loading page

Non-expert AI Users Building Machine Learning Models: A Short Survey of AutoML Systems
  • +2
  • Camilo Palazuelos,
  • Rafael Duque,
  • Cristina Tîrnȃucȃ,
  • Alejandro Pérez,
  • Abraham Casas
Camilo Palazuelos
Department of Mathematics, Statistics, and Computer Science, University of Cantabria, Avenida de los Castros S/N

Corresponding Author:

Rafael Duque
Department of Mathematics, Statistics, and Computer Science, University of Cantabria, Avenida de los Castros S/N

Corresponding Author:[email protected]

Author Profile
Cristina Tîrnȃucȃ
Department of Mathematics, Statistics, and Computer Science, University of Cantabria, Avenida de los Castros S/N
Alejandro Pérez
Scientific and Technological Park of Cantabria (PCTCAN), Centro Tecnológico de Componentes (CTC)
Abraham Casas
Scientific and Technological Park of Cantabria (PCTCAN), Centro Tecnológico de Componentes (CTC)

Abstract

The Artificial Intelligence for All initiative strives to ensure that artificial intelligence is accessible and beneficial to individuals from diverse backgrounds, regardless of their knowledge or expertise. Machine learning, a critical component of artificial intelligence, enables computers to learn from data and facilitates informed decision-making. Automated machine learning, or AutoML, automates the machine learning pipeline and thus makes it accessible to non-expert users. Although prior scientific research has reviewed various aspects of AutoML, its potential to democratize machine learning remains unexplored. In this survey, we aim to fill this gap by (i) reviewing systems and tools that help visualize the machine learning pipeline, (ii) evaluating the degree of user control in the process and (iii) identifying risks and limitations of current AutoML systems.
13 Jan 2024Submitted to TechRxiv
25 Jan 2024Published in TechRxiv