Empreu aquest identificador per citar o enllaçar aquest ítem:
http://hdl.handle.net/10609/150455
Registre complet de metadades
Camp DC | Valor | Llengua/Idioma |
---|---|---|
dc.contributor.author | González Zelaya, Carlos Vladimiro | - |
dc.contributor.author | Salas Piñón, Julián | - |
dc.contributor.author | Megias, David | - |
dc.contributor.author | Missier, Paolo | - |
dc.date.accessioned | 2024-06-19T10:56:33Z | - |
dc.date.available | 2024-06-19T10:56:33Z | - |
dc.date.issued | 2023-12-09 | - |
dc.identifier.citation | González-Zelaya, V. [Vladimiro], Salas-Piñón, J. [Julián], Megías, D. [David] & Missier, P. [Paolo]. (2023). Fair and Private Data Preprocessing through Microaggregation. ACM Transactions on Knowledge Discovery from Data, 18(3), 1-24. doi: 10.1145/3617377 | - |
dc.identifier.issn | 1556-4681MIAR | - |
dc.identifier.uri | http://hdl.handle.net/10609/150455 | - |
dc.description.abstract | Privacy protection for personal data and fairness in automated decisions are fundamental requirements for responsible Machine Learning. Both may be enforced through data preprocessing and share a common target: data should remain useful for a task, while becoming uninformative of the sensitive information. The intrinsic connection between privacy and fairness implies that modifications performed to guarantee one of these goals, may have an effect on the other, e.g., hiding a sensitive attribute from a classification algorithm might prevent a biased decision rule having such attribute as a criterion. This work resides at the intersection of algorithmic fairness and privacy. We show how the two goals are compatible, and may be simultaneously achieved, with a small loss in predictive performance. Our results are competitive with both state-of-the-art fairness correcting algorithms and hybrid privacy-fairness methods. Experiments were performed on three widely used benchmark datasets: Adult Income, COMPAS, and German Credit. | en |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | en |
dc.publisher | Association for Computing Machinery (ACM) | - |
dc.relation.ispartof | ACM Transactions on Knowledge Discovery from Data, 2023, 18 (3) | - |
dc.relation.isreferencedby | https://archive.ics.uci.edu/ml/datasets/adult | - |
dc.relation.isreferencedby | https://github.com/propublica/compas-analysis | - |
dc.relation.isreferencedby | https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data) | - |
dc.relation.uri | https://doi.org/10.1145/3617377 | - |
dc.rights | CC BY | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | fair classification | en |
dc.subject | ethical AI | en |
dc.subject | algorithmic fairness | en |
dc.subject | privacy preserving data mining | en |
dc.subject | responsible machine learning | en |
dc.title | Fair and private data preprocessing through microaggregation | en |
dc.type | info:eu-repo/semantics/article | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.doi | https://doi.org/10.1145/3617377 | - |
dc.gir.id | AR/0000011255 | - |
dc.type.version | info:eu-repo/semantics/publishedVersion | - |
Apareix a les col·leccions: | Articles cientÍfics Articles |
Arxius per aquest ítem:
Arxiu | Descripció | Mida | Format | |
---|---|---|---|---|
gonzalez_ACM_Fair.pdf | 1,48 MB | Adobe PDF | ![]() Veure/Obrir |
Comparteix:
![]( /image/googleScholar.png)
![](/image/microsoftAcademic.png)
Aquest ítem està subjecte a una llicència de Creative CommonsLlicència Creative Commons