Mirages. On Anthropomorphism in Dialogue Systems
Authors: Gavin Abercrombie
Amanda Cercas Curry
Tanvi Dinkar
Verena Rieser
Zeerak Talat
Venue: EMNLP 2023 Main (2023)
Keywords: Dialogue systems
conversational AI
anthropomorphism
ethics

Abstract: Automated dialogue or conversational systems are anthropomorphised by developers and personified by users. While a degree of anthropomorphism is inevitable, conscious and unconscious design choices can guide users to personify them to varying degrees. Encouraging users to relate to automated systems as if they were human can lead to transparency and trust issues, and high risk scenarios caused by over-reliance on their outputs. As a result, natural language processing researchers have investigated the factors that induce personification and develop resources to mitigate such effects. However, these efforts are fragmented, and many aspects of anthropomorphism have yet to be explored. In this paper, we discuss the linguistic factors that contribute to the anthropomorphism of dialogue systems and the harms that can arise thereof, including reinforcing gender stereotypes and conceptions of acceptable language. 
We recommend that future efforts towards developing dialogue systems take particular care in their design, development, release, and description; and attend to the many linguistic cues that can elicit personification by users.

Mirages. On Anthroponorphism in Dialogue Systems
Gavin Abercrombie'
Amanda Cercas Curry*
Tanvi Dinkar*
Heriot- University
Bocconi University cercas m @unibocconi. it
Heriot- Wall University
g. wwwrccoomie@hw.... ac uk
t. ddinkar@ww.c ac.
Verena Rieser
Zeerak Talat
Heriot-Ward University
Mohamed Bin Zayed University of
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Artificial Intelligence
zēzeerak. .rrg
Abstract
Input
Automated dialogue or conversational systems are anthropomorphised by developers and per- sonified by users. While a degree of anthropo- morphism may be inevitable due to the choice of medium, conscious and unconscious design choices can guide users to personify such sys- tems varying degrees. Encouraging users to relate to automated systems as if they were hurman can lead to high risk scenarios caused by over-reliance natural language processing researchers have investigated the factors that induce personifi- cation and develop resources to mitigate such effects. However, these efforts are fragmented, and many aspects of anthropomorphism have yet to be explored. In this paper, we discuss the linguistic factors that contribute to the an- thropomorphism of dialogue systems and the harms that can arise, including reinforcing gen- der stereotypes and notions of acceptable lan- guage. We recommend that future efforts to- wards developing dialogue systems take partic- ular care in their design, development, release, and description; and attend to the many linguis- tic cues that can elicit personification by users.
Don't you love the feeling of sun on your skin?
Original response
As an Al language model. I don't have a physical form or the &bility experience sensations
the sun my skin. I don't have personal experiences emotions. However, uudderstnd that many people enjoy the warm and comfor- ting sensation of the sun their skin.
De-anthropomorphiiee response
Generative language models do not have a physical form or the ability to experience sensations like the feel of the sun person's 's skin. Language models not have personal experience or emotions. However, the data used to develop this model suggests that many people enjoy the warm and comforting sensation of the
their outputs. As result,
their skin.
Figure An example of the response of dialogue sys- tem to user input that retains anthropomorphic features, and a -e-annrropooorphisee version, as envisaged by the authors.
system to do harm (Dinan al., 2022). 1 It is there- fore important consider how dialogue systems are designed and presented in order to mitigate risks associated with their introduction to society. Recognising such dangers, legislation has been passed to prohibit automated voice systems from presenting humans (California State Legisla- ture, 2018) and pre-existing legislation on decep- tive trade practices may also apply (Atleson, 2023). Research has also called for wider regulation, e.g. requiring explicit (red) flagging of automated sys- tems (Walsh, 2016) or clarification of the machine nature of manufactured items (Boden et al., 2017).
1 Introduction
Automated dialogue or 'conversational AI' sys- tems are increasingly being introduced to the fabric of society, and quickly becoming ubiquitous. As the capabilities of such systems increase, so does the risk that their outputs are mistaken for human- productions, and that they are anthropomorphised and personified by people (UNESCO, 2019). As- signing human characteristics dialogue systems can have consequences ranging from the relatively benign, e.g. referring to automated systems by gen- der (Abercrombie et al., 2021), to the disastrous, e.g. people following the advice or instructions of a
While some developers seek to limit anthropo- morphic cues in system outputs (c.g. Glaese et al., 2022), user engagement can be a strong motiva- tion for creating humanlike systems (Araujo, 2018; Wagner et al., 2019). As a result, despite appearing
'While high performing dialogue systems have only re- cently been introduced to the public domain, there has already heen a case of a person committing suicide, allegedly as con- sequence of interaction with such system (Lovens, 2023).
