Hacia una privacidad colectivarepensar las bases teóricas de la distinción público/privado en la economía de la vigilancia

  1. Fernández Barbudo, Carlos 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Journal:
Teknokultura: Revista de Cultura Digital y Movimientos Sociales
  1. Brändle, Gaspar (coord.)
  2. Latorre Catalán, Marta (coord.)

ISSN: 1549-2230

Year of publication: 2020

Issue Title: Alternative models of consumption in the digital age

Volume: 17

Issue: 1

Pages: 69-76

Type: Article

DOI: 10.5209/TEKN.66844 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Teknokultura: Revista de Cultura Digital y Movimientos Sociales

Abstract

The defense of privacy has historically been based on the protection of the autonomy and dignity of individuals. However, the recent development of the surveillance economy has multiplied the types of monitoring technologies and observation targets available. This situation shows the limits of the political, legal and social mechanisms of the liberal societies to protect the privacy, and forces us to rethink these threats from a new perspective. To this end, the aim of this paper is to defend the necessity to develop a collective dimension of privacy, not only focused on individuals, as a mechanism to understand and interrelate the set of socio-technical changes that threaten the dignity, political autonomy and digital sovereignty of human groups.

Bibliographic References

  • Abellán, J. (2012). Diferenciación conceptual entre Estado y sociedad. En Política (pp. 233–238). Madrid: Alianza Editorial.
  • Allen, A. L. (1988). Uneasy Access: Privacy for Women in a Free Society. Totowa, N.J: Rowman & Littlefield.
  • Babuta, A. (2018). Innocent Until Predicted Guilty? Artificial Intelligence and Police Decision-Making. Royal United Services Institute, 38(2), 1–4.
  • Barocas, S. (2012). The price of precision: voter microtargeting and its potential harms to the democratic process. En PLEAD '12 Proceedings of the first edition workshop on Politics, elections and data (pp. 31–36). New York: ACM.
  • Bendiek, A. y Schulze, M. (2019). Disinformation and Elections to the European Parliament. SWP Comments k. Recuperado de https://www.swp-berlin.org/10.18449/2019C16/
  • Bobbio, N. (1998). La gran dicotomía: público/privado. En Estado, Gobierno y Sociedad (pp. 11–38). México: Fondo de Cultura Económica.
  • Bradshaw, S.y Howard, P. N. (2019). The Global Disinformation Order. 2019 Global Inventory of Organised Social Media Manipulation. Oxford: Oxford Internet Institute.
  • Brunner, O. (1977). La "casa grande" y la “oeconomica” de la Vieja Europa. En Nuevos caminos de la historia social y constitucional (pp. 87–123). Buenos Aires: Alfa (Original publicado en 1968).
  • Canadian Security Intelligence Service (2018). Who said what? The Security Challenges of Modern Disinformation (No. 2018-02-01). Ottawa: World Watch Expert Notes.
  • Christl, W. (2017). Corporate Surveillance in Everyday Life. Vienna: Cracked Lab – Institute for Critical Digital Culture. Recuperado de http://crackedlabs.org
  • Cui, G. (2016). Evidence-Based Sentencing and the Taint of Dangerousness. The Yale Law Journal, 125, 315–322.
  • Cyphers, B. y Gebhart, G. (2019). Behind the One-Way Mirror: A Deep Dive Into the Technology of Corporate Surveillance. San Francisco: Electronic Frontier Foundation. Recuperado de https://www.eff.org/wp/behind-the-one-way-mirror
  • Dahlberg, L. y Siapera, E. (2007). Introduction: Tracing Radical Democracy and the Internet. En L. Dahlberg & E. Siapera (Eds.), Radical democracy and the Internet. Interrogating theory and practice (pp. 1–16). New York: Palgrave Macmillan.
  • Díaz Rojo, J. A. (2002). Privacidad: ¿neologismo o barbarismo? Espéculo, (21). Recuperado de https://webs.ucm.es/info/especulo/numero21/privaci.html
  • Fass, T. L., Heilbrun, K., DeMatteo, D. y Fretz, R. (2008). The LSI-R and the Compas. Criminal Justice and Behavior, 35(9), 1095–1108.
  • Fernández Barbudo, C. (2019). El nuevo concepto de privacidad: la transformación estructural de la visibilidad. Revista De Estudios Políticos, 185, 139–167.
