Garantizar la imparcialidad, la exactitud, la confidencialidad y la transparencia de los datos en la perspectiva de la Ciencia de los Datos
DOI:
https://doi.org/10.5433/1981-8920.2020v25n2p26Palabras clave:
Ciencia de Datos, Ética, Big Data, Ciencia Responsable de DatosResumen
Introducción: en el contexto de Big Data, como una necesidad urgente surge la aplicación de los derechos individuales y corporativos y las normas reguladoras que salvaguardan la privacidad, imparcialidad, precisión y transparencia. En este escenario, Responsible Data Science surge como una iniciativa basada en las pautas de FACT, que corresponden a la adopción de cuatro principios: imparcialidad, precisión, confidencialidad y transparencia.Objetivo:abordar alternativas que puedan garantizar la aplicación de las pautas de FACT.
Metodología: se desarrolló una investigación exploratoria y descriptiva con un enfoque cualitativo. Las búsquedas se realizaron en los motores de búsqueda de Web of Science, Scopus y Scholar Google utilizando los términos "Ciencia de datos responsable", "Justicia, precisión, confidencialidad, transparencia + ciencia de datos", FACT y FAT relacionados con ciência de los datos.
Resultados: Responsible Data Science surge como una iniciativa basada en los lineamientos de FACT, que corresponden a la adopción de los principios: imparcialidad, precisión, confidencialidad y transparencia. Al implementar estas pautas, se debe considerar el uso de técnicas y enfoques desarrollados por Green Data Science. Conclusiones: Se concluye que Green Data Science y las pautas FACT contribuyen significativamente a salvaguardar los derechos individuales y que no es necesario tomar medidas para evitar el acceso y la reutilización de datos. Los desafíos para implementar las pautas FACT requieren estudios, condiciones sine qua non para desarrollar herramientas para el análisis y la difusión de datos en la etapa de diseño de las metodologias.
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VAN DER HOVEN, J. SoBigData ethics unpacking privacy designing for responsibility. Disponível em: https://slideplayer.com/slide/13113648/. Acesso em: 10 jan. 2019.
VEUGER, J. Attention to disruption and blockchain creates a viable real estate economy. J. Journal of US-China Public Administration, v. 14, n. 5, p. 263–285, 2017. Disponível em: https://davidpublisher.org/Public/uploads/Contribute/ 5a3c644925d78.pdf. Acesso em: 10 jan. 2019.
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WILKINSON, M. D.; DUMONTIER, M.; MONS, B. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, v. 3, n. 160018, 2016. Acesso em: 10 jan. 2019.
ANNANY, M.; CRAWFORD, K. Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability. New Media & Soc., v. 20, n. 3, p. 973–989, 2016.
ÁVILA, F. B. S.J. Pequena enciclopédia de moral e civismo. Rio de Janeiro: MEC, 1967.
BAUMAN, Z. Modernidade líquida. Rio de Janeiro: Zahar, 2001.
COLLINS dictionary, 2019. Disponível em: https://www.collinsdictionary.com/pt/dictionary/ english/accuracy. Acesso em: 2 mar. 2019.
DATA SCIENCE CENTER EINDHOVEN. Responsible Data Science. Ensuring fairness, accuracy, confidentiality & transparency by design, 2019. Disponível em: https://www.tue.nl/en/research/research-areas/data-science/responsibledata-science/. Acesso em: Acesso em: 10 jan. 2019
DE JONG, F. M. G.; MAEGAARD, B.; DE SMEDT, K.; FIŠER, D.; VAN UYTVANCK, D. CLARIN: towards FAIR and responsible data science using language resources. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, 2018, Miyazaki. Proceedings [...]. Miyazaki: LREC, 2018. p. 3259–3264. Disponível em: https://dspace.library.uu.nl/handle/1874/364776. Acesso em: 10 jan. 2019.
DE SMEDT, K.; DE JONG, F.; MAEGAARD, B.; FIŠER, D.; VAN UYTVANCK, D. Towards an open science infrastructure for the digital humanities: the case of CLARIN. CEUR-WS.org, v. 2084, p. 1–12, 2018. Disponível em: http://ceurws.org/Vol-2084/paper11.pdf. Acesso em: 10 jan. 2019.
EURONEWS. France fines Google €50 million using EU’s transparency and consent law. 2019. Disponível em: https://www.euronews.com/2019/01/21/france-fines-google-50-million-using-eus-transparency-and-consent-law. Acesso em: 10 Jan. 2019
EUROPEAN DATA SCIENCE ACADEMY. About EDSA. (2019). Disponível em: http://edsa-project.eu/overview/about-edsa/. Acesso em: 20 Jan. 2019.
