Responsible Data Science: impartiality, accuracy, confidentiality and transparency of data
DOI:
https://doi.org/10.5433/1981-8920.2020v25n2p26Keywords:
Data science, Ethic, Big data, Responsible data scienceAbstract
Introduction: In the Big Data context, as an urgent need arises the application of individual and corporate rights and regulatory standards that safeguard privacy, impartiality, accuracy and transparency. In this scenario, Responsible Data Science emerges as an initiative based on the FACT guidelines, which correspond to the adoption of four principles: impartiality, accuracy, confidentiality and transparency.Objective: To address alternatives that can ensure the application of the FACT guidelines.
Methodology: An exploratory and descriptive research with a qualitative approach was developed. Searches were performed on the Web of Science, Scopus, and Scholar Google search engines using Responsible Data Science, Fairness, Accuracy, Confidentiality, Transparency Data Science, FACT, and FAT related to Data Science.
Results: Responsible Data Science emerges as an initiative based on the FACT guidelines, which correspond to the adoption of the principles: impartiality, accuracy, confidentiality and transparency. In implementing these guidelines, consideration should be given to the use of techniques and approaches being developed by Green Data Science.
Conclusions: It is concluded that Green Data Science and the FACT guidelines contribute significantly to safeguarding individual rights and that no measures need to be taken to prevent access and reuse of data. Challenges for implementing the FACT guidelines require studies, sine qua non conditions for tools for data analysis and dissemination to be developed at the design stage of methodologies.
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References
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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.
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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). Responsible Data Science: impartiality, accuracy, confidentiality and transparency of data. Informação & Informação, 25(2), 26–48. https://doi.org/10.5433/1981-8920.2020v25n2p26
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