Skip to main content
SpringerLink
Log in

Black-box artificial intelligence: an epistemological and critical analysis

  • Original Article
  • Published:
AI & SOCIETY Aims and scope Submit manuscript

Abstract

The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry out an epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bach S, Binder A, Montavon G, Klauschen F, Müller KR, Samek W (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One 10(7):1–46. https://doi.org/10.1371/journal.pone.0130140

    Article  Google Scholar 

  • Benítez A (2011) Fundamentos de inteligencia artificial. Libro segundo: Inteligencia artificial clásica. Escolar y Mayo, Madrid

    Google Scholar 

  • Benítez A (2013) Fundamentos de inteligencia artificial. Libro tercero: Inteligencia artificial bioinspirada. Escolar y Mayo, Madrid

    Google Scholar 

  • Bostrom N (2014) Superintelligence: paths, dangers, strategies. Oxford University Press, Oxford

    Google Scholar 

  • Brennan J (2016) Against democracy. Princeton University Press, Princeton

    Book  Google Scholar 

  • Bueno G (2006) Zapatero y el pensamiento Alicia: un presidente en el país de las maravillas. Temas de Hoy, Madrid

    Google Scholar 

  • Burrell J (2016) How the machine ‘thinks’: understanding opacity in machine learning algorithms. Big Data Soc. https://doi.org/10.1177/2053951715622512

    Article  Google Scholar 

  • Carabantes M (2016) Inteligencia artificial: una perspectiva filosófica. Escolar y Mayo, Madrid

    Google Scholar 

  • CFTC & SEC (Commodity Futures Trading Commission and Securities & Exchange Commission) (2010) Findings regarding the market events of May 6, 2010: report of the staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues. Washington, DC

  • Comte A (1989) Course de philosophie positive. Nathan, Paris

    Google Scholar 

  • Copeland J (1993) Artificial intelligence: a philosophical introduction. Blackwell, Oxford

    Google Scholar 

  • DARPA (Defense Advanced Research Projects Agency) (2016) Explainable Artificial Intelligence (XAI). DARPA-BAA-16-53. https://www.darpa.mil/attachments/DARPA-BAA-16-53.pdf

  • De Bruin B (2015) Ethics and the global financial crisis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • De Bruin B, Floridi L (2016) The ethics of cloud computing. Sci Eng Ethics 23(1):21–39. https://doi.org/10.1007/s11948-016-9759-0

    Article  Google Scholar 

  • Diakopoulos N (2013) Algorithmic accountability reporting: on the investigation of black boxes. Report, Tow Center for Digital Journalism, Columbia University

  • Douglas W (2008) Informal logic: a pragmatic approach. Cambridge University Press, Cambridge

    Google Scholar 

  • Dreyfus H (1992) What computers still can’t do. The MIT Press, Cambridge

    Google Scholar 

  • Ferrater J (2009) Diccionario de Filosofía. Ariel, Barcelona

    Google Scholar 

  • Fraunhofer (2017) Watching computers think. Fraunhofer Research News (blog). http://www.fraunhofer.de. Accessed 7 Dec 2018

  • Goodman B, Flaxman S (2016) European Union regulations on algorithmic decision-making and a "right to explanation". AI Mag 38(3):50–57. https://doi.org/10.1609/aimag.v38i3.2741

    Article  Google Scholar 

  • Haugeland J (1981) The nature and plausibility of cognitivism. In: Haugeland J (ed) Mind design. Cambridge University Press, Cambridge, pp 243–281

    Google Scholar 

  • Hawkins J, Blakeslee S (2005) On intelligence. Times Books, New York

    Google Scholar 

  • Horkheimer M (2004) Eclipse of reason. Continuum, London

    Google Scholar 

  • Horkheimer M, Adorno T (2002) Dialectic of enlightenment. Stanford University Press, Stanford

    Google Scholar 

  • Iacoboni M (2008) Mirroring people: the new science of how we connect with others. Farrar, Straus and Giroux, New York

    Google Scholar 

  • Kahneman D (2012) Thinking, fast and slow. Penguin Books, New York

    Google Scholar 

  • Krening S, Harrison B, Feigh K, Isbell C, Riedl M, Thomaz A (2016) Learning from explanations using sentiment and advice in RL. IEEE Trans Cogn Dev Syst 9(1):44–55. https://doi.org/10.1109/TCDS.2016.2628365