FACEBOOK: Cambridge analytica data scandal. Wikipedia: The Free Encyclopedia. Apr. 26, 2019. Disponível em: https://en.wikipedia.org/wiki/Facebook% E2%80%93Cambridge_Analytica_data_scandal. Acesso em: 26 abr. 2019.
FISER, D.; LENARDIC, J.; ERJAVEC, T. CLARIN’s key resource families. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, 2018, Miyazaki. Proceedings [...]. Miyazaki, LREC, 2018. Disponível em: http://www.lrecconf.org/proceedings/lrec2018/pdf/829.pdf. Acesso em: 10 Jan. 2019.
GE 301 Group 7. Responsible Data Science. 2017. Disponível em: http://ge301.bilkent.edu.tr/fall2017group7/. Acesso em: 11 Jan. 2019.
GOLDIM, J. R. Confidencialidade (1997-2003). Disponível em: https://www.ufrgs.br/bioetica/confiden.htm. Acesso em: 15 Mar. 2019.
HAGGERTY, K.D.; ERICSON, R.V. The surveillant assemblage. Br. J. Sociol. v. 51, n. 4, p. 605–622, 2000.
HILBERT, M.; LÓPEZ, P. The world’s technological capacity to store, communicate, and compute information. Sciencexpress, p. 1-7, 2011. Disponível em: http://www.ris.org/uploadi/editor/13049382751297697294Science-2011-Hilbertscience.1200970.pdf. Acesso em; 18 mar. 2019.
KEMPER, J.; KOLKMAN, D. Transparent to whom? No algorithmic accountability without a critical audience. Inf. Commun. Soc., p. 1–16, 2018. Disponível em: https://doi.org/10.1080/1369118X.2018.1477967. Acesso em. 23 jan. 2019.
LEPRI, B.; OLIVER, N.; LETOUZÉ, E.; PENTLAND, A.; VINCK, P. Fair, transparent, and accountable algorithmic decision-making processes the premise, the proposed solutions, and the open challenges. Philosophy and Technology. v. 31, n. 4, p. 611-627, 2018. doi: 10.1007/s13347-017-0279-x.
LODDER, G. M. A.; SCHOLTER, R. H. J.; GOOSSENS, L.; ENGELS, R. C. M. E.; VERHAGEN, M. Loneliness and the social monitoring system: emotion recognition and eye gaze in a real-life conversation. Br. J. Psychol., v. 107, n. 1, p. 135-153, 2016. doi: https://doi.org/10.1111/bjop.12131
MOEREL, L. GDPR conundrums: the data protection officer requirement. 19 July 2016. Disponível em: https://research.tilburguniversity.edu/en/publications/gdpr-conundrums-the-data-protection-officer-requirement. Acesso em: 23 jan. 2019.
MOEREL, L.; PRINS, C. Privacy for the homo digitalis: proposal for a new regulatory framework for data protection in the light of Big Data and the Internet of Things. May 25, 2016. Acesso em: 10 jan. 2019. doi: http:??dx.doi.org/10.2139/ssrn.2784123.
OHM, P. Changing the rules: general principles for data use and analysis. In: LANE, J.; STODDEN, V.; BENDER, S.; NISSENBAUM, H. Privacy, big data, and the public good: frameworks for engagement. Cambridge: Cambridge University Press, 2014. v.1, p. 96-111.
PENNOCK, M. Digital curation: a life-cycle approach to managing and preserving usable digital information. Library & Archives Journal, n. 1. 2007. Disponível em: http://www.ukoln.ac.uk/ukoln/staff/m.pennock/publications/docs/libarch_curation.pdf. Acesso em: 12 May 2019.
PIERSMA, N. Data in urban environments. In: PIERSMA, N. Through the clouds: urban analytics for smart cities. Amsterdam: Hogeschool van Amsterdam, 2018. p. 11-21.
SATARIANO, A. Google is fined $57 million under Europe’s Data Privacy Law. New York Times. Disponível em: https://www.nytimes.com/2019/01/21/technology/google-europe-gdpr-fine.html. Acesso em: 21 mar. 2019.
SRIVASTAVA, D.; SCANNAPIECO, M.; REDMAN, T. C. Ensuring high-quality private data for responsible data science: vision and challenges. ACM Journal of Data and Information Quality (JDIQ). v. 11, n. 1, p. 1, 2019.
STOYANOVICH, J.; HOWE, B. Follow the data! Algorithmic transparency starts with data transparency. 2018. Disponível em: https://ai.shorensteincenter.org/ideas/2018/11/26/follow-the-data-algorithmictransparency-starts-with-data-transparency. 21 mar. 2019.
