This project investigates the susceptibility of jobs to computerisation and particularly which features of a job determine the probability of computerisation. This is achieved by using Gaussian Process Classification. A set of labelled occupations is used to train and test the model and the effect of using different feature sets is explored. Feature selection in the form of greedy selection is used to find the feature set that achieves the best classification and thereby find the features that are most significant when determining if a job can be computerised. It is concluded that the most important feature is Originality and the best feature set for classifying the data in this project consists of Originality and Service Orientation. Furthermore, experiments are performed using linear embedding methods for feature learning. However, these experiments fail to prove that better classification can be achieved using this method.
Other Recent Journal Article / Working Papers
Convergent trends and spatiotemporal patterns of Aedes-borne arboviruses in Mexico and Central America
A novel fluoro-electrochemical technique for classifying diverse marine nanophytoplankton
International Governance of Civilian AI: A Jurisdictional Certification Approach
Chemical Recycling of Commercial Poly(l-lactic acid) to l-Lactide Using a High-Performance Sn(II)/Alcohol Catalyst System
Drought-tolerant succulent plants as an alternative crop under future global warming scenarios in sub-Saharan Africa
Evaluating fossil fuel companies’ alignment with 1.5 °C climate pathways