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Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems

  • Owen C. KingEmail author
Chapter
Part of the Philosophical Studies Series book series (PSSP, volume 134)

Abstract

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy.

Keywords

Machine learning algorithms Image recognition systems Training data Responsible AI judgment Ingrained responsibility Modular responsibility Intention ascription 

Notes

Acknowledgments

I am grateful to Andréa Atkins and to attendees of the IACAP 2016 for discussion of these issues. A preliminary exposition of some of the ideas and arguments presented in this chapter appeared in a short essay posted on the website of the Loyola Center for Digital Ethics and Policy (http://www.digitalethics.org/).

References

  1. Beeghly, Erin. 2015. What is a stereotype? What is stereotyping? Hypatia 30 (4): 675–691.CrossRefGoogle Scholar
  2. Blum, Lawrence. 2004. Stereotypes and stereotyping: A moral analysis. Philosophical Papers 33 (3): 251–289.MathSciNetCrossRefGoogle Scholar
  3. Davidson, Donald. 1984a. Inquiries into truth and interpretation. Oxford: Clarendon Press.Google Scholar
  4. ———. 1984b. Belief and the basis of meaning. Reprinted in Davidson (1984a): 141–154.Google Scholar
  5. ———. 2004a. Problems of rationality. Oxford: Clarendon Press.CrossRefGoogle Scholar
  6. ———. 2004b. Expressing evaluations. Reprinted in Davidson (2004a): 19–37.Google Scholar
  7. Dennett, Daniel. 1989a. The intentional stance. Cambridge, MA: MIT Press.Google Scholar
  8. ———. 1989b. True believers. Reprinted in Dennet (1989a): 13–35.Google Scholar
  9. Fei-Fei, Li, and Li-Jia Li. 2010. What, where and who? telling the story of an image by activity classification, scene recognition and object categorization. In Computer vision, ed. Cipolla et al., 157–171. Berlin: Springer.CrossRefGoogle Scholar
  10. Hodosh, Micah, Peter Young, and Julia Hockenmaier. 2013. Framing image description as a ranking task: Data, models and evaluation metrics. Journal of Artificial Intelligence Research 47: 853–899.MathSciNetCrossRefGoogle Scholar
  11. Karpathy, Andrej, and Li Fei-Fei. 2014. Deep visual-semantic alignments for generating image descriptions. arXiv preprint arXiv:1412.2306.Google Scholar
  12. Lippmann, Walter. 1922. Public opinion. New York: Macmillan.Google Scholar
  13. Marvit, Moshe. 2014. How crowdworkers became the ghosts in the digital machine. The Nation. http://www.thenation.com/article/how-crowdworkers-became-ghosts-digital-machine/. Accessed 11 Jan 2016.
  14. Moor, James. 1985. What is computer ethics? Metaphilosophy 16 (4): 266–275.CrossRefGoogle Scholar
  15. Park, Eunbyung, Xufeng Han, Tamara Berg, and Alexander Berg. (unpublishedms). Combining multiple sources of knowledge in deep CNNs for action recognition. http://www.cs.unc.edu/~eunbyung/papers/wacv2016_combining.pdf. Accessed 11 Jan 2016.
  16. Quine, W.V. 1960. Word and object. Cambridge, MA: MIT press.zbMATHGoogle Scholar
  17. Rashtchian, Cyrus, Peter Young, Micah Hodosh, and Julia Hockenmaier. 2010. Collecting image annotations using Amazon’s Mechanical Turk. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, 139–147. Association for Computational Linguistics.Google Scholar
  18. Strawson, Peter. 1962. Freedom and resentment. Proceedings of the British Academy 48: 1–25.CrossRefGoogle Scholar
  19. Vinyals, Oriol, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2014. Show and tell: A neural image caption generator. arXiv preprint arXiv:1411.4555.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of TwenteTwenteThe Netherlands

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