How to Cautiously Uncover the "Black Box" of Machine Learning Models for Legislative Scholars. Issue 1 (2nd March 2022)

• Record Type: Journal Article
• Authors: Jordan, Soren; Paul, Hannah L.; Philips, Andrew Q.
• Is Part Of: Legislative studies quarterly. Volume 48:Issue 1(2023)
• Page Start: 165
• Page End: 202
• Publication Date: 2022-03-02
• Subjects: individual conditional expectation plots -- machine learning -- partial dependence plots -- visualization; Legislative bodies -- Periodicals; Parlement; Procédure législative; Système parlementaire; Étude comparée (Descripteur de forme); Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 328
• DOI: 10.1111/lsq.12378
• Languages: English
• ISSNs: 0362-9805

Abstract

Abstract : Machine learning models, especially ensemble and tree‐based approaches, offer great promise to legislative scholars. However, they are heavily underutilized outside of narrow applications to text and networks. We believe this is because they are difficult to interpret: while the models are extremely flexible, they have been criticized as "black box" techniques due to their difficulty in visualizing the effect of predictors on the outcome of interest. In order to make these models more useful for legislative scholars, we introduce a framework integrating machine learning models with traditional parametric approaches. We then review three interpretative plotting strategies that scholars can use to bring a substantive interpretation to their machine learning models. For each, we explain the plotting strategy, when to use it, and how to interpret it. We then put these plots in action by revisiting two recent articles from Legislative Studies Quarterly .