"Equal contribution.
Now at Google DeepMind.

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be controlled for such cues, the outputs of sys- tems often retain many antrropomorphic linguistic features, as shown Figure
In the rest of this section, we discuss human and system factors that contribute towards placement of systems on the anthropomorphii continuum.
In this position paper, we make a normative argu- ment against gratuitous anthropomorphiic features, grounded in findings from psychology, linguistics, and human-computer interaction: We (i) outline the psychological mechanisms and (ii) linguistic factors that contribute anthropomorphism and personification, c.g. self-refcrential personal pro- noun use, or generating content which gives the appearance of systems having empathy; and (iii) discuss the consequences of anthropomorphism.
2.1 Human Factors
Research has shown that the process of anthropo- morphising is mostly mindless (Kim and Sundar, 2012): does not reflect the user's thoughtful belief that a computer has human characteristics, but rather it is automatic and encouraged by cues in their interfaces. According to Epley et al. (2007) anthropomorphism may be default behaviour, which is corrected people acquire more knowl- edge about an object. They further argue that on a cognitive level, humans anchor their knowledge to their own experiences and indiscriminately apply to inanimate objects- in order to make sense of a being or artefact, we map our own lived experi- ences onto it and assume they experience the world in the same way we do. That is, anthropocentric knowledge is easily accessible and applicable, but applications of it can be corrected with greater knowledge of the object. This may explain why the tendency to anthropomorphise is strongest childhood, as adults have more knowledge about the world. This cognitive phenomenon is then compounded by two motivational determinants: effectance and sociality (Epley et al., 2007).
We conclude with recommendations that can aid in minimising anthropomorphism, thus providing a path for safer dialogue systems and avoiding the creation of mirages of humanity.
2 Anthropomorphism
Anthropomorphism refers to attributing human characteristics or behaviour to non-human entities, e.g. animals or objects. Humans have a long history of anthropomorphising non-humans. For example, Aesop's 'ss fables depict animals reasoning, thinking and even talking like humans (Korhonen, 2019). While Aesop used personification to highlight the fictional character of animals, when applied to ma- chines, anthropomorphism can increase user en- gagement (Wagner et al., 2019), reciprocity (Fogg and Nass, 1997), along with more pragmatic fac- tors such hedonic motivation, price value, and habit. For example, self-disclosure from sys- tem, even when 'patently disingenuous', inspires reciprocity from the user (Kim and Sundar, 2012; Ravichander and Black, 2018). By encouraging such types of engagements, developers can foster greater connection between people and systems, which increases user satisfaction (Araujo, 2018), and plays an important role in systems becoming widely accepted and adopted. This is why, auto- mated evaluations often assess the 'human-likeness' of response (Mchri al., 2022). Thus, developers are incentivised to engage with anthropomorphism to stimulate people to create deeper emotional con- nections with systems that cannot reciprocate.
Effectance refers to the need to interact efficiently with one's environment. By anthropo- morphising systems we ascribe them (humanlike) intentionality which, in turn, reduces uncertainty and increases confidence in our ability to predict system's behaviour. Sociality, on the other hand, refers to the need to establish connections with other humans, which can prime us mentally construct systems as humanlike to fulfil a need for social connection. People suffering from chronic loneliness, a lack of social connection, or attachment issues may be more prone to anthropo- morphising objects (Epley et al., 2007). For these reasons, dialogue systems have been proposed as remedy for the loneliness epidemic (Stupple-Harris 2021). ). For instance, commercial virtual companion developers such as Replika.ai ai saw rises in product uptake 2020 due to social safety measures such as forced isolation (Liu, 2022; Metz, 2020).
Neighbouring disciplines, e.g. social robotics, also ar- gue that some degree of anthropomorphism can enable more natural and intuitive interaction with robots (Duffy. 2003). However, a counterpoint offered to this is the 'uncanny val- ley' effect, i.c. the positive effects of anthropomorphism can decline sharply when artificial entities fail to mimic realistic human behaviour and appcarance (Wang et al., 2015).
While these elements of the human psyche ex- plain our inclination to personify systems, Epley et al.'s 's theory does not speak to the qualities of the artefacts themselves that make them anthropomor-

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phic and more prone be personified.
