Explainable emotion decoding for human and computer vision

Alessio Borriero; Martina Milazzo; Matteo Diano; Davide Orsenigo; Maria Chiara Villa; Chiara DiFazio; Marco Tamietto; Alan Perotti

doi:10.1007/978-3-031-63797-1_10

Explainable emotion decoding for human and computer vision

Alessio Borriero
, Martina Milazzo
, Matteo Diano
, Davide Orsenigo
, Maria Chiara Villa
, Chiara DiFazio
, Marco Tamietto
, Alan Perotti^*

^*Corresponding author for this work

Medical and Clinical Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

Modern Machine Learning (ML) has significantly advanced various research fields, but the opaque nature of ML models hinders their adoption in several domains. Explainable AI (XAI) addresses this challenge by providing additional information to help users understand the internal decision-making process of ML models. In the field of neuroscience, enriching a ML model for brain decoding with attribution-based XAI techniques means being able to highlight which brain areas correlate with the task at hand, thus offering valuable insights to domain experts.

Original language	English
Title of host publication	Explainable artificial intelligence
Editors	Luca Longo, Sebastian Lapuschkin, Christin Seifert
Place of Publication	Cham
Publisher	Springer
Pages	178-201
Number of pages	24
Volume	2
ISBN (Electronic)	9783031637971
ISBN (Print)	9783031637964
DOIs	https://doi.org/10.1007/978-3-031-63797-1_10
Publication status	Published - 2024
Event	Second World Conference: xAI 2024 - Valetta, Malta Duration: 17 Jul 2024 → 19 Jul 2024 Conference number: 2

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	2154
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Second World Conference
Country/Territory	Malta
City	Valetta
Period	17/07/24 → 19/07/24

Access to Document

10.1007/978-3-031-63797-1_10

Cite this

Borriero, A., Milazzo, M., Diano, M., Orsenigo, D., Villa, M. C., DiFazio, C., Tamietto, M., & Perotti, A. (2024). Explainable emotion decoding for human and computer vision. In L. Longo, S. Lapuschkin, & C. Seifert (Eds.), Explainable artificial intelligence (Vol. 2, pp. 178-201). (Communications in Computer and Information Science; Vol. 2154). Springer. https://doi.org/10.1007/978-3-031-63797-1_10

@inproceedings{d6b442867d734fbe9b313ee2930c7503,

title = "Explainable emotion decoding for human and computer vision",

abstract = "Modern Machine Learning (ML) has significantly advanced various research fields, but the opaque nature of ML models hinders their adoption in several domains. Explainable AI (XAI) addresses this challenge by providing additional information to help users understand the internal decision-making process of ML models. In the field of neuroscience, enriching a ML model for brain decoding with attribution-based XAI techniques means being able to highlight which brain areas correlate with the task at hand, thus offering valuable insights to domain experts.",

author = "Alessio Borriero and Martina Milazzo and Matteo Diano and Davide Orsenigo and Villa, \{Maria Chiara\} and Chiara DiFazio and Marco Tamietto and Alan Perotti",

year = "2024",

doi = "10.1007/978-3-031-63797-1\_10",

language = "English",

isbn = "9783031637964",

volume = "2",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "178--201",

editor = "Luca Longo and Sebastian Lapuschkin and Christin Seifert",

booktitle = "Explainable artificial intelligence",

address = "Germany",

note = "Second World Conference : xAI 2024 ; Conference date: 17-07-2024 Through 19-07-2024",

}

Borriero, A, Milazzo, M, Diano, M, Orsenigo, D, Villa, MC, DiFazio, C, Tamietto, M & Perotti, A 2024, Explainable emotion decoding for human and computer vision. in L Longo, S Lapuschkin & C Seifert (eds), Explainable artificial intelligence. vol. 2, Communications in Computer and Information Science, vol. 2154, Springer, Cham, pp. 178-201, Second World Conference, Valetta, Malta, 17/07/24. https://doi.org/10.1007/978-3-031-63797-1_10

Explainable emotion decoding for human and computer vision. / Borriero, Alessio; Milazzo, Martina; Diano, Matteo et al.
Explainable artificial intelligence. ed. / Luca Longo; Sebastian Lapuschkin; Christin Seifert. Vol. 2 Cham: Springer, 2024. p. 178-201 (Communications in Computer and Information Science; Vol. 2154).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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T1 - Explainable emotion decoding for human and computer vision

AU - Borriero, Alessio

AU - Milazzo, Martina

AU - Diano, Matteo

AU - Orsenigo, Davide

AU - Villa, Maria Chiara

AU - DiFazio, Chiara

AU - Tamietto, Marco

AU - Perotti, Alan

N1 - Conference code: 2

PY - 2024

Y1 - 2024

N2 - Modern Machine Learning (ML) has significantly advanced various research fields, but the opaque nature of ML models hinders their adoption in several domains. Explainable AI (XAI) addresses this challenge by providing additional information to help users understand the internal decision-making process of ML models. In the field of neuroscience, enriching a ML model for brain decoding with attribution-based XAI techniques means being able to highlight which brain areas correlate with the task at hand, thus offering valuable insights to domain experts.

AB - Modern Machine Learning (ML) has significantly advanced various research fields, but the opaque nature of ML models hinders their adoption in several domains. Explainable AI (XAI) addresses this challenge by providing additional information to help users understand the internal decision-making process of ML models. In the field of neuroscience, enriching a ML model for brain decoding with attribution-based XAI techniques means being able to highlight which brain areas correlate with the task at hand, thus offering valuable insights to domain experts.

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DO - 10.1007/978-3-031-63797-1_10

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