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Philosophical Inquiry into Computer Intentionality: Machine Learning and Value Sensitive Design

  • Dmytro Mykhailov ORCID logo EMAIL logo
Published/Copyright: December 23, 2022

Abstract

Intelligent algorithms together with various machine learning techniques hold a dominant position among major challenges for contemporary value sensitive design. Self-learning capabilities of current AI applications blur the causal link between programmer and computer behavior. This creates a vital challenge for the design, development and implementation of digital technologies nowadays. This paper seeks to provide an account of this challenge. The main question that shapes the current analysis is the following: What conceptual tools can be developed within the value sensitive design school of thought for evaluating machine learning algorithms where the causal relation between designers and the behavior of their computer systems has been eroded? The answer to this question will be provided through two levels of investigation within the value sensitive design methodology. The first level is conceptual. Within the conceptual level, we will introduce the notion of computer intentionality and will show how this term may be used for solving an issue of non-causal relation between designer and computer system. The second level of investigation is technical. At this level the emphasis will be given to machine learning algorithms.


Corresponding author: Dmytro Mykhailov, Postdoctoral fellow, School of Humanities, Southeast University, Nanjing211189, China, E-mail:

Funding source: National Social Science Fund of China

Award Identifier / Grant number: 19ZDA040

  1. Research funding: The work on this paper has been supported financially by the Major Project of the National Social Science Fund of China: “The philosophy of technological innovations and the practical logic of Chinese independent innovation” (技术创新哲学与中国自主创新的实践逻辑研究). Grant number: 19ZDA040.

References

Benjamin, J. J., Berger, A., Merrill, N., & Pierce, J. (2021). Machine learning uncertainty as a design material: A post-phenomenological inquiry. In ACM 2021 CHI Conference on Human Factors in Computing Systems.10.1145/3411764.3445481Search in Google Scholar

Berkich, D. (2017). The problem of original agency. Southwest Philosophy Review, 33(1), 75–82. https://doi.org/10.5840/swphilreview20173318Search in Google Scholar

Binns, R. (2017). Fairness in machine learning: Lessons from political philosophy. ArXiv, 81, 1–11. http://arxiv.org/abs/1712.03586Search in Google Scholar

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878Search in Google Scholar

Brey, P. (2010). Philosophy of technology after the empirical turn. Techné: Research in Philosophy and Technology, 14(1 PLISS), 36–48. https://doi.org/10.5840/TECHNE20101416Search in Google Scholar

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data and Society, 3, 1–12.10.1177/2053951715622512Search in Google Scholar

Flanagan, M., Howe, D., & Nissenbaum, H. (2008). Embodying values in technology: Theory and practice. In J. van den Hoven, & J. Weckert (Eds.), Information Technology and Moral Philosophy (pp. 322–353). Cambridge University Press. https://philpapers.org/rec/FLAEVI10.1017/CBO9780511498725.017Search in Google Scholar

Floridi, L. (2013). The Ethics of information. Oxford University Press.10.1093/acprof:oso/9780199641321.001.0001Search in Google Scholar

Floridi, L., & Sanders, J. W. (2004). On the morality of artificial agents. Minds and Machines, 14(3), 349–379. https://doi.org/10.1023/B:MIND.0000035461.63578.9d10.1023/B:MIND.0000035461.63578.9dSearch in Google Scholar

Friedman, B., Harbers, M., Hendry, D. G., van den Hoven, J., Jonker, C., & Logler, N. (2021). Eight grand challenges for value sensitive design from the 2016 Lorentz workshop. Ethics and Information Technology, 23, 5–16. https://doi.org/10.1007/s10676-021-09586-y.Search in Google Scholar

Friedman, B., & Hendry, D. (2019). Value sensitive design: Shaping technology with moral imagination. MIT Press.10.7551/mitpress/7585.001.0001Search in Google Scholar

Friedman, B., Kahn, P., & Borning, A. (2006). Value sensitive design and information systems. In P. Zhang, & D. Galletta (Eds.), Human–computer interaction in management information systems: Foundations (pp. 348–372). M. E. Sharpe.Search in Google Scholar

Gillespie, T. (2014). The relevance of algorithms. In Media Technologies (pp. 167–194). The MIT Press.10.7551/mitpress/9780262525374.003.0009Search in Google Scholar

Hoffmann, A. L. (2019). Where fairness fails: Data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society, 22(7), 900–915. https://doi.org/10.1080/1369118X.2019.1573912.Search in Google Scholar

Hoven, J. (2013). Value sensitive design and responsible innovation. In R. Owen, J. Bessant, & M. Heintz (Eds.), Responsible innovation (pp. 75–83). Wiley.10.1002/9781118551424.ch4Search in Google Scholar

