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2 Gender dimension in Assistive Technology for the Elderly
Care
In the field of Assistive Technology for the elderly, the analysis of
data related to elder care, using sex and gender analysis, reveals new
opportunities for development of assistive technologies and robotics.
Researchers have studied the different needs of women and men
according to their age (Scheibinger, 2012). This research, thanks to the
direct collaboration of the elderly, their caregivers, and further
stakeholders, provides to the engineers many insights for designing
and developing assistive products that are more tailored to the users’
needs. Understanding the characteristics of the elderly population is
the key to successfully design new technologies and service systems.
Understanding how sex (physiological needs) and gender (social
issues) interact and influence aging can help designers to implement
technologies that best fit the needs of the people involved. Here are
some examples:
• Dementia: affects men and women alike in their age, but as
women live longer in many developed countries, they suffer
longer.
• Arthritis: It is much more common in women, about 2-3
times more than men of the same age.
• Deafness: this is much more common in men than women of
the same age.
In complex scenarios in which the robot should be able to recognize
the mood of the elderly through the facial expression, the influence
of gender and ethnicity is particularly significant
(Brody et al, 2008)
.
In
(Sheikhjafari et al, 2014)
it is reported a concrete example of
gender-based design. The robot tunes handshaking based on personal
characteristics of the person who interacts with it. The
characteristics which are considered in this research are gender and familiarity.
Handshaking mode changes according to the value of these factors.
The algorithm used by the robot to tailor the handshake is designed
as follows:
• The robot identifies the person
• Calculates "familiarity" and gender
• Adjust handshake based on computed data by setting
duration and topology
• Robot starts handshake
As the familiarity increases, the duration of handshaking increases
and stretches out the hand more. Generally, if the detected person is
a man, the robot increases the frequency of handshaking and its
duration. We want to point out, again, that studies of this type are of
great importance, especially when the assisting robots are to be
designed for people where greater adaptability is required than other
scenarios.
Starting from the previous considerations, another important aspect
is the study of how people perceive the aging process and how this
perception is associated with the general aspects of accepting
medical technology in terms of usability (Wilkiwska et al).
3
3 Simulated-Gender Robot in Human-Robot Interaction
In this section we show the results obtained in two experiments
dealing with the influence of robot gender on human behavior.
To investigate the effects of human and robot factors and their
interaction effects preliminarily, an experiment was conducted to
measure and analyze human cognition and feelings toward a robot under
the condition in which the robot was simply labeled as a male or a
female
(Takagi et al, 2011)
. The research question in the experiment
was the following one: how can human educational background,
human gender, and robot gender influence the cognition and feelings
toward the robot?
To detect the influence of educational background in the perception
of robot, the experimenters of both gender had two different
backgrounds: 1. natural science and technology education and 2. social
science education. The obtained results suggested differences
dependent on the educational backgrounds of humans in human-robot
interaction. The impression scores of the two items “politeness” and
“assertiveness” proved that the subjects with educational
backgrounds in natural science and technology considered the robot more
polite and assertive than did those with social science backgrounds.
Although robot gender did not have a sufficient effect in the
experiment, the manipulation check suggested that even labeling the robot
with gendered names could provide humans with the perception of
robot gender. The subjects in the experiment had less perception of
femininity for the male-labeled robot and less perception of
masculinity for the female-labeled robot, regardless that the appearance,
motions, and voices had no information on gender. Moreover, the
perception of femininity had a positive effect on the recall of
contents uttered by the robot. We can conclude that the results of this
experiment show a strong indirect effect of gender labeling of robots
on the human behaviors.
In
(Breazeal et al, 2009)
is described the study of persuasion as it
applies to human-robot interaction. The experiment was run at the
Museum of Science in Boston, where subjects interacted with a
humanoid robot whose gender was assigned to them. After a short
interaction and persuasive appeal, subjects responded to a donation
request made by the robot, and subsequently completed a post-study
questionnaire. Findings showed that men were more likely to donate
money to the female robot, while women showed little preference.
