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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2020: ECML PKDD 2020 Workshops pp 92–107Cite as

A Left Realist Critique of the Political Value of Adopting Machine Learning Systems in Criminal Justice

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1323))

Abstract

In this paper we discuss the political value of the decision to adopt machine learning in the field of criminal justice. While a lively discussion in the community focuses on the issue of the social fairness of machine learning systems, we suggest that another relevant aspect of this debate concerns the political implications of the decision of using machine learning systems. Relying on the theory of Left realism, we argue that, from several points of view, modern supervised learning systems, broadly defined as functional learned systems for decision making, fit into an approach to crime that is close to the law and order stance. Far from offering a political judgment of value, the aim of the paper is to raise awareness about the potential implicit, and often overlooked, political assumptions and political values that may be undergirding a decision that is apparently purely technical.

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Notes

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    [25], p. 265.

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    [25], p. 74.

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References

  1. Andrews, D.A., Bonta, J., Wormith, J.S.: The recent past and near future of risk and/or need assessment. Crime Delinq. 52(1), 7–27 (2006)

    Article  Google Scholar 

  2. Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias. ProPublica, 23 May 2016

    Google Scholar 

  3. Barabas, C., Dinakar, K., Virza, J.I., Zittrain, J., et al.: Interventions over predictions: reframing the ethical debate for actuarial risk assessment. arXiv preprint arXiv:1712.08238 (2017)

  4. Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, Cambridge (2012)

    MATH  Google Scholar 

  5. Berk, R.: Criminal Justice Forecasts of Risk: A Machine Learning Approach. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-3085-8

    Book  Google Scholar 

  6. Berk, R., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: the state of the art. arXiv preprint arXiv:1703.09207 (2017)

  7. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

  8. Brennan, T., Dieterich, W., Ehret, B.: Evaluating the predictive validity of the COMPAS risk and needs assessment system. Crim. Justice Behav. 36(1), 21–40 (2009)

    Article  Google Scholar 

  9. Brennan, T., Oliver, W.L.: The emergence of machine learning techniques in criminology: implications of complexity in our data and in research questions. Criminol. Public Policy 12(3), 551–562 (2013)

    Article  Google Scholar 

  10. Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)

    Article  Google Scholar 

  11. Citron, D.K., Pasquale, F.: The scored society: due process for automated predictions. Wash. L. Rev. 89, 1 (2014)

    Google Scholar 

  12. Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., Huq, A.: Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 797–806. ACM (2017)

    Google Scholar 

  13. Courtland, R.: Bias detectives: the researchers striving to make algorithms fair. Nature 558(7710), 357–357 (2018)

    Article  Google Scholar 

  14. Darwiche, A.: Human-level intelligence or animal-like abilities? arXiv preprint arXiv:1707.04327 (2017)

  15. DeKeseredy, W.S., Donnermeyer, J.F.: Contemporary issues in left realism. Int. J. Crime Justice Soc. Democracy 5(3), 12–26 (2016)

    Article  Google Scholar 

  16. Deng, L.: A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Process. 3 (2014)

    Google Scholar 

  17. Ensign, D., Friedler, S.A., Neville, S., Scheidegger, C., Venkatasubramanian, S.: Runaway feedback loops in predictive policing. arXiv preprint arXiv:1706.09847 (2017)

  18. Floridi, L.: The Philosophy of Information. OUP Oxford (2013). https://books.google.it/books?id=l8RoAgAAQBAJ

  19. Floridi, L.: Infraethics-on the conditions of possibility of morality. Philos. Technol. 30(4), 391–394 (2017)

    Article  Google Scholar 

  20. Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 29, 82–97 (2012)

    Article  Google Scholar 

  21. Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., Mullainathan, S.: Human decisions and machine predictions. Q. J. Econ. 133(1), 237–293 (2017)

    MATH  Google Scholar 

  22. Kleinberg, J., Mullainathan, S., Raghavan, M.: Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 (2016)

  23. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  24. Lea, J.: Left realism: a radical criminology for the current crisis. Int. J. Crime Justice Soc. Democracy 5(3), 53–65 (2016)

    Article  Google Scholar 

  25. Lea, J., Young, J., et al.: What is to be done about law and order? (1984)

    Google Scholar 

  26. Lipton, Z.C.: The mythos of model interpretability. arXiv preprint arXiv:1606.03490 (2016)

  27. Liu, L.T., Dean, S., Rolf, E., Simchowitz, M., Hardt, M.: Delayed impact of fair machine learning. arXiv preprint arXiv:1803.04383 (2018)

  28. MacKay, D.J.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  29. Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B.: Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592 (2019)

  30. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)

    Article  Google Scholar 

  31. Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  32. Peters, J., Janzing, D., Schölkopf, B.: Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, Cambridge (2017)

    MATH  Google Scholar 

  33. Richardson, R., Schultz, J., Crawford, K.: Dirty data, bad predictions: how civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review Online (2019, forthcoming)

    Google Scholar 

  34. Wick, M., Tristan, J.B., et al.: Unlocking fairness: a trade-off revisited. In: Advances in Neural Information Processing Systems, pp. 8780–8789 (2019)

    Google Scholar 

  35. Wu, Y., Zhang, L., Wu, X., Tong, H.: PC-fairness: a unified framework for measuring causality-based fairness. In: Advances in Neural Information Processing Systems, pp. 3399–3409 (2019)

    Google Scholar 

  36. Zliobaite, I.: A survey on measuring indirect discrimination in machine learning. arXiv preprint arXiv:1511.00148 (2015)

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Author information

Authors and Affiliations

  1. Department of Informatics, University of Oslo, 0316, Oslo, Norway

    Fabio Massimo Zennaro

Authors
  1. Fabio Massimo Zennaro
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Correspondence to Fabio Massimo Zennaro .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  2. Monash University, Clayton, VIC, Australia

    Michael Kamp

  3. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  4. University of Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  5. University of Guelph, Guelph, ON, Canada

    Luiza Antonie

  6. University of Caen Normandy, Caen, France

    Albrecht Zimmermann

  7. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  8. Norwegian University of Science and Technology, Trondheim, Norway

    Özlem Özgöbek

  9. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  10. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  11. University of Porto, Porto, Portugal

    João Gama

  12. Fraunhofer IAIS, St. Augustin, Germany

    Linara Adilova

  13. Royal Holloway University of London, Egham, UK

    Yamuna Krishnamurthy

  14. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  15. University of Bari Aldo Moro, Bari, Italy

    Donato Malerba

  16. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  17. University of Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  18. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  19. University of Naples Federico II, Naples, Italy

    Elio Masciari

  20. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Ras

  21. Australian National University, Canberra, ACT, Australia

    Peter Christen

  22. Leibniz University Hannover, Hannover, Germany

    Eirini Ntoutsi

  23. Technical University of Dortmund, Dortmund, Germany

    Erich Schubert

  24. University of Southern Denmark, Odense, Denmark

    Arthur Zimek

  25. University of Pisa, Pisa, Italy

    Anna Monreale

  26. Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Biecek

  27. ISTI-CNR, PISA, Italy

    Salvatore Rinzivillo

  28. Berlin Institute of Technology, Berlin, Germany

    Benjamin Kille

  29. Berlin Institute of Technology, Berlin, Germany

    Andreas Lommatzsch

  30. Norwegian University of Science and Technology, Trondheim, Norway

    Jon Atle Gulla

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Zennaro, F.M. (2020). A Left Realist Critique of the Political Value of Adopting Machine Learning Systems in Criminal Justice. In: Koprinska, I., et al. ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-65965-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65964-6

  • Online ISBN: 978-3-030-65965-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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