Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data. (February 2020)

• Record Type: Journal Article
• Authors: Frey, William R.; Patton, Desmond U.; Gaskell, Michael B.; McGregor, Kyle A.
• Other Names: Chen Wenhong guest-editor.; Quan-Haase Anabel guest-editor.
• Is Part Of: Social science computer review. Volume 38:Number 1(2020)
• Page Start: 42
• Page End: 56
• Publication Date: 2020-02
• Subjects: social media -- gang violence -- domain experts -- artificial intelligence -- inclusion -- qualitative methods -- natural language processing -- Big Data -- ethics -- law enforcement; Social sciences -- Data processing -- Periodicals; Computers -- Social aspects -- Periodicals; Microcomputers -- Periodicals; Sciences sociales -- Informatique -- Périodiques; Micro-ordinateurs -- Périodiques; 300.285
• DOI: 10.1177/0894439318788314
• Languages: English
• ISSNs: 0894-4393

Abstract

Mining social media data for studying the human condition has created new and unique challenges. When analyzing social media data from marginalized communities, algorithms lack the ability to accurately interpret off-line context, which may lead to dangerous assumptions about and implications for marginalized communities. To combat this challenge, we hired formerly gang-involved young people as domain experts for contextualizing social media data in order to create inclusive, community-informed algorithms. Utilizing data from the Gang Intervention and Computer Science Project—a comprehensive analysis of Twitter data from gang-involved youth in Chicago—we describe the process of involving formerly gang-involved young people in developing a new part-of-speech tagger and content classifier for a prototype natural language processing system that detects aggression and loss in Twitter data. We argue that involving young people as domain experts leads to more robust understandings of context, including localized language, culture, and events. These insights could change how data scientists approach the development of corpora and algorithms that affect people in marginalized communities and who to involve in that process. We offer a contextually driven interdisciplinary approach between social work and data science that integrates domain insights into the training of qualitative annotators and the production of algorithms for positive social impact.