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Assessing algorithmic biases for musical version identification

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dc.contributor.author Yesiler, Furkan
dc.contributor.author Serrà, Joan
dc.contributor.author Miron, Marius
dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
dc.date.accessioned 2023-03-01T13:48:45Z
dc.date.available 2023-03-01T13:48:45Z
dc.date.issued 2022
dc.identifier.citation Yesiler F, Miron M, Serrà J, Gómez E. Assessing algorithmic biases for musical version identification. In: WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining; 2022 Feb 21-25; online. New York: Association for Computing Machinery; 2022. p. 1284-90. DOI: 10.1145/3488560.3498397
dc.identifier.uri http://hdl.handle.net/10230/55987
dc.description Comunicació presentada a 15th ACM International Conference on Web Search and Data Mining (WSDM 2022), celebrat del 21 al 25 de febrer de 2022 de manera virtual.
dc.description.abstract Version identification (VI) systems now offer accurate and scalable solutions for detecting different renditions of a musical composition, allowing the use of these systems in industrial applications and throughout the wider music ecosystem. Such use can have an important impact on various stakeholders regarding recognition and financial benefits, including how royalties are circulated for digital rights management. In this work, we take a step toward acknowledging this impact and consider VI systems as socio-technical systems rather than isolated technologies. We propose a framework for quantifying performance disparities across 5 systems and 6 relevant side attributes: gender, popularity, country, language, year, and prevalence. We also consider 3 main stakeholders for this particular information retrieval use case: the performing artists of query tracks, those of reference (original) tracks, and the composers. By categorizing the recordings in our dataset using such attributes and stakeholders, we analyze whether the considered VI systems show any implicit biases. We find signs of disparities in identification performance for most of the groups we include in our analyses. We also find that learning- and rule-based systems behave differently for some attributes, which suggests an additional dimension to consider along with accuracy and scalability when evaluating VI systems. Lastly, we share our dataset to encourage VI researchers to take these aspects into account while building new systems.
dc.description.sponsorship This work is supported by the MIP-Frontiers project, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACM Association for Computer Machinery
dc.relation.ispartof WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining; 2022 Feb 21-25; online. New York: Association for Computing Machinery; 2022. p. 1284-90.
dc.relation.isreferencedby https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3488560.3498397&file=wsdmfp176_video.mp4
dc.rights © 2022 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.title Assessing algorithmic biases for musical version identification
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1145/3488560.3498397
dc.subject.keyword information retrieval
dc.subject.keyword version identification
dc.subject.keyword algorithmic bias
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765068
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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