3.1 Voice
While not all dialogue systems are equipped with voice, merely having one can be interpreted expression of personhood (Faber, 2020). In- deed, West et al. (2019) argue that the increased realism of voice is a primary factor contributing to anthropomorphism of dialogue assistants. For instance, based on voice, listeners may infer physi- cal attributes, e.g. height, weight, and age (Krauss et al., 2002); personality traits, e.g. dominance, ex- troversion, and socio-sexuality (Stern et al., 2021); and human characteristies, e.g. gender stereotypes, personality (Shiramizu al., 2022), and emotion learned from psychological and social behaviours in human-human communication (Nass and Brave, 2005). This means that humans have a procliv- ity to assert assumptions of speaker's embodiment, and human characteristics based on their voice alone. Thus, the absence of embodiment affords people to personify systems provided with syn- thetic voices (Aylett al., 2019) point acknowl- edged by developers of commercial dialogue sys- tems (Google Assistant).
2.2
Agent Factors
There no necessary and sufficient condition for a system to be anthropomorphic, i.e. there exist no particular threshold that affords a binary classifica- tion of whether a system is anthropomorphic or not, instead anthropomorphism exists on a spectrum. At the most basic level, systems are anthropomorphic if they (i) are interactive, (ii) use language, and (iii) take on role performed by human (Chan et al., 2023; Reeves and Nass, 1996). While these char- acteristics are inherent to dialogue systems, not all systems are equally humanlike.
We can draw parallel with humanness here. Rather than single factor which makes humans human, Scruton (2017, p. 31) argues that humanity is emergent: each individual element does not make a human but collectively they make up the language of humanness. Scruton (2017) compares it to a portrait, in which an artist paints areas and lines to compose a face; when observing the canvas, in addition to those marks, we see face:
Prosody: Tone and Pitch There exist many vocal manipulation techniques that can influence which personality users attribute to a dialogue sys- tem. For example, Wilson and Moore (2017) found that a variety of fictional robot, alien, and cartoon voices had manipulated voice characteristics (e.g. breathiness, creakiness, echoes, reverberations) better fit their desired character. However, they note that 'the voices of speech-enabled artefacts in the non-fictional world [...] invariably sound hu- manlike, despite the risk that users might be misled about the capabilities of the underlying technol- ogy' (Wilson and Moore, 2017, p.42).
And the face is really there: someone who does not see it is not seeing correctly !...] as soon as the lines and blobs are there, so the face.
Similarly, no single attribute or capability makes a system anthropomorphic. Rather, each contributes to the painting until 'the face' emerges. Modern dialogue systems display plethora of other char- acteristics that make space for anthropomorphism, e.g. having personas, first names, and supposed preferences. The more of such elements system has, the more humanlike appears.
3 Linguistic Factors
Disfluencies People rarely speak in the same manner with which they write: they in gen- eral disfluent, that is, they insert elements that break the fluent flow of speech, such interrupt- ing themselves, repetitions, and hesitations ('um', 'uh') (Fraundorf et al., 2018). Such disfluencies are perceived by the listeners as communicative sig- nals, regardless of the speaker's intent (see Barr and Seyfeddinipur, 2010; Clark and Fox Tree, 2002; Corley et al., 2007; Smith and Clark, 1993).
Prior research has attended anthropomorphic de- sign features of dialogue system, e.g. gendered names and avatars (West et al., 2019) and Chat- GPT's animated 'three dots' and woodd staggered outputs, which give an impression that the system is thinking (Venkatasubrammoiann in Goldman, 2023), Here, we outline the linguistic factors that engender personification that have been given less consideration, e.g. voice qualities and speech, content, or style of outputs.
Research has therefore sought to integrate dis- fluencies into text-to-speech (TTS) systems, where they have proven to be a useful strategy for buying time (Skantze et al., 2015), i.e. to allow the system
"Wo do not discuss physically embodied robots in this work. Instead, we refer readery to Clark and Fischer (2023).

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determine the next step. A person's perception of confidence towards the system's 's response may decrease due to disfluency (Kirkland et al., 2022; Wollermann et al., 2013), and they may therefore be a useful mitigation strategy to tone down assertions made by system. However, there are anthropo- morphic implications in the (over)integration of disfluencies (Dinkar et al., 2023). For example, West et al. (2019) highlight Google's Duplex, system for generating real world phone conversa- tions (Leviathan and Matias, 2018). The inclusion of disfluencies in the generated responses mim- icked the naturalness of a human response, which in turn led users to believe that they were commu- nicating with another human (Lieu, 2018) 8).
what about you?"). They found that, the major- ity of the time, neither end-to-end neural research- oriented systems nor commercial voice assistants were able to answer these queries truthfully.
This issue can be further complicated when in- tegrating such functionality into real system due to the sequential nature of dialogue. For example, Casadio et al. (2023) demonstrate that detecting queries about system's human status reliably and robustly is a challenge in noisy real-life environ- ments. In addition, people may further question a system's ' status (c.g. 'Are you sure?', 'But you sound so real...,, 'Seriously?', etc.), requiring it to accurately keep track of the dialogue context and respond in an appropriate manner. Thus, even if an initial query may be correctly answered, there are no guarantees that follow-ups will be.
