Data Science for Social Good
- URL: http://arxiv.org/abs/2311.14683v1
- Date: Thu, 2 Nov 2023 15:40:20 GMT
- Title: Data Science for Social Good
- Authors: Ahmed Abbasi and Roger H. L. Chiang and Jennifer J. Xu
- Abstract summary: We present a framework for "data science for social good" (DSSG) research.
We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems.
We hope that this article and the special issue will spur future DSSG research.
- Score: 2.8621556092850065
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Data science has been described as the fourth paradigm for 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 have sparked debates about the socio-technical
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 socio-technical 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 special issue. We hope that this article 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 garnering proportionately less attention with each passing day.
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