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The uselessness of AI ethics

Open access
Munn, Luke·2023·AI and Ethics

Abstract

As the awareness of AI’s power and danger has risen, the dominant response has been a turn to ethical principles. A flood of AI guidelines and codes of ethics have been released in both the public and private sector in the last several years. However, these are meaningless principles which are contested or incoherent, making them difficult to apply; they are isolated principles situated in an industry and education system which largely ignores ethics; and they are toothless principles which lack consequences and adhere to corporate agendas. For these reasons, I argue that AI ethical principles are useless, failing to mitigate the racial, social, and environmental damages of AI technologies in any meaningful sense. The result is a gap between high-minded principles and technological practice. Even when this gap is acknowledged and principles seek to be “operationalized,” the translation from complex social concepts to technical rulesets is non-trivial. In a zero-sum world, the dominant turn to AI principles is not just fruitless but a dangerous distraction, diverting immense financial and human resources away from potentially more effective activity. I conclude by highlighting alternative approaches to AI justice that go beyond ethical principles: thinking more broadly about systems of oppression and more narrowly about accuracy and auditing.

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Research works printed but not linked: 48

  1. Amsler, S.S.: Embracing the politics of ambiguity: towards a normative theory of ‘Sustainability.’ Capital. Nat. Social. 20(2), 111–125 (2009) · doi:10.1080/10455750902941169
  2. Beller, J.: The World Computer: Derivative Conditions of Racial Capitalism. Duke University Press, Durham (2021) · doi:10.2307/j.ctv1fcf88w
  3. Bellin, J.: Pure privacy. Northwest. Univ. Law Rev. 116(2), 463 (2021)
  4. Bryson, J.J.: The artificial intelligence of the ethics of artificial intelligence: an introductory overview for law and regulation. In: Dubber, M.D., Pasquale, F., Das, S. (eds.) The Oxford Handbook of Ethics of AI. Oxford University Press, New York (2020)
  5. Buckley, R.P., Zetzsche, D.A., Arner, D.W., Tang, B.W.: Regulating artificial intelligence in finance: putting the human in the loop. Sydney Law Rev. 43(1), 43–81 (2021)
  6. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pp. 77–91. PMLR (2018)
  7. Burleigh, N.: What silicon valley thinks of women. Newsweek 28, 2015 (2015)
  8. Canca, C.: Operationalizing AI ethics principles. Commun. ACM 63(12), 18–21 (2020) · DOI
  9. Davis, J.L., Williams, A., Yang, M.W.: Algorithmic reparation. Big Data Soc. 8(2), 20539517211044810 (2021) · DOI
  10. Desai, J.N., Pandian, S., Vij, R.K.: Big data analytics in upstream oil and gas industries for sustainable exploration and development: a review. Environ. Technol. Innov. 21, 101186 (2021) · doi:10.1016/j.eti.2020.101186
  11. Eubanks, V.: Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, New York (2018)
  12. European Group on Ethics in Science and New Technologies.: Statement on artificial intelligence, robotics and ‘autonomous’ systems: Brussels, 9 March 2018. European Commission, Brussels (2018). https://data.europa.eu/doi/10.2777/531856 · doi:10.2777/531856
  13. Gabriel, I.: Toward a theory of justice for artificial intelligence. Daedalus 151(2), 218–231 (2022). https://doi.org/10.1162/daed_a_01911 · DOI
  14. García-Holgado, A., García-Peñalvo, F.J., Therón, R., Vázquez-Ingelmo, A., Gamazo, A., González-González, C.S., Iranzo, R.M.G., Silveira, I.F., Forment, M.A.: Development of a SPOC of computer ethics for students of computer science degree. In: 2021 XI International Conference on Virtual Campus (JICV), pp. 1–3. IEEE (2021) · doi:10.1109/jicv53222.2021.9600272
  15. Garrett, N., Beard, N., Fiesler, C.: More than ‘If Time Allows’: the role of ethics in AI education. Proc. AAAI/ACM Conference on AI, Ethics, and Society, pp. 272–8. (2020). https://dl.acm.org/doi/abs/10.1145/3375627.3375868. · DOI
  16. Gorur, R., Hoon, L., Kowal, E.: Computer science ethics education in Australia—a work in progress. In: 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 945–947. (2020). https://doi.org/10.1109/TALE48869.2020.9368375. · DOI
  17. Green, B.: The contestation of tech ethics: a sociotechnical approach to technology ethics in practice. J. Soc. Comput. 2(3), 209–225 (2021). https://doi.org/10.23919/JSC.2021.0018 · DOI
  18. IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems.: Ethically aligned design: a vision for prioritizing human well-being with autonomous and intelligent systems. IEEE (2019). https://ethicsinaction.ieee.org/
  19. Khan, A.A., Badshah, S., Liang, P., Khan, B., Waseem, M., Niazi, M., Akbar, M.A.: Ethics of AI: a systematic literature review of principles and challenges. (2021). http://arxiv.org/abs/2109.07906 [Cs] · DOI · Paper page
  20. Krijger, J.: Enter the metrics: critical theory and organizational operationalization of AI ethics. AI Soc. (2021). https://doi.org/10.1007/s00146-021-01256-3 · DOI
  21. Latour, B.: Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press, Oxford (2007)
