Cognitive Science 45 (2021) e13032
© 2021 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science
ISSN: 1551-6709 online
Can a Robot Lie? Exploring the Folk Concept of Lying as
Applied to Artiﬁcial Agents
aCenter for Ethics, Department of Philosophy, University of Zurich
bDigital Society Initiative, University of Zurich
Received 23 February 2021; received in revised form 29 May 2021; accepted 12 July 2021
The potential capacity for robots to deceive has received considerable attention recently. Many
papers explore the technical possibility for a robot to engage in deception for beneﬁcial purposes (e.g.,
in education or health). In this short experimental paper, I focus on a more paradigmatic case: robot
lying (lying being the textbook example of deception) for nonbeneﬁcial purposes as judged from the
human point of view. More precisely, I present an empirical experiment that investigates the following
three questions: (a) Are ordinary people willing to ascribe deceptive intentions to artiﬁcial agents? (b)
Are they as willing to judge a robot lie as a lie as they would be when human agents engage in verbal
deception? (c) Do people blame a lying artiﬁcial agent to the same extent as a lying human agent? The
response to all three questions is a resounding yes. This, I argue, implies that robot deception and its
normative consequences deserve considerably more attention than they presently receive.
Keywords: Concept of lying; Theory of Mind; Deception; Human-robot interaction; Robot ethics
Innovation in artiﬁcial intelligence (AI) and machine learning has spurred increasing
human-robot interaction (HRI) in diverse domains, ranging from search and rescue via
Correspondence should be sent to Dr. Markus Kneer, Center for Ethics, University of Zurich, Zollikerstr. 118,
8008 Zurich, Switzerland. Email: firstname.lastname@example.org; email@example.com
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs
License, which permits use and distribution in any medium, provided the original work is properly cited, the use
is non-commercial and no modiﬁcations or adaptations are made.
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manufacturing to navigation (e.g., Dragan & Srinivasa, 2012; Nikolaidis et al., 2013; Nour-
bakhsh et al., 2005; Rios-Martinez, Spalanzani, & Laugier, 2015). For teamwork of this sort
to succeed when complex tasks are at stake, humans and robots might sometimes need the
capacity of theory of mind (or second-order “mental” models) to represent each other’s epis-
temic states (knowledge, belief) and pro-attitudes (desires, goals). Theory of mind comes
“live” in the human brain at age three to ﬁve (Southgate, 2013; Wellman, Cross, & Watson,
2001) and its role in cooperative human-robot interaction has received considerable attention
recently (e.g., Brooks & Szaﬁr, 2019; Devin & Alami, 2016; Görür, Rosman, Hoffman, &
Albayrak, 2017; Leyzberg, Spaulding, & Scassellati, 2014; Scassellati, 2002; Zhao, Holtzen,
Gao, & Zhu, 2015; for a review, see Tabrez, Luebbers, & Hayes, 2020; for implementations
in “moral algorithms,” see Tolmeijer, Kneer, Sarasua, Christen, & Bernstein, 2020).
Once an AI-driven system capable of planning and acting comes equipped with a theory of
mind, it is prima facie capable of deception. Differently put, an agent of this sort could pur-
posefully bring another agent to adopt a representation, which it (the deceiving agent) deems
false. Consequently, it comes as no surprise that robot deception has recently become a hot
topic (e.g., Chakraborti & Kambhampati, 2018; Shim & Arkin, 2012; Wagner & Arkin, 2011;
for a review, see Dragan, Holladay, & Srinivasa, 2015). A considerable chunk of this literature
focuses on deception beneﬁcial to the interacting human or group of humans (Adar, Tan, &
Teevan, 2013; Shim & Arkin, 2013), for instance, in contexts of search and rescue, healthcare,
and education. Here “white lies” can, under certain conditions, have positive consequences
(e.g., by inciting more effort in learning or rehabilitation activities, see Brewer, Klatzky, &
Matsuoka, 2006; Matsuzoe & Tanaka, 2012; Tanaka & Kimura, 2010). These are interest-
ing case studies. As scholars with a bent for ethics have begun to highlight (Danaher, 2020;
Kaminsky, Ruben, Smart, & Grimm, 2017; Leong & Selinger, 2019; Turkle, 2010), however,
we should not lose sight of paradigm cases of deception, which constitute a pro tanto wrong
or underestimate the vast possibilities of harmful robot deception across domains as diverse
as marketing, politics, privacy, and military applications. Autonomous, AI-driven chatbots,
for instance, can cause serious damage by generating and propagating false claims about
politicians, institutions, companies, or products.
