Science· 2017Q1
Beyond prediction: Using big data for policy problems
- 580citations
- Q1SCImago
- 2017year
Short summary
Machine learning excels at prediction, but bridging the gap to actual policy decisions requires understanding underlying assumptions for optimized data-driven outcomes.
AI-generated from the title and abstract; the full text is not read.
Key points
- Machine learning is widely used for prediction in fields like medicine and public safety.
- A gap exists between making predictions and making policy decisions.
- Understanding underlying assumptions is key to optimizing data-driven decision-making.
AI-generated from the title and abstract; the full text is not read.
Abstract
Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.
The authors' abstract, as published at the source. Science, 2017 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences