Title Information
Title
A Framework for Understanding Automation Propensities in the Job Market
Abstract
With the phrases “artificial intelligence” and “Deep Learning” surfacing in the news alongside projections of rampant automation, this paper aims to explore Deep Learning in order to clarify its ability to disrupt the job market. We propose that automation occurs at the task level and that the probability a job is automated can be understood in terms of how likely it is that each task required within that job becomes automated. The paper uses a three-dimensional task space into which any task can be mapped and analyzes how Deep Learning performs in each segment of this task space. Then, a job can be separated into its component tasks, these tasks can be analyzed within our framework, and the automation propensity of the job can be estimated in terms of the summation of its task-level automation propensities.
Name: Personal
Name Part
Song, Carson
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/cre")
creator
Name: Personal
Name Part
Sandstede, Bjorn
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/ths")
thesis advisor
Name: Personal
Name Part
Pavlick, Ellie
Role
Role Term
reader
Name: Corporate
Name Part
Brown University. Applied Mathematics
Role
Role Term: Text
sponsor
Origin Information
Copyright Date
2019
Type of Resource
text
Physical Description
digitalOrigin
born digital
Language
Language Term: Text (ISO639-2B) (authorityURI="http://id.loc.gov/vocabulary/iso639-2.html", valueURI="http://id.loc.gov/vocabulary/iso639-2/eng")
English
Note: thesis
Senior thesis (ScB)--Brown University, 2019
Note (displayLabel="Concentration")
Applied Math and Computer Science
Genre (aat)
theses
Subject (fast) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00822786")
Topic
Automation
Identifier: DOI
10.26300/rrhe-3m52
Access Condition: rights statement (href="http://rightsstatements.org/vocab/InC/1.0/")
In Copyright
Access Condition: restriction on access
Collection is open for research.