  • Ferraris, V., Bosco, F., Cafiero, G., D'Angelo, E. y Suloyeva, Y. (2013). Defining Profiling. PROFILING. Fundamental Rights and Citizenship Programme of the European Union. Recuperado de www.proling-project.eu
  • Goldfarb, A. y Tucker, C. E. (2011). Online advertising, behavioral targeting, and privacy. Communications of the ACM, 54(5), 25–27.
  • Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., et al. (2019). Identifying facial phenotypes of genetic disorders using deep learning. Nature Medicine, 25(1), 60–64.
  • Hardyns, W. y Rummens, A. (2017). Predictive Policing as a New Tool for Law Enforcement? Recent Developments and Challenges. European Journal on Criminal Policy and Research, 24(3), 1–18.
  • IEEE. (2015). Internet of Things (IoT) Ecosystem Study. Piscataway: IEEE. Recuperado de http://standards.ieee.org/innovate/iot/study.html
  • Inness, J. C. (1992). Privacy, intimacy, and isolation. New York: Oxford University Press.
  • Kleinberg, J., Ludwig, J., Mullainathan, S.y Sunstein, C. R. (2019). Discrimination in the Age of Algorithms. SSRN Electronic Journal. Recuperado de https://ssrn.com/abstract=3329669
  • Koselleck, R. (2004). Historia de los conceptos y conceptos de historia. Ayer, 1(53), 27–45.
  • Koselleck, R. (2007). Crítica y Crisis. Madrid: Trotta, Universidad Autónoma de Madrid (Original publicado en 1959).
  • Koselleck, R.y Gadamer, H.-G. (1997). Historia y hermenéutica. Barcelona: Paidós I.C.E./U.A.B.
  • Marsland, S. (2015). Machine Learning. An Algorithmic Perspective. Boca Raton: Champan & Hall.
  • Mill, J. S. (2017). Sobre la libertad. Madrid: Biblioteca Nueva (Original publicado en 1859).
  • Moghaddam, H. M., Acar, G., Ben Burgess, Mathur, A., Huang, D. Y., Feamster, N., et al. (2019). Watching You Watch: The Tracking Ecosystem of Over-the-Top TV Streaming Devices. ACM SIGSAC Conference on Computer and Communications Security, Londres.
  • Ren, J., Dubois, D. J., Choffnes, D., Mandalari, A. M., Kolcun, R.y Haddadi, H. (2019). Information Exposure From Consumer IoT Devices: A Multidimensional, Network-Informed Measurement Approach. IMC '19, Amsterdam.
  • Ricaurte, P. (2019). Data Epistemologies, Coloniality of Power, and Resistance:. Television & New Media, 20(4), 350-365.
  • Richardson, M. (2017). The Right to Privacy. Origins and Influence of a Nineteenth-Century Idea. Cambridge: Cambridge University Press.
  • Sánchez, S. A. (2018). La esfera pública en la era de la hipermediación algorítmica: noticias falsas, desinformación y la mercantilización de la conducta. Hipertext.Net: Revista Académica Sobre Documentación Digital Y Comunicación Interactiva, 17, 74–82.
  • Sanje, G.y Senol, I. (2012). The Importance of Online Behavioral Advertising for Online Retailers. International Journal of Business and Social Science, 3(18), 114-121.
  • Sarigol, E., Garcia, D.y Schweitzer, F. (2014). Online privacy as a collective phenomenon. En Proceedings of the second ACM conference on Online social networks (pp. 95–106). New York: ACM Press.
  • Skeem, J. L.y Lowenkamp, C. T. (2016). Risk, Race, And Recidivism: Predictive Bias And Disparate Impact. Criminology, 54(4), 680–712.
  • Smartt, U. (2014). Media and Entertainment Law. London: Routledge.
  • Suresh, H.y Guttag, J. V. (2019). A Framework for Understanding Unintended Consequences of Machine Learning. arXiv. Recuperado de https://arxiv.org/abs/1901.10002v1
  • Tambini, D., Labo, S., Goodman, E.y Moore, M. (2017). The new political campaigning. Media Policy Brief. London: Media Policy Project, London School of Economics and Political Science.
  • Tayebi, M. A.y Glasser, U. (2016). Social Network Analysis in Predictive Policing. Concepts, Models and Methods. Génova: Springer.
  • Taylor, L., Floridi, L.y van der Sloot, B. (Eds.). (2017). Group Privacy. Cham: Springer International Publishing.
  • Toscano, M. (2017). Sobre el concepto de privacidad: la relación entre privacidad e intimidad. Isegoría, 57, 533–20.
  • Wang, Y.y Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology, 114(2), 246–257.
  • Whitman, J. Q. (2004). The Two Western Cultures of Privacy: Dignity versus Liberty. The Yale Law Journal, 113(6), 1151–1222.
  • Wu, X.y Zhang, X. (2016). Automated Inference on Criminality using Face Images. arXiv. Recuperado de https://arxiv.org/abs/1611.04135v2