# Harnessing the Power of the Crowd to Increase Capacity for Data Science in the Social Sector

arXiv: https://arxiv.org/abs/1606.07781 · HTML: https://mlsanity.com/paper/1606.07781/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees](https://mlsanity.com/paper/2205.00359/) (2022)
- [Automated Distance Estimation for Wildlife Camera Trapping](https://mlsanity.com/paper/2202.04613/) (2022)
- [DynFrs: An Efficient Framework for Machine Unlearning in Random Forest](https://mlsanity.com/paper/2410.01588/) (2024)
- [Adaptive Hopfield Network: Rethinking Similarities in Associative Memory](https://mlsanity.com/paper/2511.20609/) (2025)
- [Pump It Up: Predict Water Pump Status using Attentive Tabular Learning](https://mlsanity.com/paper/2304.03969/) (2023)
- [Edge Phoneme Recognition for Children's Speech through Age-Aware Training](https://mlsanity.com/paper/2608.10206/) (2026)

## Abstract (arXiv)

We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care provider wanted to understand the effect of demographic and behavioral questions on predicting which services a woman would need. The third and final example is in government innovation: using online restaurant reviews from Yelp, competitors built models to forecast which restaurants were most likely to have hygiene violations when visited by health inspectors. Finally, we reflect on the unique benefits of the open, public competition model.