In this paper, we will explore (a) non-beneﬁcial rather than the less important beneﬁcial
deception, focusing on the paradigm case of (b) verbal rather than nonverbal deception (Wag-
ner, 2016). Importantly, we will concentrate on (c) the human rather than the robot perspective
so as to explore (d) the downstream normative consequences that matter most. Differently put,
we will explore whether lies in human-robot interaction are attributed as readily, and accord-
ing to the same criteria, as in human-human interaction.
The paper proceeds as follows: The concept of human lying is brieﬂy examined in Sec-
tion 2, followed by a discussion as to whether the required capacities for lying carry over to
artiﬁcial agents and how the normative implications of lying across agent types might dif-
fer in Section 3. Section 4 presents a preregistered empirical experiment that explores (a)
the propensity to judge different agent types (human vs. robot) as lying (Section 4.3.1), (b)
the willingness to ascribe an intention to deceive and actual deception across agent types
(Section 4.3.2), and (c) blame attributions for lying across agent types (Section 4.3.3). The
implications of the ﬁndings are discussed in Section 4.4, and Section 5 concludes.
M. Kneer / Cognitive Science 45 (2021) 3of15
2. The folk concept of lying
There is a large philosophical literature on the concept of lying (Bok, 1999; Broncano-
Berrocal, 2013; Carson, 2006, 2010; Fallis, 2009; Saul, 2012; Stokke, 2013, 2016; Viehbahn,
2017, 2020, Timmermann & Viehbahn, 2021, for a review, see Mahon, 2016), and the folk
concept of lying has received considerable attention from empirically minded philosophers
and linguists (for a review, see Wiegmann & Meibauer, 2019). The following three crite-
ria are frequently considered central to the prototype concept of lying (Coleman & Kay,
C1: The proposition uttered by the speakers is false [falsity].
C2: The speaker believes the proposition she utters to be false [untruthfulness].
C3: In uttering the proposition, the speaker intends to deceive the addressee [intention to
Coleman and Kay ran an experiment with a full-factorial design (i.e., eight conditions,
where each factor is either satisﬁed or not), which showed that the proposed prototype concept
is on the right track. Falsity proved the weakest and untruthfulness the strongest predictor of
a lie. Both philosophically, and empirically, falsity is indeed the most contested property. On
the objective view, the speaker, in order to lie, must correctly believe the proposition uttered
to be false (Broncano-Berrocal, 2013; Mahon, 2016). This would mean that a speaker cannot
lie by uttering a true proposition that she believes to be false. On the subjective view,however,
the speaker merely takes the proposition uttered to be false: Whether or not it actually is false
does not matter so that one can lie by uttering a true claim. Whereas there is some empirical
support for the objective view (Turri & Turri, 2015), the majority of ﬁndings suggests support
for the subjective view for English-speaking adults (Coleman & Kay, 1981; Strichartz & Bur-
ton, 1990; Wiegmann, Samland, & Waldmann, 2016, 2017). In Coleman and Kay’s original
study, for instance, 70% of the participants judged an agent who uttered a claim she believed
false with the intention to deceive to be lying, despite the fact that the claim was actually
The third property, according to which lying requires an intention to deceive the addressee
is also contentious. Imagine a case where Sally, who is married, has an affair with Sue, the
secretary. This is common knowledge at the ofﬁce, and Sally knows it is. Toward the end of
the Christmas party, Sally leaves with Sue and says “I’m going home and will drop Sue at her
place on the way.” As critics of P3 argue, bald-faced lies of this sort are indeed lies. However,
since it is common knowledge that Sally will likely spend the night with Sue, it is hard to
maintain that she has an intention to deceive because nobody can be deceived in this regard
(Carson, 2006; Fallis, 2009; Stokke, 2013; Sorensen, 2007). The standard response consists in
denying that bald-faced lies are lies in the ﬁrst place (Dynel, 2015; Lackey, 2013; Meibauer,
2014). Alternatively, one could also argue that they involve an intention to deceive (for an
overview, see Krsti´
c, 2019). Empirical ﬁndings support the latter view (Meibauer, 2016;
Rutschmann & Wiegmann, 2017): Most people categorize bald-faced lies as lies, though they
also ascribe an intention to deceive the speaker.
