Machine Learning and Human Perspective

Underwood, Ted

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https://hdl.handle.net/2142/109140 Copy

Description

Title

Machine Learning and Human Perspective

Author(s)

Underwood, Ted

Issue Date

2020-01-20

Keyword(s)

• Hermeneutics
• Machine learning
• Literary theory
• Speculative fiction
• Science fiction
• Fantasy fiction
• Digital humanities

Abstract

We've been taught that numbers are good at measuring objective facts, and ham-handed when it comes to slippery subjective questions. But this folk maxim is exactly wrong about twenty-first-century quantitative methods. Machine learning algorithms are actually quite bad at being objective, and very good at absorbing human perspectives implicit in the evidence used to train them. They will be valuable for humanists, not as objective oracles, but as ways of representing parallax and historical change. To dramatize perspectival uses of machine learning, I train models of genre on groups of books categorized by historical actors who range from Edwardian advertisers to contemporary librarians. Comparing the perspectives implicit in their choices casts new light on received histories of genre. Scientific romance and science fiction—whose shifting names have often suggested a fractured history—turn out to be more stable across two centuries than the genre we call fantasy.

Publisher

Modern Language Association of America

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/109140

DOI

10.1632/pmla.2020.135.1.92

Sponsor(s)/Grant Number(s)

Work on this article was supported by a Meyer H. Abrams fellowship at the National Humanities Center.

Copyright and License Information

The publisher's exclusive license to distribute expired after one year; as of January 2021, this publication is in the public domain licensed CC-BY.

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