Do Americans Think the Digital Economy is Fair? Using Supervised Learning to Explore Evaluations of Predictive Automation

Abstract

Predictive automation is a pervasive and archetypical example of the digital economy. Studying how Americans evaluate predictive automation is important because it affects corporate and state governance. However, we have relevant questions unanswered. We lack comparisons across use cases using a nationally representative sample. We also have yet to determine what are the key predictors of evaluations of predictive automation. This article uses the American Trends Panel’s 2018 wave ($n=4,594$) to study whether American adults think predictive automation is fair across four use cases: helping credit decisions, assisting parole decisions, filtering job applicants based on interview videos, and assessing job candidates based on resumes. Results from lasso regressions trained with 112 predictors reveal that people’s evaluations of predictive automation align with their views about social media, technology, and politics.

Editorial: Data Science Meets Social Sciences

“…The paper targets explicitly a non-expert audience, making it a valuable reference for researchers with a limited background in data handling and distributed computing. Lehoucq (2022) argued that optimism among American liberals and conservatives about technology, specifically, the fairness of predictive automation, is becoming increasingly polarized. The author relied on nationallyrepresentative survey data from the Pew Research Center's American Trends Panel and utilized a variety of machine learning techniques to understand difference in American perceptions about predictive automation.…”