CryptoCredit: Securely Training Fair Models

dc.contributor.author	Castro, Leo
dc.contributor.author	Chen, Jiahao
dc.contributor.author	Polychroniadou, Antigoni
dc.date.accessioned	2022-11-03T15:38:26Z
dc.date.available	2022-11-03T15:38:26Z
dc.date.issued	2020-10-15
dc.identifier.isbn	978-1-4503-7584-9
dc.identifier.uri	https://hdl.handle.net/1721.1/146106
dc.publisher	ACM\|ACM International Conference on AI in Finance	en_US
dc.relation.isversionof	https://doi.org/10.1145/3383455.3422567	en_US
dc.rights	Creative Commons Attribution 4.0 International license	en_US
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	en_US
dc.source	ACM\|ACM International Conference on AI in Finance	en_US
dc.title	CryptoCredit: Securely Training Fair Models	en_US
dc.type	Article	en_US
dc.identifier.citation	Castro, Leo, Chen, Jiahao and Polychroniadou, Antigoni. 2020. "CryptoCredit: Securely Training Fair Models."
dc.contributor.department	Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.identifier.mitlicense	PUBLISHER_CC
dc.eprint.version	Final published version	en_US
dc.type.uri	http://purl.org/eprint/type/ConferencePaper	en_US
eprint.status	http://purl.org/eprint/status/NonPeerReviewed	en_US
dc.date.updated	2022-11-02T22:07:02Z
dc.language.rfc3066	en
dc.rights.holder	The author(s)
dspace.date.submission	2022-11-02T22:07:03Z
mit.license	PUBLISHER_CC
mit.metadata.status	Authority Work and Publication Information Needed	en_US