    Article  Google Scholar 

  • Lapuschkin S, Binder A, Montavon G, Müller KR, Samek W (2016) Analyzing classifiers: fisher vectors and deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2912–2920. https://doi.org/10.1109/CVPR.2016.318

  • Lipton Z (2017) The mythos of model interpretability. http://arxiv.org/abs/1606.03490v3. Accessed 26 Dec 2018

  • McCarthy J, Hayes P (1969) Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer B, Michie D (eds) Machine intelligence, vol 4. Edinburgh University Press, Edinburgh

    MATH  Google Scholar 

  • Montavon G, Samek W, Müller KR (2017) Methods for interpreting and understanding deep neural Networks. Digit Signal Process 73:1–15. https://doi.org/10.1016/j.dsp.2017.10.011

    Article  MathSciNet  Google Scholar 

  • Nguyen A, Dosovitskiy A, Yosinski J, Brox T, Clune J (2016) Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. http://arxiv.org/abs/1605.09304v5. Accessed 10 Dec 2018

  • Nilsson N (2009) The quest for artificial intelligence: a history of ideas and achievements. Cambridge University Press, New York

    Book  Google Scholar 

  • Ortega y Gasset J (2009) Introducción al curso ¿Qué es la técnica? In: Obras completas, vol IX. Taurus Ediciones, Madrid, pp 27–31

    Google Scholar 

  • Pasquale F (2015) The black box society: the secret algorithms that control money and information. Harvard University Press, Cambridge

    Book  Google Scholar 

  • Ribeiro M, Singh S, Guestrin C (2016) Why should I trust you?: explaining the predictions of any classifier. http://arxiv.org/abs/1602.04938v3. Accessed 27 Dec 2018

  • Robinson D, Yu H, Rieke A (2014) Civil rights, big data and our algorithmic future. Social Justice and Technology. https://centerformediajustice.org/wp-content/uploads/2014/10/Civil-Rights_Big-Data_Our-Future.pdf.   Accessed 4 Jan 2019

  • Rumelhart D (1997) The architecture of mind: a connectionist approach. In: Haugeland J (ed) Mind design II. Cambridge University Press, Cambridge, pp 205–232

    Google Scholar 

  • Rumelhart D, McClelland J, The PDP Research Group (1989) Parallel distributed processing, vol I. The MIT Press, Cambridge

    Google Scholar 

  • Russell S, Norvig P (2016) Artificial intelligence: a modern approach. Global Edition, 3rd edn. Pearson Education, London

    MATH  Google Scholar 

  • Samek W, Wiegand T, Müller KR (2017) Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. http://arxiv.org/abs/1708.08296v1. Accessed 26 Dec 2018

  • Sandvig C, Hamilton K, Karahalios L, Langbort C (2014) Auditing algorithms: research methods for detecting discrimination on internet platforms. In: Annual Meeting of the International Communication Association, Seattle, WA, pp 1–23

  • Simonyan K, Vedaldi A, Zisserman A (2014) Deep inside convolutional networks: visualizing image classification models and saliency maps. http://arxiv.org/abs/1312.6034v2. Accessed 10 Dec 2018

  • Somin I (2013) Democracy and political ignorance: why smaller government is smarter. Stanford University Press, Stanford

    Google Scholar 

  • Weizenbaum J (1976) Computer power and human reason: from judgement to calculation. W. H. Freeman & Company, San Francisco

    Google Scholar 

  • Zeiler M, Fergus R (2013) Visualizing and understanding convolutional networks. http://arxiv.org/abs/1311.2901v3. Accessed 6 Dec 2018

Download references

Author information

Authors and Affiliations

  1. Faculty of Philosophy, Universidad Complutense de Madrid (Complutense University of Madrid), c/ Profesor Aranguren 5, 28040, Madrid, Spain

    Manuel Carabantes

Authors
  1. Manuel Carabantes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Carabantes.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carabantes, M. Black-box artificial intelligence: an epistemological and critical analysis. AI & Soc 35, 309–317 (2020). https://doi.org/10.1007/s00146-019-00888-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00146-019-00888-w

Keywords

Search

Navigation