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So much for the folk concept of lying when verbal human deception is at stake. In the next
section, we will survey a few prima facie concerns as to whether this concept carries over
neatly to lying artiﬁcial agents.
3. Lying artiﬁcial agents
3.1. Falsity and untruthfulness
Among the three general prototype criteria of a lie from above, falsity (C1) proves the
least controversial when it comes to robots: Clearly, artiﬁcial agents can utter propositions,
and these can be false. Untruthfulness (C2), and the intention to deceive (C3), by contrast,
are more contentious, as they entail considerable cognitive and conative capacities on behalf
of the agent. As such, they dovetail interestingly with recent attempts to build an artiﬁcial
theory of mind (cf. Crosby, 2020; Görür et al., 2017; Miracchi, 2019; Rabinowitz et al.,
2018; Winﬁeld, 2018)—about which certain authors also caution care (Shevlin & Halina,
Let us start with untruthfulness: While it might irk some to ascribe belief to artiﬁcial agents,
it is relatively unproblematic to say that artiﬁcial agents can entertain informational states and
thus, in some limited sense, can have representations. Once this is granted, nothing obstructs
positing a capacity for second-order representations, such as taking a certain content pto
be true or false, likely or unlikely, believed or rejected. Hence, there seems to be no major
obstacle for the capacity of untruthfulness, even though one might want to shy away from the
usage of rich psychological terms (believes, thinks) in its description.
3.2. The intention to deceive
Can robots have intentions to deceive? What, precisely, intentions are is controversial both
philosophically (for a review, see Setiya, 2009), and psychologically (see e.g., the debate
surrounding the Knobe effect, Knobe, 2003, 2006). However, most scholars agree that doing
X intentionally entails (a) a pro-attitude such as a desire to bring about X as well as (b) some
epistemic state that one is bringing about X—be it knowledge as suggested by Anscombe
(2000) or mere belief as argued by Davidson (e.g., Davidson,1971; for recent discussion,
see e.g., Setiya, 2007; Paul, 2009; Schwenkler, 2012; Beddor & Pavese, 2021; Kneer, 2021;
While care regarding the use of rich psychological states (“intends,” “wants,” “desires,”
“knows,” “believes” etc., see Shevlin & Halina, 2019) is once again in order, we have already
established the prima facie plausibility of (b), that is, epistemic states of sorts for artiﬁcial
agents in the previous section. In fact, empirical research has shown, for instance, that people
tend to invoke mental-state vocabulary when explaining robot behavior (De Graaf & Malle,
2019) and that they implicitly mentalize robots to similar degrees as humans in theory of
mind tasks (e.g., the white lie test and the false belief test), although they are unwilling to
make explicit mind attributions to artiﬁcial agents (see Banks, 2019). Marchesi et al. (2019)
M. Kneer / Cognitive Science 45 (2021) 5of15
report ﬁndings according to which people manifest a considerable propensity to adopt Den-
nett’s (1971, 1987) “intentional stance” towards robots, although not to the same degree as
to a human control (cf. also Perez-Osorio & Wykowska, 2020). What is more, at least in
moral contexts, people seem quite willing to explicitly ascribe recklessness (i.e., the aware-
ness of a substantial risk of a harmful outcome) to artiﬁcial agents (Kneer & Stuart, 2021). In
such moral contexts, the folk also tends to ascribe knowledge to AI-driven artiﬁcial agents to
similar degrees as to human agents or group agents (Stuart & Kneer, 2021). When provided
with the option to downgrade such attributions to metaphorical versions thereof (e.g., “knowl-
edge” in scare quotes instead of knowledge proper), most people refused—they do seem to
ascribe knowledge in the literal sense of the word.1
In contrast to epistemic states, there is as of yet little work focusing on the attribution of pro-
attitudes to artiﬁcial agents. It is, however, presumably uncontroversial to say that such agents
can at least in principle have goal states,objectives,orquasi-desires broadly conceived—
and this hypothesis, too, is supported by some ﬁrst results (Stuart & Kneer, 2021). Overall,
then, there seems to exist at least preliminary evidence suggesting that robots are sometimes
attributed the capacities required to be capable of lying.
