Adversarial Machine Learning: A Blow to the Transportation Sharing Economy

Steven Van Uytsel, Danilo Vasconcellos Vargas

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Adversarial machine learning has indicated that perturbations to a picture may disable a deep neural network from correctly qualifying the content of a picture. The progressing research has even revealed that the perturbations do not necessarily have to be large in size. This research has been transplanted to traffic signs. The test results were disastrous. For example, a perturbated stop sign was recognized as a speeding sign. Because visualization technology is not able to overcome this problem yet, the question arises who should be liable for accidents caused by this technology. Manufacturers are being pointed at and for that reason it has been claimed that the commercialization of autonomous vehicles may stall. Without autonomous vehicles, the sharing economy may not fully develop either. This chapter shows that there are alternatives for the unpredictable financial burden on the car manufacturers for accidents with autonomous cars. This chapter refers to operator liability, but argues that for reasons of fairness, this is not a viable choice. A more viable choice is a no-fault liability on the manufacturer, as this kind of scheme forces the car manufacturer to be careful but keeps the financial risk predicable. Another option is to be found outside law. Engineers could build infrastructure enabling automation. Such infrastructure may overcome the problems of the visualization technology, but could potentially create a complex web of product and service providers. Legislators should prevent that the victims of an accident, if it were still to occur, would face years in court with the various actors of this complex web in order to receive compensation.

Original languageEnglish
Title of host publicationPerspectives in Law, Business and Innovation
PublisherSpringer
Pages179-208
Number of pages30
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NamePerspectives in Law, Business and Innovation
ISSN (Print)2520-1875
ISSN (Electronic)2520-1883

Fingerprint

Learning systems
Accidents
accident
Railroad cars
liability
visualization
economy
Visualization
infrastructure
traffic sign
learning
Traffic signs
commercialization
automation
neural network
fairness
service provider
engineer
Automation
Engineers

All Science Journal Classification (ASJC) codes

  • Law
  • Management of Technology and Innovation

Cite this

Van Uytsel, S., & Vargas, D. V. (2020). Adversarial Machine Learning: A Blow to the Transportation Sharing Economy. In Perspectives in Law, Business and Innovation (pp. 179-208). (Perspectives in Law, Business and Innovation). Springer. https://doi.org/10.1007/978-981-15-1350-3_11

Adversarial Machine Learning : A Blow to the Transportation Sharing Economy. / Van Uytsel, Steven; Vargas, Danilo Vasconcellos.

Perspectives in Law, Business and Innovation. Springer, 2020. p. 179-208 (Perspectives in Law, Business and Innovation).

Research output: Chapter in Book/Report/Conference proceedingChapter

Van Uytsel, S & Vargas, DV 2020, Adversarial Machine Learning: A Blow to the Transportation Sharing Economy. in Perspectives in Law, Business and Innovation. Perspectives in Law, Business and Innovation, Springer, pp. 179-208. https://doi.org/10.1007/978-981-15-1350-3_11
Van Uytsel S, Vargas DV. Adversarial Machine Learning: A Blow to the Transportation Sharing Economy. In Perspectives in Law, Business and Innovation. Springer. 2020. p. 179-208. (Perspectives in Law, Business and Innovation). https://doi.org/10.1007/978-981-15-1350-3_11
Van Uytsel, Steven ; Vargas, Danilo Vasconcellos. / Adversarial Machine Learning : A Blow to the Transportation Sharing Economy. Perspectives in Law, Business and Innovation. Springer, 2020. pp. 179-208 (Perspectives in Law, Business and Innovation).
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