Emancipating data science for Black and Indigenous students via liberatory datasets and curricula
Keywords:data harms, data science, liberatory pedagogy, curricula, data justice
Despite findings highlighting the severe underrepresentation of women and minoritized groups in data science, most scholarly research has focused on new methodologies, tools, and algorithms as opposed to who data scientists are or how they learn their craft. This paper proposes that increased representation in data science can be achieved via advancing the curation of datasets and pedagogies that empower Black, Indigenous, and other minoritized people of color to enter the field. This work contributes to our understanding of the obstacles facing minoritized students in the classroom and solutions to mitigate their marginalization.
How to Cite
Copyright (c) 2022 Thema Monroe-White
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.
The Creative Commons-Attribution-Noncommercial License 4.0 International applies to all works published by IASSIST Quarterly. Authors will retain copyright of the work. Your contribution will be available at the IASSIST Quarterly website when announced on the IASSIST list server.