Errors, biases and overconfidence in artificial emotional modeling

Jordi Vallverdù, Valentina Franzoni, Alfredo Milani

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

With the diffusion and successful new implementation of several machine learning techniques, together with the substantial cost decrease of sensors, also included in mobile devices, the field of emotional analysis and modeling has boosted. Apps, web apps, brain-scanning devices, and Artificial Intelligence assistants often include emotion recognition features or emotional behaviors, but new researches contain, maintain, or create several design errors, which analysis is the main aim of this paper.

Original languageAmerican English
Pages (from-to)86-90
Number of pages5
JournalProceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WI 2019 Companion
DOIs
Publication statusPublished - 14 Oct 2019

Keywords

  • Affective computing
  • Emotion
  • Errors
  • Gendered
  • HRI
  • Overconfidence

Fingerprint

Dive into the research topics of 'Errors, biases and overconfidence in artificial emotional modeling'. Together they form a unique fingerprint.

Cite this