Johnson, D. G. (2006). Computer systems: Moral entities but not moral agents. Ethics and Information Technology, 8(4), 195–204. https://doi.org/10.1007/s10676-006-9111-5Search in Google Scholar

Johnson, D. G., & Powers, T. M. (2008). Computers as surrogate agents. In Information Technology and Moral Philosophy (pp. 251–269). Cambridge University Press.10.1017/CBO9780511498725.014Search in Google Scholar

Jordan, M., & Bishop, C. (2004). Neural networks. In A. B. Tucker (Ed.), Handbook of Computer Science. CRC Press. https://leeway.tistory.com/950Search in Google Scholar

Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges. Philosophy and Technology, 31(4), 611–627. https://doi.org/10.1007/s13347-017-0279-xSearch in Google Scholar

Liberati, N. (2020). The Borg–eye and the We–I. The production of a collective living body through wearable computers. AI & Society, 35(1), 39–49. https://doi.org/10.1007/s00146-018-0840-xSearch in Google Scholar

Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175–183. https://doi.org/10.1007/s10676-004-3422-1Search in Google Scholar

Matthias, A. (2011). From coder to creator. In Handbook of Research on Technoethics (pp. 635–650). IGI Global.10.4018/978-1-60566-022-6.ch041Search in Google Scholar

Mykhailov, D. (2020). The phenomenological roots of technological intentionality: A postphenomenological perspective. Frontiers of Philosophy in China, 15(4), 612–635. https://doi.org/10.3868/s030-009-020-0035-6Search in Google Scholar

Mykhailov, D. (2021). A moral analysis of intelligent decision-support systems in diagnostics through the lens of Luciano Floridi’s information ethics. Human Affairs, 31(2), 149–164. https://doi.org/10.1515/humaff-2021-0013Search in Google Scholar

Mykhailov, D. (2022). Postphenomenological variation of instrumental realism on the “problem of representation”: fMRI imaging technology and visual representations of the human brain. Prometeica – Journal of Philosophy and Science, Special, 64–78. https://doi.org/10.34024/prometeica.2022.Especial.13520Search in Google Scholar

Mykhailov, D., & Liberati, N. (2022). A study of technological intentionality in C++ and generative adversarial model: Phenomenological and postphenomenological perspectives. Foundations of Science, 2022, 1–17. https://doi.org/10.1007/S10699-022-09833-5Search in Google Scholar

Pasquale, F. (2015). The black blox society: The secret algorithms that control money and information. Harvard University Press.10.4159/harvard.9780674736061Search in Google Scholar

Powers, T. M. (2013). On the moral agency of computers. Topoi, 32(2), 227–236. https://doi.org/10.1007/s11245-012-9149-4Search in Google Scholar

Primiero, G. (2017). Algorithmic iteration for computational intelligence. Minds and Machines, 27(3), 521–543. https://doi.org/10.1007/S11023-017-9423-8Search in Google Scholar

Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003Search in Google Scholar

Simon, J., Wong, P. H., & Rieder, G. (2020). Algorithmic bias and the value sensitive design approach. Internet Policy Review: Journal on Internet Regulation, 9(4), 1–16. https://doi.org/10.14763/2020.4.1534. https://ideas.repec.org/a/zbw/iprjir/233110.htmlSearch in Google Scholar

Umbrello, S., & van de Poel, I. (2021). Mapping value sensitive design onto AI for social good principles. AI and Ethics, 1, 3. https://doi.org/10.1007/s43681-021-00038-3Search in Google Scholar

van den Hoven, J. (2017). Ethics for the digital age: Where are the moral specs? In H. Werthner, & F. van Harmelen (Eds.), Informatics in the Future (pp. 65–76). Springer International Publishing.10.1007/978-3-319-55735-9_6Search in Google Scholar

van de Poel, I. (2020). Embedding values in artificial intelligence (AI) systems. Minds and Machines, 30(3), 385–409. https://doi.org/10.1007/s11023-020-09537-4Search in Google Scholar

Verma, S., & Rubin, J. (2018). Fairness definitions explained. Proceedings - International Conference on Software Engineering, 1–7. https://doi.org/10.1145/3194770.3194776.Search in Google Scholar

Wong, P. H. (2020). Democratizing algorithmic fairness. Philosophy and Technology, 33(2), 225–244. https://doi.org/10.1007/s13347-019-00355-wSearch in Google Scholar

Received: 2022-08-16
Revised: 2022-10-30
Accepted: 2022-11-02
Published Online: 2022-12-23
Published in Print: 2023-02-23

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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