Data Science for Social Good
Ahmed Abbasi, Roger H. L. Chiang, Jennifer J. Xu

TL;DR
This paper highlights the declining focus on social good in data science research and proposes a framework to promote research that addresses societal challenges using data science techniques.
Contribution
It introduces a comprehensive framework for data science for social good (DSSG) research and analyzes the current literature to identify gaps and impediments.
Findings
Less than 10% of data science research focuses on social good.
Empirical evidence shows a decreasing trend in DSSG-related work.
The proposed framework links data science genres with social good challenges.
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,…
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Videos
Data Science for Social Good· youtube
Taxonomy
TopicsMental Health Research Topics
