Decision Trees, Random Forests, and the Genealogy of the Black Box

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


This chapter concerns the genesis and development of one of the foremost kinds of algorithms for supervised learning: decision trees. A series of researchers came obliquely to trees in the 1970s: a data-driven statistician, a machine learning expect focused on large data sets, social scientists unhappy with multivariate statistics, and a physicist interested mostly in computers who eventually was tenured in a statistics department. The history of trees is iterative: the implementation of algorithms on actually existing computers with various limitations drives the development and transformation of the techniques. Before the recent renaissance and current triumph of neural networks, decision trees were central to the transformation of artificial intelligence and machine learning of recent years: the shift in the central goal to a focus on prediction at the expense of concerns with human intelligibility, and a shift from symbolic interpretation to potent but inscrutable black boxes. Trees exploded in the late 1980s and 1990s as paragons of interpretable algorithms but developed in the late 1990s into an example of powerful but opaque ensemble models, predictive but almost unknowable. In some cases, techniques imported from academic statistics have made them even less attractive objects within traditional statistics, even as they are embedding in increasingly ubiquitous systems making judgments on our behalf. We need to explain, rather than take as given, the shift in values to prediction—to an instrumentalism—central to the ethos and practice of the contemporary data sciences. Studying trees, as this chapter does, will help us do this.

Original languageEnglish (US)
Title of host publicationAlgorithmic Modernity
Subtitle of host publicationMechanizing Thought and Action, 1500–2000
PublisherOxford University Press
Number of pages27
ISBN (Electronic)9780197502426
StatePublished - Jan 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Arts and Humanities


  • Algorithms
  • Decision trees
  • Epistemic virtues
  • Instrumentalism
  • Machine learning
  • Prediction
  • Statistics


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