critical conversations to support justice-oriented mathematics pedagogy, see N. N. Alexander, Teymuroglu, and Yerger (2021). |
| p. 6 | Disparity in funding of NIH proposals is studied by Taffe and Gilpin (2021). |
| p. 6 | For further critical perspective on the role of justice and equity-oriented data science, see N. Alexander et al. (2022) and Castillo and Gillborn (2022). Eaton (2022) offers a detailed description of implementing justice orientation in machine learning pedagogy. |
Section 4
| p. 8 | The preprint referred to by Andrés R. Vindas Meléndez is Buckmire et al. (2023). |
| p. 9 | Another important limitation of the automated gender inference methods described in Heather Zinn Brooks’ vignette is the Euro- and US-centrism of most commercially available algorithms, including the ones we used. For a review of these biases and limitations, see Santamaría and Mihaljević (2018). |
Section 5
| p. 10 | Other examples include a recent workshop on Mathematics and Racial Justice at the Mathematical Sciences Research Institute (MSRI), the upcoming workshop on Interdisciplinary and Critical Data Science Motivated by Social Justice at the Institute for Mathematical and Statistical Innovation, and the second part of the program on Data Science and Social Justice at the Institute for Computational and Experimental Research in Mathematics. We also note the long-standing annual Critical Inquiry in Mathematics Education conference at MSRI. |
References
Alexander, Nathan N, Zeynep Teymuroglu, and Carl R Yerger. 2021. “Critical Conversations on Social Justice in Undergraduate Mathematics.” In Mathematics for Social Justice, 190–212. Routledge.
Alexander, Nathan, Carrie Diaz Eaton, Anelise H Shrout, Belin Tsinnajinnie, and Krystal Tsosie. 2022. “Beyond Ethics: Considerations for Centering Equity-Minded Data Science.” Journal of Humanistic Mathematics 12 (2): 254–300.
Buckmire, Ron, Carrie Diaz Eaton, Jr. Joseph E. Hibdon, Katherine M. Kinnaird, Drew Lewis, Jessica Libertini, Omayra Ortega, Rachel Roca, and Andrés R. Vindas-Meléndez. 2023. “On Definitions of “Mathematician".”
Castillo, W, and D Gillborn. 2022. “How to ‘QuantCrit:’ Practices and Questions for Education Data Researchers and Users.” EdWorkingPaper. Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/v5kh-dd65.
Charlton, James I. 2000. Nothing about Us Without Us: Disability Oppression and Empowerment. University of California Press.
Ciocanel, Maria-Veronica, Chad M Topaz, Rebecca Santorella, Shilad Sen, Christian Michael Smith, and Adam Hufstetler. 2020. “JUSTFAIR: Judicial System Transparency Through Federal Archive Inferred Records.” Plos One 15 (10): e0241381.
Eaton, Carrie Diaz. 2022. “Teaching Machine Learning in the Context of Critical Quantitative Information Literacy.” In Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, 51–56. PMLR.
Freire, Paulo. 1996. “Pedagogy of the Oppressed (Revised).” New York: Continuum 356: 357–58.
Garibay, Juan C. 2015. “STEM Students’ Social Agency and Views on Working for Social Change: Are STEM Disciplines Developing Socially and Civically Responsible Students?” Journal of Research in Science Teaching 52 (5): 610–32.
Ho, Anita. 2011. “Trusting Experts and Epistemic Humility in Disability.” IJFAB: International Journal of Feminist Approaches to Bioethics 4 (2): 102–23.
Jones, Quindel, Andrés R Vindas Meléndez, Ariana Mendible, Manuchehr Aminian, Heather Z Brooks, Nathan Alexander, Carrie Diaz Eaton, and Philip Chodrow. 2023. “Data Science and Social Justice in the Mathematics Community.” arXiv Preprint arXiv:2303.09282.
Mercier, Hugo, and Dan Sperber. 2011. “Why Do Humans Reason? Arguments for an Argumentative Theory.” Behavioral and Brain Sciences 34 (2): 57–74.
Pierson, Emma, Sam Corbett-Davies, and Sharad Goel. 2018. “Fast Threshold Tests for Detecting Discrimination.” In International Conference on Artificial Intelligence and Statistics, 96–105. PMLR.
Santamaría, Lucía, and Helena Mihaljević. 2018. “Comparison and Benchmark of Name-to-Gender Inference Services.” PeerJ Computer Science 4: e156.
Smith, Christian M, Nicholas Goldrosen, Maria-Veronica Ciocanel, Rebecca Santorella, Chad M Topaz, and Shilad Sen. 2021. “Racial Disparities in Criminal Sentencing Vary Considerably Across Federal Judges.” SocArXiv (July 2021).
Taffe, Michael A, and Nicholas W Gilpin. 2021. “Equity, Diversity and Inclusion: Racial Inequity in Grant Funding from the US National Institutes of Health.” Elife 10: e65697.
Tervalon, Melanie, and Jann Murray-Garcia. 1998. “Cultural Humility Versus Cultural Competence: A Critical Distinction in Defining Physician Training Outcomes in Multicultural Education.” Journal of Health Care for the Poor and Underserved 9 (2): 117–25.
Wells, Ida B. 2020. Crusade for Justice: The Autobiography of Ida b. Wells. University of Chicago Press.