research-article

Errors, Biases and Overconfidence in Artificial Emotional Modeling

Authors:

Valentina Franzoni,

Jordi Vallverdù, and

Alfredo MilaniAuthors Info & Claims

WI '19 Companion: IEEE/WIC/ACM International Conference on Web Intelligence - Companion Volume

October 2019

Pages 86 - 90

https://doi.org/10.1145/3358695.3361749

Published: 14 October 2019 Publication History

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.

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Cited By

Franzoni VBiondi GMilani A(2024)Advanced techniques for automated emotion recognition in dogs from video data through deep learningNeural Computing and Applications10.1007/s00521-024-10042-3Online publication date: 4-Jul-2024
https://doi.org/10.1007/s00521-024-10042-3
Debnath RCreutzig FSovacool BShuckburgh E(2023)Harnessing human and machine intelligence for planetary-level climate actionnpj Climate Action10.1038/s44168-023-00056-32:1Online publication date: 17-Aug-2023
https://doi.org/10.1038/s44168-023-00056-3
Franzoni V(2023)From Black Box to Glass Box: Advancing Transparency in Artificial Intelligence Systems for Ethical and Trustworthy AIComputational Science and Its Applications – ICCSA 2023 Workshops10.1007/978-3-031-37114-1_9(118-130)Online publication date: 29-Jun-2023
https://doi.org/10.1007/978-3-031-37114-1_9
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cover image ACM Other conferences

WI '19 Companion: IEEE/WIC/ACM International Conference on Web Intelligence - Companion Volume

October 2019

326 pages

ISBN:9781450369886

DOI:10.1145/3358695

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

Publication History

Published: 14 October 2019

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Funding Sources

University of Perugia, Italy

Conference

WI '19

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 14 - 17, 2019

Thessaloniki, Greece

Acceptance Rates

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Franzoni VBiondi GMilani A(2024)Advanced techniques for automated emotion recognition in dogs from video data through deep learningNeural Computing and Applications10.1007/s00521-024-10042-3Online publication date: 4-Jul-2024
https://doi.org/10.1007/s00521-024-10042-3
Debnath RCreutzig FSovacool BShuckburgh E(2023)Harnessing human and machine intelligence for planetary-level climate actionnpj Climate Action10.1038/s44168-023-00056-32:1Online publication date: 17-Aug-2023
https://doi.org/10.1038/s44168-023-00056-3
Franzoni V(2023)From Black Box to Glass Box: Advancing Transparency in Artificial Intelligence Systems for Ethical and Trustworthy AIComputational Science and Its Applications – ICCSA 2023 Workshops10.1007/978-3-031-37114-1_9(118-130)Online publication date: 29-Jun-2023
https://doi.org/10.1007/978-3-031-37114-1_9
Hamid TChhabra MRavulakollu KSingh PDalal SDewan R(2022)A Review on Artificial Intelligence in Orthopaedics2022 9th International Conference on Computing for Sustainable Global Development (INDIACom)10.23919/INDIACom54597.2022.9763178(365-369)Online publication date: 23-Mar-2022
https://doi.org/10.23919/INDIACom54597.2022.9763178
Cabitza FCampagner AMattioli M(2022)The unbearable (technical) unreliability of automated facial emotion recognitionBig Data & Society10.1177/205395172211295499:2(205395172211295)Online publication date: 2-Oct-2022
https://doi.org/10.1177/20539517221129549
Franzoni VMezzetti D(2022)Recognizing and Predicting Neonatal Pain in Preterm Intensive Care Unit: a Study Protocol2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00107(680-682)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00107
Xie BSidulova MPark C(2021)Robust Multimodal Emotion Recognition from Conversation with Transformer-Based Crossmodality FusionSensors10.3390/s2114491321:14(4913)Online publication date: 19-Jul-2021
https://doi.org/10.3390/s21144913
Bonassi AGhilardi TGabrieli GTruzzi ADoi HBorelli JLepri BShinohara KEsposito G(2021)The Recognition of Cross-Cultural Emotional Faces Is Affected by Intensity and Ethnicity in a Japanese SampleBehavioral Sciences10.3390/bs1105005911:5(59)Online publication date: 23-Apr-2021
https://doi.org/10.3390/bs11050059
Adikari ANawaratne RDe Silva DRanasinghe SAlahakoon OAlahakoon D(2021)Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial IntelligenceJournal of Medical Internet Research10.2196/2734123:4(e27341)Online publication date: 30-Apr-2021
https://doi.org/10.2196/27341
Casacuberta DVallverdú J(2021)Biases in Assigning Emotions in Patients Due to Multicultural IssuesHandbook of Artificial Intelligence in Healthcare10.1007/978-3-030-83620-7_9(215-228)Online publication date: 27-Nov-2021
https://doi.org/10.1007/978-3-030-83620-7_9
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