IEEE Technology and Society Magazine - December 2019 - 47

While there are many theoretical and empirical studies on factors affecting human-robot interaction trust
[20], previous to this survey there has been no corresponding empirical evidence regarding trust toward
robots in connection with trust among humans.
The conceptual framework of the present study was
enhanced by taking into account both the recent methodological Guidelines of the European Commission's
High-Level Expert Group on Artificial Intelligence (AI
HLEG) [22], and the results of the 2018 Edelman Trust
Barometer [23], which shows a dramatic distrust at market and media levels due to lack of reliability and transparency of information. The AI HLEG, raising the need for
a human- centric approach to AI, states explicitly that
"it is important to build AI systems that are worthy of
trust, since human beings will only be able to confidently and fully reap its benefits when the technology, including the processes and people behind the
technology, are trustworthy" [[22], p. 35].
Additionally, the guidelines identify three key components for the achievement of Trustworthy AI:
"1) it should be lawful, ensuring compliance with
all applicable laws and regulations, 2) it should be
ethical, demonstrating respect for, and ensure
adherence to, ethical principles and values, and
3) it should be robust, both from a technical and
social perspective, since, even with good intentions, AI systems can cause unintentional harm.
Trustworthy AI concerns not only the trustworthiness of the AI system itself but also comprises the
trustworthiness of all processes and actors that
are part of the system's life cycle" [[22], p. 38].
Although the first point is not specifically addressed
in AI HLEG, we may note that laws in different countries
may lead to unescapable contradictions. A more

suitable way to deal with this point would be to make
reference to human rights rather than laws in general.
The second condition raises an interesting question
since the ethical principles the document referred to
are the marking point of U.S. ethics, while the aim of
the document is to provide a specifically European ethics guide for AI. Of course, something like an "European ethics" is not an easy topic to address, nevertheless
this is the real next step. The third component correctly
addressed both of the perspectives according to which
a system could be defined as robust. This issue could
benefit from a more specific reference to the idea of
technical and social endgoals. Consider as an example
a robot devised to detect specific pathologies: it may
work very well, but if it undermines trust in the physician, it fails to deliver on a social level, i.e, on the level
of building trust between patient and physician. This is
how an accomplishment on the technical level could be
achieved at the expenses of a social aim.

Data Collection Tools
For data collection, an anonymous ad hoc online questionnaire was developed with a brief introduction describing
the aim of the survey and the demographic information to
be collected (age, sex, and education background). The
questionnaire, divided into three sections, contained 45
closed-ended questions. Section A (see Figure 1), consisting of 6 questions, was in Likert scale format using Yamagishi & Yamagishi's General Trust Scale [24]. This section
assessed respondents' beliefs in the honesty and trustworthiness of others, along with the propensity to be trustful
of others, and perceptions about the influence that trust
has on behavior toward others.
Section B's 33 questions addressed interest in technology and the participant's view of robots, along with
preferences about future scenarios of advanced robotics.
It also included 14 hypothetical questions about the
desire for robots to have an anthropomorphic appearance, an important factor affecting human comfort with

Only 1 Answer per Question
Strongly
Disagree

Disagree

Neutral

Agree

Strongly
Agree

1) Most people are basically honest
2) Most people are trustworthily
3) Most people are basically good and kind
4) Most people are trustful of others
5) I am trustful
6) Most people will respond in kind when they are trusted by others

FIGURE 1. Questonaire sample questions: Section A.

DECEMBER 2019

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IEEE Technology and Society Magazine - December 2019

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