STOYANOVICH, J.; HOWE, B.; JAGADISH, H. V. Special Session: A technical research agenda in data ethics and responsible data management. In Proceedings of the 2018 International Conference on Management of Data, p. 1635–1636, 2018.
STOYANOVICH, J.; HOWE, B.; ABITEBOUL, S.; MIKLAU, G.; SAHUGUET, A.; WEIKUM, G. Fides: Towards a platform for responsible data science. In: SSDBM’17. 29th International Conference on Scientific and Statistical Database Management, Jun 2017, Chicago, United States. 10.1145/3085504.3085530. hal-01522418. Disponível em: https://hal.inria.fr/hal-01522418/document. Acesso em: 10 jan. 2019.
STOYANOVICH, J.; HOWE, B.; JAGADISH, H. V.; MIKLAU, G. Panel: a debate on data and algorithmic ethics. Proceedings of the VLDB Endowment, v. 11, n. 12, p. 2165–2167, 2018.
STOYANOVICH, J.; YANG, K.; JAGADISH, H. V. Online set selection with fairness and diversity constraints. In Proceedings of the 21st International Conference on Extending Database Technology (EDBT), March 26-29, 2018. Disponível em: https://openproceedings.org/2018/conf/edbt/paper-98.pdf. Acesso em: 10 jan. 2019.
TAYLOR, L. What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society. p. 1-14, July/Dec. 2017 Disponível em: https://doi.org/10.1177/2053951717736335. Acesso em: 10 jan. 2019.
TAYLOR, L. Data, visibility and justice. 2017. Disponível em: https://redasci.org/wp-content/uploads/2016/10/Linnet-Taylor-RDS-16.3.17.pdf. Acesso em: 10 jan. 2019.
TAYLOR, L.; BROEDERS, D. (2015). In the name of development: power, profit and the datafication of the global south. Geoforum, v. 64, p. 229–237, 2015.
VAN BE.RCHUM, M.; TRIPPEL, T. CLARIN data management activities in the PARTHENOS context. In CLARIN Annual Conference 2018. pp. 95-99, 2018. Disponível em: https://ris.utwente.nl/ws/portalfiles/portal/63914609/CE_2018_1292_CLARIN2018_ConferenceProceedings.pdf#page=102. Acesso em: 10 jan. 2019.
VAN DER AALST, W. M. Green data science: using big data in an" environmentally friendly" manner. In 18th International Conference on Enterprise Information Systems (ICEIS 2016). Apr. 25-28, 2016. Rome: SciTePress, 2016. p. 9-21. Disponível em: https://pdfs.semanticscholar.org/5889/68dd392ae93b1524aa7a491917d839bca050.pdf. Acesso em: 10 jan. 2019.
VAN DER AALST, W. M.; BECKER, J.; BICHLER, M.; BUHL, H. U.; DIBBERN, J.; FRANK, U.; HUI, K.-L. Views on the past, present, and future of business and information systems engineering. Business & Information Systems Engineering, v. 60, n. 6, p. 448–450, 2018. Disponível em: https://doi.org/10.1007/s12599-018-0561-1. Acesso em: 10 jan. 2019.
VAN DER AALST, W. M.; BICHLER, M.; HEINZL, A. Responsible data science. Business & Information Systems Engineering, v. 59, n. 5, p. 311–313, 2017. Disponível em: https://link.springer.com/article/10.1007%2Fs12599-017-0487-z. Acesso em: 10 jan. 2019.
VAN DER HOVEN, J. SoBigData ethics unpacking privacy designing for responsibility. Disponível em: https://slideplayer.com/slide/13113648/. Acesso em: 10 jan. 2019.
VEUGER, J. Attention to disruption and blockchain creates a viable real estate economy. J. Journal of US-China Public Administration, v. 14, n. 5, p. 263–285, 2017. Disponível em: https://davidpublisher.org/Public/uploads/Contribute/ 5a3c644925d78.pdf. Acesso em: 10 jan. 2019.
VEUGER, J. Trust in a viable real estate economy with disruption and blockchain. Facilities, v. 36, n. ½, p. 103–120, 2018. Disponível em: https://doi.org/10.1108/F-11-2017-0106. Acesso em: 10 jan. 2019.
WILKINSON, M. D.; DUMONTIER, M.; MONS, B. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, v. 3, n. 160018, 2016. Acesso em: 10 jan. 2019.
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2020-07-02
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Andrade, M. C., Gonçalez, P. R. V. A., Berti Junior, D. W., Baptista, A. A., & Coneglian, C. S. (2020). Garantizar la imparcialidad, la exactitud, la confidencialidad y la transparencia de los datos en la perspectiva de la Ciencia de los Datos. Informação & Informação, 25(2), 26–48. https://doi.org/10.5433/1981-8920.2020v25n2p26
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