AI for Social Justice: New Methodological Horizons in Technical Communication. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Graham, S. Scott; Hopkins, Hannah R.
• Is Part Of: Technical communication quarterly. Volume 31:Issue 1(2022)
• Page Start: 89
• Page End: 102
• Publication Date: 2022-01-02
• Subjects: Data feminism -- artificial intelligence -- machine learning -- feature engineering -- natural language processing; Technical writing -- Periodicals; Technical writing -- Study and teaching -- Periodicals; 808.0666
• DOI: 10.1080/10572252.2021.1955151
• Languages: English
• ISSNs: 1057-2252

Abstract

ABSTRACT: This Methodologies and Approaches piece argues artificially intelligent machine learning systems can be used to effectively advance justice-oriented research in technical and professional communication (TPC). Using a preexisting dataset investigating patient marginalization in pharmaceuticals policy discourse, we built and tested 49 machine learning systems designed to identify and track rhetorical features of interest. Three popular and one new approach to feature engineering (text quantification) were evaluated. The results indicate that these systems have great potential for use in TPC research.