JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1053 10.6339/22-JDS1053 Data Science in Action Do Americans Think the Digital Economy is Fair? Using Supervised Learning to Explore Evaluations of Predictive Automation LehoucqEmilioemilio-l@u.northwestern.edu1∗ Department of Sociology, Northwestern University, 1810 Chicago Avenue, Evanston, IL 60208, USA Email: emilio-l@u.northwestern.edu. 20222062022203381399 Supplementary Material

This article includes a replication file with an R project, unprocessed and processed data, and a table listing all the predictors used in the models, how they are measured, and their pre-processing. The online appendix referred to in the text is also available as a supplement online at the journal’s website.

21220212652022 2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2022 Open access article under the CC BY license.

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.

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