Predicting the pasta philosophical critique of predictive analytics

  1. Daniel Innerarity 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Revista:
IDP: revista de Internet, derecho y política = revista d'Internet, dret i política

ISSN: 1699-8154

Año de publicación: 2023

Título del ejemplar: "Digitalització i algoritmització de la justícia"

Número: 39

Tipo: Artículo

DOI: 10.7238/IDP.V0I39.409672 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IDP: revista de Internet, derecho y política = revista d'Internet, dret i política

Objetivos de desarrollo sostenible

Resumen

If we address this topic from a conceptual and critical point of view, we need to address three issues: 1)why predictions are too often right, 2) why, at the same time, they are so often mistaken, and 3) what consequences arise from the fact that our instruments for prediction ignore at least four realities that must be true about future forecasts or at least be conscious of their limits: a) that individuals cannot be fully subsumed into categories, b) that their future behaviour tends to have unpredictable dimensions, c) that propensity is not the same as causality and d) that democratic societies must make the desire to anticipate the future compatible with respect for the open nature of the future.

Referencias bibliográficas

  • ABEBE, R.; KASY, M. (2021). “The means of prediction”. In: Acemoglu, D. Redesigning AI. Work, democracy, and justice in the age of automation, pp. 87-91. Cambridge: Boston Review.
  • ACCOTO, C. (2019). Il mondo ex machina. Cinque brevi lezioni di filosofia dell’automazion. Milan: Egea.
  • ADAMS, V.; MURPHY, M.; CLARKE, A. (2009). “Anticipation: Technoscience, life, affect, temporality”. Subjectivity, vol. 28, n.º 1, pp. 246-265. DOI: https://doi.org/10.1057/sub.2009.18
  • AGRAWAL, A.; GANS, J.; Goldfarb, A. (2018). Prediction Machines. The Simple Economics of Artificial Intelligence. Cambridge: Harvard University Press.
  • AMOORE, L.; PIOTUKH, V. (2015). “Life beyond big data: governing with little analytics”. Economy and Society, vol. 44, n.º 3, pp. 314-366. DOI: https://doi.org/10.1080/03085147.2015.1043793
  • ANDREJEVIC, M. (2013). Infoglut: How Too Much Information Is Changing the Way We Think and Know. New York: Routledge. DOI: https://doi.org/10.4324/9780203075319
  • ANGWIN, J.; LARSON, J. (2016, December). “Bias in Criminal Risk Scores is Mathematically Inevitable, Researches Say”. ProPublica [online]. Available at: https://www.propublica.org/article/bias-in-criminal-risk-scores-is-mathematical-inevitable-researches-say
  • ARENDT, H. (2017). Mensch und Politik. Stuttgart: Reclam.
  • BOELLSTORF, T. (2013). “Making big data, in theory”. First Monday, vol. 18, no. 10. DOI: https://doi.org/10.5210/fm.v18i10.4869
  • BRAMAN, S. (2009). Change of State: Information, Policy and Power. Cambridge: The MIT Press.
  • BRAYNE, S. (2020). Predict and Surveil: Data, Discretion, and the Future of Policing. Oxford University Press. DOI: https://doi.org/10.1093/oso/9780190684099.001.0001
  • BROUSSARD, M. (2018). Artificial Unintelligence: How Computers Misunderstand the World. Cambridge: The MIT Press. DOI: https://doi.org/10.7551/mitpress/11022.001.0001
  • DERRIDA, J. (1994). “Nietzsche and the Machine”. Journal of Nietzsche Studies, no. 7, pp. 7-65.
  • ESPOSITO, E. (2011). The Future of Futures: The Time of Money in Financing and Society. Edward Elgar. DOI: https://doi.org/10.4337/9781849809115
  • ESPOSITO, E. (2021). Artificial Communication: How Algorithms Produce Social Intelligence. Cambridge: The MIT Press. DOI: https://doi.org/10.7551/mitpress/14189.001.0001
  • EUROPEAN COMMISSION (EC) (2015). Evidence-Based Better Regulation. European Commission [online]. Available at: https://commission.europa.eu/law/law-making-process/planning-and-proposing-law/better-regulation/better-regulation-guidelines-and-toolbox_en
  • FEDERAL TRADE COMMISSION (2016, January). Big Data. A tool of Inclusion or Exclusion? Understanding the issues. United Sates of America: Federal Trade Commission [online]. Available at: https://www.ftc.gov/system/files/documents/reports/big-data-tool-inclusion-or-exclusion-understanding-issues/160106big-data-rpt.pdf
  • FOERSTER, H. Von (2003). Understanding Understanding: Essays on Cybernetics and Cognition. New York: Springer. DOI: https://doi.org/10.1007/b97451
  • HILDEBRANDT, M. (2006). “Privacy and identity”. In: Claes, E., Duff, A., Gurtwith, S. (eds.). Privacy and the Criminal Law. Antwerpen/Oxford: Intersentia, pp. 43-57. DOI: https://doi.org/10.1007/s11572-006-9006-x
  • KAPOOR, S.; NARAYANAN, A. (2022). “Leakage and the Reproducibility Crisis in ML-based Science”. Patterns, vol. 4, no. 9. DOI: https://doi.org/10.1016/j.patter.2023.100804
  • MACKENZIE, A. (2015). “The production of prediction: What does machine learning want?”. European Journal of Cultural Studies, vol. 18, no. 4-5, pp. 429-445. DOI: https://doi.org/10.1177/1367549415577384
  • MASSUMI, B. (2007). “Potential politics and the primacy of preemption”. Theory & Event, vol. 10, no. 2. DOI: https://doi.org/10.1353/tae.2007.0066
  • MATZNER, T. (2018). “Grasping the ethics and politics of algorithms”. In: Sætnan, A. R., Schneider, I., Green, N. (2018). The Politics of Big Data. Big Data, Big Brother, pp. 30-45. Oxford, New York: Routledge.
  • MAYER-SCHOENBERGER, V.; CUKIER, K. (2013). Big Data. A Revolution That Will Transform How We Live, Work, and Think. New York: Houghton.
  • MERTON, R. (1948). “The self-fulfilling prophecy”. The Antioch Review, vol. 8, no. 2, pp. 193-210. DOI: https://doi.org/10.2307/4609267
  • NOWOTNY, H. (2021). In AI we trust. Power, illusion and control of predictive algorithms. Cambridge: Polity Press.
  • PORTER, T. M. (1995). Trust in Numbers. The Pursuit of Objectivity in Science and Public Life. Princeton University Press. DOI: https://doi.org/10.1515/9780691210544
  • SCHNEIDER, I. (2018). “Bringing the state back in. Big Data-based capitalism, disruption, and novel regulatory approaches in Europe”. In: Sætnan, A. R., Schneider, I., Green, N. The Politics of Big Data. Big Data, Big Brother, pp. 129-175. Oxford, New York: Routledge.
  • STRAUSS, S. (2015). “Datafication and the Seductive Power of Uncertainty-A Critical Exploration of Big Data”. Information, no. 6, pp. 836-847. DOI: https://doi.org/10.3390/info6040836
  • TYLER, I. (2015). “Classificatory Struggles: Class, Culture and Inequality in Neoliberal Times”. The Sociological Review, vol. 63, no. 2, pp. 493-511. DOI: https://doi.org/10.1111/1467-954X.12296
Fuente de los datos: Dialnet