In Figure 1 it is reported the proportion of people that gave any
donation separated by subject gender, robot gender and whether or not
the subject was alone. Men consistently donate more often to the
female robot, while women change their gender preference
depending on whether or not they were alone with the robot. Women on the
other hand donate more often to the female robot when
accompanied, but reverse their preference to slightly favor the male robot
when alone. This experimental result suggesting a cross-gender
preference came as a surprise as some literature in social psychology
would tend to suggest a same-gender preference rather than a
crossgender preference. This is surely still an open question to be studied
in the HRI community.
4 About Gendered Innovations
From this insights it is clear that the gender dimension influences in
an important way the design of robots for assisting and interacting
with people. In scenarios where robots can assume complex
behaviors, it is very important to consider these factors for better results in
terms of robot's robustness and efficiency in running the various
tasks.
More in general, let’s see how new gendered science can be
developed together with new interpretations of facts with respect to an
universal male-point-of-view proposed as neutral. It is important to
understand how we can re-design the scientific theories, how we can
propose new hypothesis taking into account the gender dimension,
how we can formulate new scientific questions having the awareness
that another science is possible, how we can produce a critical view
of the method in re-shaping the science. According to
(Sanchez,
2013)
: “There is a need to go beyond stereotypical feminization of
products – so called “pinking” – as female preferences can be
drivers for substantial innovation”, the “pinking” method is not
sufficient to produce a new gendered innovation.
Another point to take into consideration is the difference that women
and men have in their approach to the use of technology. While
women tend to be more interested in the ease of use of technological
devices and in their social benefits, many men focus on the
performance of the technology and often, technological devices can become
for them quite a ‘status symbol’. Also social needs and life models
are different for women and men: this can largely influence
technology and its products. As women represent the mentality, the
preferences and the needs of every day by more than 50 % of the human
race it is important that, as reported in
(Sanchez 2013)
:
If research institutions and industry want to create valuable and
sustainable research results and technologies for people (the market), it
is recommended to include women at all stages of the research and
innovation process.
With these premises, let’s consider a formal reflection on the
scientific method and a critical analysis of logical rules underlying the
method used in Science
(Badaloni, 2016)
.
A very common belief is that, in the first instance, experiments are
conducted to test the hypothesis of a theory: if the expected
observations of experiments are verified then the theory is fully
demonstrated. Formally, if the assumptions of the theory are H and O the
observations, the rule underlying the knowledge process can be the
following:
H→O and
O
------------
H
From the premises that H implies O and O is true, we can deduce
that H is true. The logical rule that represents this schema goes under
the name of confirming argument: it seems well representing the
process of innovation in scientific research. But it is a wrong logical
rule as proved in
(Federspil, 2004)
. Science does not proceed for
confirming argument and does not advance according to the
progressive and continuous accumulation of truth but thanks to the attempts
of refutation of the theories proposed, we advance if there are errors
in the accepted theory. So the wright rule is called falsifying
argument, represented by:
H→O and
¬ O
------------
¬ H
From the premises H→O (H implies O) and ¬ O (not O, O false) it
can be deduced ¬ H (not H, H false). In other words, when the
consequences of a theory are not verified in the experimental context
then the theory has to be completely re-designed. This argument
corresponds to a correct logical rule called Modus Tollens.
To consider the gender in the development of new science we can
start from this rule. We have to put the following question: if a
certain theory H doesn’t take into account gender do we expect to find
the observations foreseen by the theory true (eg medicine vs gender
medicine)? Evidently not, because 50% of the users of the
innovations are women but, as evidenced by a large literature, it is
presumable state that the needs of this substantial part of users are not
incorporated in the search and the innovation. Hence these
observations can be false (¬ O) and the theories of departure, too (¬
H).
The rule underlying the scientific method in the production of
gendered innovations is the falsifying argument: this leads us to say that
to produce a new gendered science in all fields it is not sufficient to
apply the ‘pinking method’ but it is necessary to radically change the
assumptions. Only a complete redefinition of the method and the
research model with new applications and new ways of observation
can re-design the science in a gender perspective. Thus, in order to
design Intelligent Autonomous Systems able to socially interact for
facing complex challenges the gender dimension has to be taken
explicitly into account re-formulating the questions that can produce
responsible research innovations.