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Machine Thinking, Fast and Slow
被引:25
|作者:
Bonnefon, Jean-Francois
[1
]
Rahwan, Iyad
[2
]
机构:
[1] Univ Toulouse Capitole, CNRS, Toulouse Sch Econ TSM R, Toulouse, France
[2] Max Planck Inst Human Dev, Ctr Humans & Machines, Berlin, Germany
关键词:
ALGORITHM AVERSION;
PEOPLE;
INCREASES;
SELECTION;
TRUST;
ROBOT;
D O I:
10.1016/j.tics.2020.09.007
中图分类号:
B84 [心理学];
C [社会科学总论];
Q98 [人类学];
学科分类号:
03 ;
0303 ;
030303 ;
04 ;
0402 ;
摘要:
Machines do not 'think fast and slow' in the sense that humans do in dualprocess models of cognition. However, the people who create the machines may attempt to emulate or simulate these fast and slow modes of thinking, which will in turn affect the way end users relate to these machines. In this opinion article we consider the complex interplay in the way various stakeholders (engineers, user experience designers, regulators, ethicists, and end users) can be inspired, challenged, or misled by the analogy between the fast and slow thinking of humans and the Fast and Slow Thinking of machines.
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页码:1019 / 1027
页数:9
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