  22. Lauer, D.: You cannot have AI ethics without ethics. AI Ethics 1(1), 21–25 (2021). https://doi.org/10.1007/s43681-020-00013-4 · DOI
  23. Luke, T.W.: Neither sustainable nor development: reconsidering sustainability in development. Sustain. Dev. 13(4), 228–238 (2005) · DOI
  24. Maxwell, R., Miller, T.: Greening the Media. Oxford University Press, Oxford (2012)
  25. McNamara, A., Smith, J., Murphy-Hill, E.: Does ACM’s code of ethics change ethical decision making in software development?” In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 729–733 (2018) · DOI · Paper page
  26. Mökander, J., Floridi, L.: Operationalising AI governance through ethics-based auditing: an industry case study. AI Ethics (2022). https://doi.org/10.1007/s43681-022-00171-7 · DOI
  27. Morley, J., Elhalal, A., Garcia, F., Kinsey, L., Mökander, J., Floridi, L.: Ethics as a service: a pragmatic operationalisation of AI ethics. Mind. Mach. 31(2), 239–256 (2021) · DOI · Paper page
  28. Morley, J., Floridi, L., Kinsey, L., Elhalal, A.: From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices. In: Ethics, Governance, and Policies in Artificial Intelligence, pp. 153–183. Springer, Berlin (2021) · DOI
  29. Munn, L.: Cash burning machine: uber’s logic of planetary expansion. TripleC Commun. Capital. Crit. Open Access J. Glob. Sustain. Inf. Soc. 17(2), 1–17 (2019). https://doi.org/10.31269/triplec.v17i2.1097 · DOI
  30. Munn, L.: Automation is a Myth. Stanford University Press, Stanford (2022) · DOI
  31. Munn, L.: Data and the new oil: cloud computing’s lubrication of the petrotechnical‬. J. Environ. Media 2(2), 211–227 (2022). https://doi.org/10.1386/jem_00063_1 · DOI
  32. Munn, L., Hristova, T., Magee, L.: Clouded data: privacy and the promise of encryption. Big Data Soc. 6(1), 2053951719848781 (2019). https://doi.org/10.1177/2053951719848781 · DOI
  33. Nemitz, P.: Constitutional democracy and technology in the age of artificial intelligence. Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci. 376(2133), 1–13 (2018). https://doi.org/10.1098/rsta.2018.0089 · DOI · Paper page
  34. Noble, S.: Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press, New York (2018) · DOI · Paper page
  35. Noble, S., Roberts, S.: Technological Elites, the Meritocracy, and Postracial Myths in Silicon Valley. Duke University Press, Durham (2019)
  36. Oliver, J.C., McNeil, T.: Undergraduate data science degrees emphasize computer science and statistics but fall short in ethics training and domain-specific context. PeerJ Comput. Sci. 7, e441 (2021) · DOI
  37. O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Penguin Books, London (2018)
  38. Oswald, D.: From ethics to politics: if design is problem solving, what then are the problems. In: Proceedings of the 18th International Conference on Engineering and Product Design Education, pp. 620–625. Aalborg (2016)
  39. Rességuier, A., Rodrigues, R.: AI ethics should not remain toothless! A call to bring back the teeth of ethics. Big Data Soc. 7(2), 2053951720942541 (2020) · DOI · Paper page
  40. Resseguier, A., Rodrigues, R.: Ethics as attention to context: recommendations for the ethics of artificial intelligence. Open Res. Europe 1(27), 27 (2021) · doi:10.12688/openreseurope.13260.2
  41. Sambasivan, N., Arnesen, E., Hutchinson, B., Prabhakaran, V.: Non-portability of algorithmic fairness in India. (2020). http://arxiv.org/abs/2012.03659 [Cs] · arXiv
  42. Schiff, Daniel, Justin Biddle, Jason Borenstein, and Kelly Laas. 2020. “What’s next for Ai Ethics, Policy, and Governance? A Global Overview.” In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 153–58. · DOI
  43. Smith, L.T.: Decolonizing Methodologies: Research and Indigenous Peoples. Zed Books Ltd, London (2021) · DOI · Paper page
  44. Vakkuri, V., Kemell, K.-K., Jantunen, M., Abrahamsson, P.: ‘This Is Just a Prototype’: how ethics are ignored in software startup-like environments. In: International Conference on Agile Software Development, pp. 195–210. Springer, Cham (2020) · DOI
  45. Vakkuri, V., Kemell, K.-K., Kultanen, J., Siponen, M., Abrahamsson, P.: Ethically aligned design of autonomous systems: industry viewpoint and an empirical study. https://doi.org/10.48550/arXiv.1906.07946 (2019). http://arxiv.org/abs/1906.07946 · DOI
  46. Wachter-Boettcher, S.: Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech. WW Norton & Company, London (2017)
  47. Weinberg, L.: Rethinking fairness: an interdisciplinary survey of critiques of hegemonic ML fairness approaches. J. Artif. Intell. Res. 74, 1–35 (2022). https://doi.org/10.1613/jair.1.13196 · DOI · Paper page
  48. Zalnieriute, M.: ‘Transparency-Washing’ in the digital age: a corporate agenda of procedural fetishism. In: The Digital Age: A Corporate Agenda of Procedural Fetishism, pp. 21–33. (2021) · DOI

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Metadata

Authors
Munn, L.
Venue
AI and Ethics
Published
2022-08-23
Volume
3
Issue
3
Pages
869-877
ISSN
  • 2730-5953 (ISSN-L)
  • 2730-5961
OpenAlex
W4292731246
Cited by
355
References
19
FWCI
73.92
Type
journal article
Language
en
Open access
hybrid
Licence
Funders
Western Sydney University

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