=Paper=
{{Paper
|id=Vol-3825/extendedAbstract5-1
|storemode=property
|title=Trust in Deceptive Robots
|pdfUrl=https://ceur-ws.org/Vol-3825/extendedAbstract5-1.pdf
|volume=Vol-3825
|authors=Raffaella Esposito,Alessandra Rossi,Michela Ponticorvo,Silvia Ross
|dblpUrl=https://dblp.org/rec/conf/hhai/Esposito0P024
}}
==Trust in Deceptive Robots==
https://ceur-ws.org/Vol-3825/extendedAbstract5-1.pdf
Trust in deceptive robots
Raffaella Esposito1,∗,† , Alessandra Rossi1,† , Michela Ponticorvo1,† and Silvia Rossi1,†
1
University of Naples Federico II, Via Claudio 21, Naples, Italy
Abstract
In this work, we explore the dynamics of trust and deception in human-robot interaction (HRI). Trust is crucial
for successful interactions between humans and robots, especially when users are required to rely on robotic
systems for assistive tasks. While often viewed negatively, certain forms of deception, such as prosocial deception
which could take form of white lies or emotional expressions, can paradoxically enhance trust. Our aims are
to investigate how deceptive practices affect trust, to set a starting point or further research that delves into
the development, deterioration, and possible restoration of trust following deceptive interactions in robotics. In
particular, here we start by drawing on findings from existing literature and experimental work in human-robot
interaction.
Keywords
Trust, Social Robotics, Human-Robot Interaction, Deception
1. Introduction
The effectiveness of a robot in an assistive capacity depends on whether individuals follow the guidance
or instructions it offers [1]. Different methodologies exist for favoring the human compliance with the
robot’s suggestions. One prerequisite is trust, as usage of automation by humans is strongly influenced
by their trust in the robot [2]. At the same time, deception is also considered a necessity in situations
like rehabilitation and rescue operations, preventing emotional distress and possible conflicts, and in
these cases it is considered prosocial [3, 4]. However, deception is an ethical dilemma, since it can be
harmful to human’s trust in the robot and the human-robot interaction [4]. Although a loss of trust
may be a consequence of certain types of deception, actually trust can be obtained through deception,
especially where interpersonal relationships and emotional support are involved [5]. Thus, it is crucial
to understand the effects of deception on trust in robots, and how trust can be established and restored
in cases in which a robot’s deception has caused a decrease in trust. This might be the case of lies or
intentional errors [6]. In this contribution, we present traditional concepts of trust in HRI, and which
are the effects of the interaction with a deceptive robot on people’s trust.
2. The concept of trust in human-human relationships
Trust is a complex feeling, and several definitions exist. For example, Schilke’s [7] defines trust as the
willingness of one party (the trustor) to expose themselves to vulnerability in relation to another party
(the trustee). In doing so, the trustor takes a risk, believing that the trustee will act in ways that are
beneficial to the trustor, despite these actions being outside the trustor’s control.
The BDT (Basis-Domain-Target) framework [8] specifies that trust comprises three bases: reliability,
that is the consistent fulfilment of commitments; emotional trust as the assurance that one will not
cause emotional harm to another; honesty, involving straightforwardness and truthfulness in one’s
MULTITRUST workshop at the third International Conference on Hybrid Human-Artificial Intelligence - HHAI ’24, June 10–14,
2024, Malmo, Sweden
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open raffaella.esposito3@unina.it (R. Esposito); alessandra.rossi@unina.it (A. Rossi); michela.ponticorvo@unina.it
(M. Ponticorvo); silvia.rossi@unina.it (S. Rossi)
Orcid 0009-0002-6582-0002 (R. Esposito); 0000-0003-1362-8799 (A. Rossi); 0000-0003-2451-9539 (M. Ponticorvo);
0000-0002-3379-1756 (S. Rossi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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Workshop ISSN 1613-0073
Proceedings
actions and statements. Individuals can rely on others to demonstrate the three bases of trust from a
cognitive, affective and behavioral point of view, and the target dimensions of trust are familiarity and
specificity.
