Mostrar el registro sencillo del ítem
dc.contributor.author | Richter, Francisca | es_ES |
dc.contributor.author | Nelson, Emily | es_ES |
dc.contributor.author | Coury, Nicole | es_ES |
dc.contributor.author | Bruckman, Laura | es_ES |
dc.contributor.author | Knighton, Shanina | es_ES |
dc.date.accessioned | 2024-01-11T12:54:15Z | |
dc.date.available | 2024-01-11T12:54:15Z | |
dc.date.issued | 2023-09-22 | |
dc.identifier.isbn | 9788413960869 | |
dc.identifier.uri | http://hdl.handle.net/10251/201796 | |
dc.description.abstract | [EN] Building upon the FAIR principles of (meta)data (Findable, Accessible, Interoperable and Reusable) and drawing from research in the social, health, and data sciences, we propose a framework -FAIR2 (Frame, Articulate, Identify, Report) - for identifying and addressing discrimination bias in social data science. We illustrate how FAIR2 enriches data science with experiential knowledge, clarifies assumptions about discrimination with causal graphs and systematically analyzes sources of bias in the data, leading to a more ethical use of data and analytics for the public interest. FAIR2 can be applied in the classroom to prepare a new and diverse generation of data scientists. In this era of big data and advanced analytics, we argue that without an explicit framework to identify and address discrimination bias, data science will not realize its potential of advancing social justice. | es_ES |
dc.description.sponsorship | This work was generously funded by grant #015865 from the Public Interest Technology University Network - New America Foundation. | es_ES |
dc.format.extent | 9 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Editorial Universitat Politècnica de València | es_ES |
dc.relation.ispartof | 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023) | |
dc.rights | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | es_ES |
dc.subject | Discrimination Bias | es_ES |
dc.subject | Social Data Science Framework | es_ES |
dc.subject | Experiential Knowledge | es_ES |
dc.subject | Causal Diagrams | es_ES |
dc.title | FAIR2: A framework for addressing discrimination bias in social data science | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.identifier.doi | 10.4995/CARMA2023.2023.16400 | |
dc.relation.projectID | info:eu-repo/grantAgreement/PIT-UN//015865 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Richter, F.; Nelson, E.; Coury, N.; Bruckman, L.; Knighton, S. (2023). FAIR2: A framework for addressing discrimination bias in social data science. Editorial Universitat Politècnica de València. 327-335. https://doi.org/10.4995/CARMA2023.2023.16400 | es_ES |
dc.description.accrualMethod | OCS | es_ES |
dc.relation.conferencename | CARMA 2023 - 5th International Conference on Advanced Research Methods and Analytics | es_ES |
dc.relation.conferencedate | Junio 28-30, 2023 | es_ES |
dc.relation.conferenceplace | Sevilla, España | es_ES |
dc.relation.publisherversion | http://ocs.editorial.upv.es/index.php/CARMA/CARMA2023/paper/view/16400 | es_ES |
dc.description.upvformatpinicio | 327 | es_ES |
dc.description.upvformatpfin | 335 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | OCS\16400 | es_ES |
dc.contributor.funder | Public Interest Technology University Network | es_ES |