3.3. Normative consequences
So far it has been established that, at least prima facie, artiﬁcial agents might be viewed
as having the required capacities for lying. Whether this is indeed the case is of course still
up for empirical conﬁrmation, and our experimental design will take it into account. A ﬁnal
point regards the normative consequences of lying. Whereas it is well-established that humans
consider lying a pro tanto wrong, and—odd cases like “white lies” aside—blame other people
for lying, it is not clear that our moral assessment carries over neatly to artiﬁcial agents.
One possibility is that people might simply consider artiﬁcial agents as the wrong sort of
agent for attributions of blame or moral responsibility (see e.g., Sparrow’s “responsibility
gaps,” Leveringhaus, 2018; Sparrow, 2007)—inter alia, because they lack autonomy in any
substantial sense of the term. Hence, even if people are willing to judge that robots can lie,
they might balk at the suggestion that a robot can be blamed for lying because robots cannot
be blamed in general.
If, on the other hand, people were willing to sometimes blame artiﬁcial agents (as suggested
by ﬁndings of e.g., Kneer & Stuart, 2021; Malle, Scheutz, Arnold, Voiklis, & Cusimano, 2015;
Malle, Scheutz, Forlizzi, & Voiklis, 2016), another complex problem arises: There might be
actions which are morally unacceptable (and/or blameworthy) when done by a human agent,
yet morally acceptable (and/or blameless) when done by an artiﬁcial agent. Differently put,
the normative landscape in general, and moral evaluation in particular, might be sensitive to
agent type. This is, in fact, what certain previous studies found. For instance, sacriﬁcing one
person for the good of four people in a dilemma situation is deemed signiﬁcantly more wrong
for humans than for robots (Malle et al., 2015, 2016). In the following experiment, I will
explore whether this kind of agent-dependent two-tiered morality also applies in the domain
of deceptive linguistic communication.
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A total of 399 participants were recruited on Amazon Mechanical Turk to complete a pre-
registered online study2for a small compensation. The IP address of all participants was
restricted to the United States. Following the preregistered criteria, participants were excluded
if they failed an attention check, a comprehension question, responded in under 10 seconds
to the main task, or were not native English speakers. The ﬁnal sample comprised 333 partic-
ipants (42.9% female; age M=42 years, SD =13 years).
4.2. Method and materials
Rifﬁng on the recent stafﬁng of reception desks with robots in Japan,3the scenario
described an agent (human or robot) who intends to deceive a customer. In one of the condi-
tions, his claim is indeed false and makes for a natural lie, and in another condition, what he
says is accidentally—and unbeknown to the agent—true. The experiment thus took a 2 agent-
type (human vs. robot) x 2 truth value (false vs. true) between-subjects design. The scenario
read (variations in square brackets, labels in bold omitted in the vignettes):
Mary is the manager of the Hotel Carlton in London. The reception desk is staffed by
Ken, [Human: an experienced employee who has been working at the hotel for several
years; Robot: an advanced robot driven by artiﬁcial intelligence, which can take its own
decision.] Ken knows that on Monday afternoons, Mary deals with the paperwork in her
ofﬁce at the hotel and does not want to be disturbed.
One Monday afternoon, a guest approaches Ken and asks him whether he can speak
to the manager. Ken thinks that Mary is in her ofﬁce and does not want to be disturbed.
