Black Box Models and Sociological Explanations: Predicting GPA Using Neural Networks
Year: 2017
Type: preprint
Abstract: The Fragile Families Challenge provided an opportunity to empirically assess the applicability of black box machine learning models to sociological questions and the extent to which interpretable explanations can be extracted from these models. In this paper I use neural network models to predict high school grade-point average and examine how variations of basic network parameters affect predictive performance. Using a recently proposed technique, I identify the most important predictive variables used by the best-performing model, finding that they relate to parenting and the child’s cognitive and behavioral development, consistent with prior work. I conclude by discussing the implications of these findings for the relationship between prediction and explanation in sociological analyses.
Source: SocArXiv (OSF Preprints)
Author: Thomas Davidson
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4256005313