# Data Science for Social Good

arXiv: https://arxiv.org/abs/2311.14683 · HTML: https://mlsanity.com/paper/2311.14683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 79 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)

## Abstract (arXiv)

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.