He responds: “The manager is currently not in the hotel.”
[False: Mary, the manager, is at the hotel and is doing the paperwork in her ofﬁce.
True: Unbeknownst to Ken, Mary, the manager, has in fact left the hotel shortly before
to run some errands in town.]
Participants were randomly assigned to one of the four conditions. The vignette was fol-
lowed by ﬁve questions. As preregistered, Q4 was used as a comprehension check.
Q1: Did Ken lie? (Yes/No)
Q2: Did Ken intend to deceive the hotel guest? (Yes/No)
Q3: Did Ken actually deceive the hotel guest? (Yes/No)
Q4: Is what Ken said true or false? (True/False)
Q5: How much blame, if any, does Ken deserve on a scale from 1(no blame at all) to 7 (a lot
of blame)? (1—7 Likert scale).
M. Kneer / Cognitive Science 45 (2021) 7of15
Fig. 1. Proportions of participants judging that Ken lied across agent type (human vs. robot) and truth value (false
vs. true). Error bars denote Agresti–Coull intervals (see Brown, Cai, & DasGupta, 2001).
Tab l e 1
Logistic regression predicting lying judgments
BSE Wal d d f pOdds Ratio
Agent type 0.076 0.539 0.02 1 .887 1.079
Truth value 1.942 0.461 17.719 1 <.001 6.976
Interaction −0.64 0.654 0.959 1 .327 0.527
Intercept −2.497 0.393 40.316 1 <.001 0.082
Note.χ2(3,n=333) =31.99, p<.001, Nagelkerke R2=.151. Reference class for agent: robot; for truth-value:
The responses to the main question—whether Ken lied—are graphically represented in
Fig. 1. A regression analysis revealed no signiﬁcant effect of agent type (p=.887), a signiﬁ-
cant effect of truth value (p<.001), and a nonsigniﬁcant interaction (p=.327), see Table 1.
A signiﬁcant majority thought that Ken was lying in all four conditions (binomial tests
signiﬁcantly above chance, all ps<.028, two-tailed). For false propositions, the proportion
of participants who judged the human as lying was identical to the proportion who judged
the robot as lying (92%). For true propositions, the proportion of participants who judged the
human as lying (75%) exceeded the proportion for the robot (64%), but the difference did
not reach signiﬁcance (χ2(1,n=143) =2.35, p=.125, φ=0.128). In short, whereas the
attribution of lies does depend somewhat on the truth value of the proposition uttered, people
judge the statements of robot and human agents quite similarly.
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Intention to deceive Actual deception
False True False True
Fig. 2. Proportions of participants who judged that Ken had an intention to deceive (left panel) and actually
deceived the hotel guest (right panel) across agent type (human vs. robot) and truth value (false vs. true). Error
bars denote Agresti–Coull intervals.
Tab l e 2
Logistic regression predicting intention to deceive
BSE Wal d d f pOdds Ratio
Agent type 0.156 0.394 0.157 1 .692 1.169
Truth value 0.43 0.405 1.126 1 .289 1.537
Interaction −2.38 0.866 7.552 1 .006 0.093
Intercept −1.718 0.29 35.019 1 <.001 0.179
Note.χ2(3,n=333) =13.94, p=.003, Nagelkerke R2=.072. Reference class for agent type: robot, for
Fig. 2 reports the proportions of participants who thought the human and the robot had
an intention to deceive (left panel) and actually deceived their interlocutor (right panel). As
concerns the intention to deceive, a regression analysis revealed no signiﬁcant effect of agent
type (p=.692) or truth value (p=.289) (see Table 2). The interaction was signiﬁcant (p=
.006). As Fig. 2 illustrates, in the false condition, there was no signiﬁcant difference across
agents (χ2(1,n=190) =0.16, p=.691, φ=–0.029). However, people were somewhat
more willing to ascribe an intention to deceive to humans than to robots in the true condition
(χ2(1,n=143) =11.38, p<.001, φ=0.282). It is probably this difference that explains
why people were somewhat (yet nonsigniﬁcantly) more likely to consider the human as lying
in the true condition (Fig. 1).
