The Reverse of Engineering

2020 ◽  
Vol 119 (1) ◽  
pp. 75-93
Author(s):  
Brett Neilson

Insofar as planning mediates between the order of what is and the question of what might be, it is not only a matter of philosophy but also one of engineering. Particularly at a time when routines of financial speculation and pattern recognition have colonized the making of futures, planning has become a process of creating architectural opportunities from scattered corpuses of extracted data. Mindful of the importance of machine learning in such processes, this article critically grapples with the proposition that techniques of reverse engineering offer a means of cracking these future making routines and turning them toward projects of social and political ameli oration. I argue that technical practices of reverse engineering need to articulate to radical political projects and modes of organization. Drawing on computer science studies of adversarial machine learning, I also consider the question of whether reverse engineering of machine learning techniques is technically possible. Ultimately, the article contrasts political claims for reverse engineering with what I call the reverse of engineering, or a program that entails the subordination of data to futures rather than planning processes that work from the merely evidential and measurable.

Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.


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