Capturing contextual morality : applying game theory on smartphones

van Berkel, Niels; Hosio, Simo; Tag, Benjamin; Goncalves, Jorge (2019-09-30)
 
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https://doi.org/10.1145/3341162.3344846

van Berkel, Niels
Hosio, Simo
Tag, Benjamin
Goncalves, Jorge
Association for Computing Machinery
30.09.2019

Niels van Berkel, Simo Hosio, Benjamin Tag, and Jorge Goncalves. 2019. Capturing contextual morality: applying game theory on smartphones. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 888–892. DOI: https://doi.org/10.1145/3341162.3344846

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© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019, https://doi.org/10.1145/3341162.3344846.
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doi:https://doi.org/10.1145/3341162.3344846
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Abstract

In order to build more fair Artificial Intelligence applications, a thorough understanding of human morality is required. Given the variable nature of human moral values, AI algorithms will have to adjust their behaviour based on the moral values of its users in order to align with end user expectations. Quantifying human moral values is, however, a challenging task which cannot easily be completed using e.g. surveys. In order to address this problem, we propose the use of game theory in longitudinal mobile sensing deployments. Game theory has long been used in disciplines such as Economics to quantify human preferences by asking participants to choose between a set of hypothetical options and outcomes. The behaviour observed in these games, combined with the use of mobile sensors, enables researchers to obtain unique insights into the effect of context on participant convictions.

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