Abstract
Data science has been described as the fourth paradigm of scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges—our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI has sparked debates about the sociotechnical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for “data science for social good” (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of sociotechnical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the JAIS special issue on data science for social good. We hope that this editorial and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are attracting proportionately less attention with each passing day.
Recommended Citation
Abbasi, Ahmed; Chiang, Roger H. L.; and Xu, Jennifer
(2023)
"Data Science for Social Good,"
Journal of the Association for Information Systems, 24(6), 1439-1458.
DOI: 10.17705/1jais.00849
Available at:
https://aisel.aisnet.org/jais/vol24/iss6/10
DOI
10.17705/1jais.00849
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