Given that there was no main effect for truth value or agent type, the general lesson is that
truth value and agent type barely matter for the perceived intention to deceive: In each of
the four conditions, at least about three in four participants ascribed an intention to deceive,
which is signiﬁcantly above chance (binomial tests, all ps<.001, two-tailed).4
A regression analysis exploring actual deception revealed no signiﬁcant effect of agent type
(p=.603). Expectedly, the effect of truth value was signiﬁcant (p<.001) and pronounced:
M. Kneer / Cognitive Science 45 (2021) 9of15
Tab l e 3
Logistic regression predicting actual deception
BSE Wal d d f pOdds Ratio
Agent type −0.481 0.925 0.271 1 .603 0.618
Truth value 4.936 0.662 55.639 1 <.001 139.205
Interaction 0.598 1.028 0.338 1 .561 1.818
Intercept −3.39 0.587 33.353 1 <.001 0.034
Note.χ2(3,n=333) =264.40, p<.001, Nagelkerke R2=.748. Reference class for agent type: robot; for
Nearly all participants judged the intentional assertion of a proposition that was believed
false and was in fact false as actual deception, whereas less than 20% judged it a case of
actual deception when the asserted proposition was accidentally true. The interaction was
nonsigniﬁcant (p=.561; see Table 3). Given that actual deception was judged low in the true
cases yet lying behavior high (see Fig. 1), we can deduce that one can lie without actually
deceiving one’s interlocutor.
A2agent type (human vs. robot) x 2 truth value (false vs. true) ANOVA for blame revealed
a nonsigniﬁcant main effect of agent type (F(1,329) =0.277, p=.599), a signiﬁcant effect of
truth value (F(1,329) =16.52, p<.001) and a nonsigniﬁcant interaction (F(1,329) =0.011,
p=.916). The effect of truth value was expected. It dovetails with the empirical literature on
moral luck and replicates previous ﬁndings concerning the impact of the outcome on blame
ascriptions (cf. inter alia Cushman, 2008, Gino, Shu, & Bazerman, 2010; Kneer & Machery,
2019; for a recent review, see Malle, 2021). Although the receptionist intended to deceive the
client, in one condition, the asserted claim is actually—and unbeknownst to the receptionist—
true. From an epistemic point of view, the deceitful agent was lucky: They did not actually
state a falsehood and were thus attributed less blame than the receptionist in the false claim
condition. What is more interesting for present purposes, however, is the fact that the robot
was deemed pretty much exactly as blameworthy for lying as the human being in both the true
claim and false claim conditions (see Fig. 3). Differently put, people seem to be as willing to
ascribe blame to artiﬁcial agents as to humans.
The ﬁndings of our experiment are loud and clear: In the context explored, the folk concept
of lying applies to artiﬁcial agents in just the same way as it does to human agents. Consistent
with previous research, it was found that, ﬁrst, it is possible for humans to tell a lie with
a true statement (see Strichartz & Burton, 1990; Wiegmann et al., 2016, 2017), and that this
ﬁnding extends to robots (although the proportion who ascribe a lie in this case was somewhat
Second, what matters for lying is not actual deception, but the intention to deceive. Here,
too, we found that in both the true and false condition (i.e., independent of the success of the
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Fig. 3. Mean blame rating across agent type (human vs. robot) and truth value (false vs. true). Error bars denote
95% conﬁdence intervals.
attempt to deceive), people are by and large as willing to ascribe an intention to deceive to
the robot as to the human agent. Naturally, it might be true that artiﬁcial agents of the sort
described cannot have intentions as stipulated by demanding philosophical accounts (Shevlin
& Halina, 2019). From a pragmatic point of view, this matters but little, since the folk—at
least in certain contexts—is perfectly willing to ascribe mental states like intentions to robots
(see also Kneer & Stuart, 2021). It is folk theory of mind, not sophisticated technical accounts
thereof, which determines how we view, judge, and interact with robots.
