A Framework for Understanding Automation Propensities in the Job MarketWith 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.Song, CarsoncreatorSandstede, Bjornthesis advisorPavlick, ElliereaderBrown University. Applied Mathematicssponsor2019textborn digitalEnglishSenior thesis (ScB)--Brown University, 2019Applied Math and Computer SciencethesesAutomation10.26300/rrhe-3m52In CopyrightCollection is open for research.