Jones defines trust as properly an attitude [9] which is characterized by optimism about another’s
goodwill and competence, combined with the expectation that the trusted person will be positively
motivated by the act of being trusted. Trust is more than just a rational assessment: it involves a positive
emotional tilt toward the trusted, that leads to a favorable interpretation of actions that might otherwise
be ambiguous or even suspect. By trusting others, people are more likely to engage in cooperative
behaviors, share resources, and support one another in mutual goals.
3. Towards trusting human-robot relationships
Lee and See [10] defined trust in automation as the attitude that an agent will help achieve an individual’s
goals in a situation characterized by uncertainty and vulnerability. Wagner et al. also highlight the
dimension of risk in trusting someone [11].
The three bases of trust [8] could be also applied to HRI. Indeed, reliability in robots could translate
to consistent performance across diverse and potentially challenging environments. Secondly, robots
expressing concern about human’s emotional safety could help in establishing trust towards robots.
However, robots displaying emotions are considered deceptive, as they show characteristics that they
don’t actually possess [12]. Finally, honesty in robots could involve straightforwardness about their
functionalities and limitations.
The emotional component of trust [9] could be also included in evaluating trust towards robots, as
the attribution to the robot of goodwill, competence, and motivation to act in a trustworthy manner.
4. Deception’s role in human-robot trust dynamics
Existing research highlights that deception can be particularly detrimental to trust. However, some
forms of deception can increase trust as firstly shown by Levine and Schweitzer [5]. In fact, it is
fundamental to distinguish between the different types of deception. We can categorize deception into
two main types: hiding the truth or showing the false [13].
In the context of robotic deception, Shim and Arkin developed a taxonomy consisting of eight types
of robotic deception. This taxonomy is based on three dimensions: the deceived party (human or non-
human), the deception’s goal (self-oriented or other-oriented), and the method of deception (physical or
mental) [3], acknowledging the existence of a kind of robotic deception specifically intended to benefit
to the user.
In HRI, trust in robots can diminish following exposure to deceptive motion paths [14, 15] and after
discovering a robot’s deceit in a driving simulation [16]. The robot’s inability of taking ownership of
its faults and attempt of blaming someone else (i.e., a human) has been showed to decrease notably
people’s trust in the robot [17]. Moreover, a robot that gives incorrect suggestions during a memory
card game prevents people to accept future suggestions, and negatively affect their perception of robot’s
reliability and the faith that the robot is able to succeed [18].
In the context of a multiplayer game (whack-a-mole), in which the robot subtly balanced wins among
participants, trust was affected by the order in which participants experienced the robot’s modes of
announcing wins (honest and balancing): trust increased when participants experienced the honest
mode after the deceptive mode and decreased when the sequence was reversed [19].
While considering deception in HRI, two theories about deception are of particular interest. These
are: Saetra’s belief that displaying emotions, which it is also referred to as anthropomorphism, is a
type of deception [4]; and Danaher’s distinction of deception in external, superficial and hidden state
deception [12].
5. Conclusions & Future Works
The aim of our research is to deepen the understanding of how trust in robots is built, eroded and
recovered after a loss, and the role of deception in these dynamics. To this extent, we started by
expanding upon the studies conducted so far.
By establishing the different types of deception, we intend to explore the role of prosocial deception
in trust establishment and maintenance. Further steps will include an investigation of the use of human
social cues and empathetic responses for maintaining trust and facilitating its recovery after breaches
caused by deceptive behaviors. Indeed, the ability of a robot to show emotions can garner higher
goodwill ratings compared to the robot merely expressing preferences [20]. Moreover, we aim at using
a robot endowed with Theory of Mind and ability to use human mechanisms, such as by providing
justifications that refer to mental states for a robot’s actions, to mitigate the loss of moral trust in a
robot, even amidst disagreement with its moral decisions [21].
Research shows that a robot’s ability to express emotions effectively is crucial for engaging, trust-
worthy, and productive interactions with humans. Emotionally expressive robots are perceived as
autonomous social agents, which enhances familiarity and comfort. Positive emotional expressions
from robots can also boost human performance by creating a supportive and motivating environment
[22].
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