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ABSTRACT. We develop a conceptual framework based on a systematic and comprehensive literature review on urban mobility technologies. Building my argument by drawing on data collected from AUDI AG, AUVSI, Brookings, Capgemini, CivicScience, Ipsos, Kennedys, Perkins Coie, and Pew Research Center, we performed analyses and made estimates regarding algorithm-driven sensing devices and machine learning-based ethical judgments in a connected vehicle environment. The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 5,200 respondents.

Keywords: urban; mobility; algorithm; machine learning; connected; vehicle

How to cite: Walker, A., Rowland, Z., Frajtova Michalikova, K., and Svabova, L. (2020). “Urban Mobility Technologies, Algorithm-driven Sensing Devices, and Machine Learning-based Ethical Judgments in a Connected Vehicle Environment,” Contemporary Readings in Law and Social Justice 12(2): 34–42. doi:10.22381/CRLSJ12220204

Received 14 June 2020 • Received in revised form 6 November 2020
Accepted 9 November 2020 • Available online 10 November 2020

Amanda Walker
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The Personalized Health Care Laboratory
at CLI, Belfast, Northern Ireland
(corresponding author)
Zuzana Rowland
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The Institute of Technology and Business in Ceske Budejovice,
The School of Expertness and Valuation, Czech Republic
Katarina Frajtova Michalikova
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic
Lucia Svabova
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic

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