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.

Errors, biases and overconfidence in artificial emotional modeling

Milani A.
2019-01-01

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.
2019
Inglese
Inglese
Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WI 2019 Companion
19th IEEE/WIC/ACM International Conference on Web Intelligence Workshop, WI 2019
86
90
5
9781450369886
https://dl.acm.org/doi/10.1145/3358695.3361749
Association for Computing Machinery, Inc
Esperti anonimi
2019
grc
Internazionale
Affective computing
Emotion
Errors
Gendered
HRI
Overconfidence
3
none
Vallverdu, J.; Franzoni, V.; Milani, A.
273
info:eu-repo/semantics/conferenceObject
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14085/42899
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