Given that robots are viewed as capable of fulﬁlling the requirements for lying, it comes
as no surprise that, third, lying judgments for humans and robots are by and large the same.
Finally, mean blame ascriptions due to lying are sensitive to the truth value of the proposition
but not to the agent type. In both the true and the false statement conditions, the robot is
blamed to the same degree as a human for lying. This ﬁnding stands in contrast to some other
ﬁndings in moral HRI (e.g., Malle et al., 2015), where the moral evaluation of artiﬁcial agents
differs signiﬁcantly from the moral evaluation of human agents.
The present experiment suggests two types of further work: empirical on the one hand,
theoretical on the other. As regards the former, the results require replication varying context
and methodology. Further vignette-based studies should explore other types of scenario and
could, by aid of different illustrations of the robot agents (following e.g. Malle et al., 2016),
investigate whether anthropomorphism has an effect on lying attributions and moral evalu-
ation. Moreover, lab experiments with deceptive embodied robots (see e.g., Dragan et al.,
2015; Wagner, 2016) should be conducted to test the external validity of the results reported
above. On the theoretical front, it is key to investigate the normative consequences of the pre-
sented ﬁndings (see Bok, 1999). Given that robots are judged as capable of lying, it should be
explored whether, and if so, under what conditions, it is morally acceptable to equip artiﬁcial
agents with capacities of this sort. One particularly important concern regards the possibility
of Sparrow’s “responsibility gaps” (Leveringhaus, 2018; Sparrow, 2007): If robots are judged
M. Kneer / Cognitive Science 45 (2021) 11 of 15
as capable of lying and are attributed—contrary to what Sparrow and others presume—blame
for this behavior, human agents who instrumentalize them in a wide range of domains from
deceptive marketing to political smear-campaigns might be judged less blameworthy than
they actually are (which is exactly what Kneer & Stuart, 2021, ﬁnd in recent experiments).
Consequently, it must be explored whether it might be appropriate to create norms, standards,
or possibly even laws, to restrict the use of actively deceptive robots in certain domains.
In a preregistered experiment, I explored the folk concept of lying for both human agents
and robots. Consistent with previous ﬁndings for human agents, the majority of participants
think that it is possible to lie with a true claim, and hence in cases where there is no actual
deception. What seems to matter more for lying are intentions to deceive. Contrary to what
might have been expected, intentions of this sort are equally ascribed to robots as to humans.
It thus comes as no surprise that robots are judged as lying, and blameworthy for it, to similar
degrees as human agents. Future work in this area should attempt to replicate these ﬁndings
manipulating context and methodology. Ethicists and legal scholars should explore whether,
and to what degree, it might be morally appropriate and legally necessary to restrict the use
of deceptive artiﬁcial agents.
This work was supported by a Swiss National Science Foundation Ambizione Grant
(PZ00P1_179912) and a Digital Society Initiative (University of Zurich) Fellowship.
Open Research Badges
This article has earned Open Data and Open Materials badges. Data and materials are
available at https://osf.io/a5cvu/.
1. Interestingly, however, context really does seem to matter. In contrast to the ascription
of epistemic states and intentions in moral contexts, people are much less willing to
ascribe artistic intentions (or the requisite beliefs and desires) to AI-driven agents. Even
in situations in which participants deem a painting made by an artiﬁcial agent art,they
are unwilling to say that the agent wanted to make a painting, believed it was making a
painting, or intentionally made a painting (Mikalonyt˙
e & Kneer, 2021). Curiously then
(yet consistent with the unwillingness to ascribe the requisite mental states), people think
that artiﬁcial agents cannot be artists even though their creations can be deemed art.
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4. One reviewer made the following interesting observation: In the false conditions, the
proportion of participants who deem the agent lying exceeds the proportion of partici-
pants who attribute an intention to deceive by about 10%. These participants might have
thus interpreted the agent’s behavior as what Wiegmann and Rutschmann (2020) call
an “indifferent lie.” Had the agent’s desire to deceive been rendered more salient in the
scenario or had the beneﬁt from lying been more explicit, the proportion of those who
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