IEEE Technology and Society Magazine - December 2016 - 75

society relying on machines, and this group was the
most reluctant of all groups to think that "[they] should
be engaged in IMS impact evaluation," even in comparison with IMS researchers. PMs and SSH researchers took
a middle stance in this issue; hence, it is important to
recognize these differences when conducting collaborative research. Particularly, PMs and SSH researchers are
expected to bridge the gap between IMS researchers and
the public. The increasing development and use of intelligent machine technology raises issues about the compatibility between the technology and its implementation
in the human society. Being aware of the differences in
stakeholders' viewpoints will enhance the implementation of machine intelligence in various scenarios, many
of which we have considered in our survey.

Conclusion
This study conducted a questionnaire survey on various stakeholders from different professional groups to
investigate their perception of the current and future
road maps of AI/IMS in various fields. The results indicated that perceptions vary according to the professional background or experience of the stakeholders.
Figure 3 illustrates that people who had experience in
child rearing tended to choose option 1 (only humans
should do the activities involved). The same tendency
was shown by IMS researchers, as well. However, an
interesting contrast was found among people with
experience in different types of care: child nursing and
elder care (Figures 3 and 4). Both child nursing and
elder care includes the same keywords, such as "bathing" and "feeding," and some companies categorize
nursing and elder care as a single division when developing IMS. This result indicates that we should consider not only experience but also the context of care.
For example, since babysitters are not very common in
Japan, some people may be reluctant to ask others or
use machines to take care of their children. Therefore
further qualitative research must be conducted to
investigate how the type of experience and context of
each scenario influences the development of IMS.
The respondents of our survey were solely Japanese
and as prior studies emphasize the different perceptions regarding AI/robots between Japan and the
U.S.A./EU, we must acknowledge the cultural bias, and
hence the limited generalizability, of the data [9].
Although this questionnaire survey was conducted in
Japan, we believe that the research is of particular interest and use to all AI communities and can lead to a conversation on human-machine relations and collaborative
research among various stakeholders. In the future, we
hope to conduct similar surveys in other countries as
well as conducting a more extensive quantitative analysis on human-machine relations including some
DECember 2016

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scenarios that are not discussed in the current study,
such as education, agriculture, and medical care.

Author Information
A. Ema is with The University of Tokyo, Japan.
N. Akiya is with The Yamaguchi University, Japan.
H. Osawa is with University of Tsukuba, Japan.
H. Hattori is with Ritsumeikan University, Japan.
S. Oie is with Kobe University, Japan.
R. Ichise is with National Institute of Infomatics, Japan.
N. Kanzaki is with Nanzan University, Japan.
M. Kukita is with Nagoya University, Japan.
R. Saijo is with Hokkaido University, Japan.
T. Otani is with Kibi International Univerity, Japan.
N. Miyano, and Y. Yashiro are with Kyoto University,
Japan.

Acknowledgment
We thank D. Sakamoto, T. Narumi, T. Inoue, K. Narihara, S. Hase, and K. Nagakura for their cooperation.
We also thank respondents of the questionnaire survey. This work was supported by International Institute for Advanced Studies, National Institute of
Informatics and the JSPS KAKENHI Grant Number
15K12435. This is a joint research project with the
Acceptable Intelligence with Responsibility (AIR)
Research Group and the Research Institute of Science
and Technology for Society (RISTEX) of Japan Science
and Technology Agency (JST).

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