NDLI: People show envy, not guilt, when making decisions with machines

Content Provider	IEEE Xplore Digital Library
Author	de Melo, C.M. Gratch, J.
Copyright Year	2015
Description	Author affiliation: USC Inst. for Creative Technol., Vista, CA, USA (de Melo, C.M.; Gratch, J.)
Abstract	Research shows that people consistently reach more efficient solutions than those predicted by standard economic models, which assume people are selfish. Artificial intelligence, in turn, seeks to create machines that can achieve these levels of efficiency in human-machine interaction. However, as reinforced in this paper, people's decisions are systematically less efficient - i.e., less fair and favorable - with machines than with humans. To understand the cause of this bias, we resort to a well-known experimental economics model: Fehr and Schmidt's inequity aversion model. This model accounts for people's aversion to disadvantageous outcome inequality (envy) and aversion to advantageous outcome inequality (guilt). We present an experiment where participants engaged in the ultimatum and dictator games with human or machine counterparts. By fitting this data to Fehr and Schmidt's model, we show that people acted as if they were just as envious of humans as of machines; but, in contrast, people showed less guilt when making unfavorable decisions to machines. This result, thus, provides critical insight into this bias people show, in economic settings, in favor of humans. We discuss implications for the design of machines that engage in social decision making with humans.
Starting Page	315
Ending Page	321
File Size	793708
Page Count	7
File Format	PDF
ISSN	21568111
e-ISBN	9781479999538
DOI	10.1109/ACII.2015.7344589
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economics Computers Decision Making Biological system modeling Decision making Bias Games Human vs. Machines Data models Guilt Standards
Content Type	Text
Resource Type	Article

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