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dc.contributor.authorGroh, Matthew
dc.contributor.authorHarris, Caleb
dc.contributor.authorDaneshjou, Roxana
dc.contributor.authorBadri, Omar
dc.contributor.authorKoochek, Arash
dc.date.accessioned2023-01-25T13:12:56Z
dc.date.available2023-01-25T13:12:56Z
dc.date.issued2022-11-11
dc.identifier.issn2573-0142
dc.identifier.urihttps://hdl.handle.net/1721.1/147689
dc.publisherACMen_US
dc.relation.isversionofhttps://doi.org/10.1145/3555634en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACMen_US
dc.titleTowards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmen_US
dc.typeArticleen_US
dc.identifier.citationGroh, Matthew, Harris, Caleb, Daneshjou, Roxana, Badri, Omar and Koochek, Arash. 2022. "Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm." Proceedings of the ACM on Human-Computer Interaction.
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratory
dc.relation.journalProceedings of the ACM on Human-Computer Interactionen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2023-01-23T14:56:55Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2023-01-23T14:56:55Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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