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Errors, biases and overconfidence in artificial emotional modeling

Conference Paper
Publication Date:
2019
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.
Iris type:
4.1 Contributo in Atti di convegno
Keywords:
Affective computing; Emotion; Errors; Gendered; HRI; Overconfidence
List of contributors:
Vallverdu, J.; Franzoni, V.; Milani, A.
Authors of the University:
MILANI ALFREDO
Handle:
https://iris.unilink.it/handle/20.500.14085/42899
Book title:
Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WI 2019 Companion
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URL

https://dl.acm.org/doi/10.1145/3358695.3361749
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