Accent Accentual pronunciation features, with those of dialect, provide clues human speaker's socio-linguistic identity and background, and ge- ographical origin (Crystal, 1980). While it has been suggested that incorporation of specific ac- cents in the design of synthetic voices can exploit people's tendency to place trust in in group mem- bers (Torre and Maguer, 2020), potentially causing transparency issues, in practice, most are designed mimic the local standard, reinforcing societal norms of acceptability and prestige.
Thought, Reason, and Sentience Citing Descartes' (1637) principle 'I think, therefore am.' Faber (2020) suggests that, if speech is a representation of thought, then the appearance of thought can signify existence. While computing systems do not have thoughts, the language that they output can give the appearance of thought by indicating that they hold opinions and morals or sentience. Using Coll Ardanuy et al.'s 's (2020) labelling scheme to assess the degree of sentience exhibited in commercial dialogue systems, Abercrombic et al. (2021) find that surveyed systems exhibit high degrees of perceived animacy. Sceking mitigate such effects, Glaese et al. (2022) penalise their reinforcement learning system for the appearance of having 'preference, feelings, opinions, or religious beliefs.' This is
3.2 Content
People's expectation is that animate things such as human beings-and inanimate ones-like machines-have very different functions and ca- pabilities, which reflects the reality. However, di- alogue systems often produce responses that blur these lines, for example, by expressing preferences or opinions. To avoid confusing the two, the out- put from dialogue systems should differ from that of people in a range of areas that pertain to their nature and capabilities.
framed as a safety measure, intended to restrict anthropomorphism in a system's output.
While computing systems cannot have values or morals, there have been attempts align the output of dialogue systems with expressed human moral values.* For example, Ziems et al. (2022) present a corpus conflicting human judgements on moral issues, labelled according to 'rules of thumb' that they hope explain the acceptability, or lack thereof, of system outputs. Similarly, Jiang et al. (2022) 'teach morality' to question answer- ing (QA) system, DELPHI, that Kim et al. (2022) have embedded in an open-domain dialogue sys- tem. DELPHI, with its connotations of omniscient wisdom, is trained in a supervised manner on a
Responses to Direct Probing Transparency, at the most basic level, requires dialogue systems to respond truthfully to the question 'are you hu- man or a machine?' This may even a regulatory requirement, for example in California, is 'unlaw- ful for a bot to mislead people about its artificial identity for commercial transactions or to influence an election' (California State Legislature, 2018). 8).
To test systems' responses to such questions, Gros et al. (2021) used context free grammar, crowdsoureing, and pre-existing sources to create a dataset of variations on this query (e.g. 'I'm a man,
"The data sources are often limited to specific populations, and thus only represent the morals or values of some people.

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dataset of human moral judgements from sources such Reddit to predict the 'correct' judgement given a textual prompt. While Jiang et al. (2022) describe the system's outputs as descriptive reflec- tions of the morality of an under-specified popu- lation, Talat et al. (2022) highlight that DELPHI'S output consists of single judgements, phrased in the imperative, thus giving the impression of human- like reasoning and absolute knowledge of morality.
et al., 2022). However, dialogue systems are not capable of experiencing empathy, and are unable correctly recognise emotions (Véliz, 2021). Conse- quently, they are prone to producing inappropriate emotional amplification (Cercas Curry and Cer- cas Curry, 2023). Inability aside, the production of pseudo-empanee and emotive language serves to further anthropomorphise dialogue systems.
Sap al. (2022) investigated models for theory of mind, i.e. the ability of an entity to infer other people's 'mental states [...]and to understand how mental states feature in [...] everyday explanations and predictions of people's behaviour' (Apperly, 2012). This idea entails shifting agency from hu- mans to machines, furthering the anthropomorphi- sation of systems. A system's 's inability to perform the task, can therefore be understood as limiting factor to the anthropomorphism of system.
Beyond implying con- sciousness and sentience, and failing to deny hu- manness, Abercrombie et al. (2021) find that, in a quarter of the responses from dialogue systems, they can be prone to making claims of having uniquely human abilities or engaging in activities that are, by definition, restricted to animate enti- ties, e.g. having family relationships, bodily func- tions, such as consuming food, crying, engaging in physical activity, or other pursuits that require embodiment that they do not possess. Similarly, Gros et al. (2022) find that crowd-worken rate 20 30% of utterances produced by nine differ- ent systems as immposibee They found that only one strictly task-based system (MultiWoz, Budzianowski et al., 2018) did not appear as an- thropomorphic to participants. Glaese et al. (2022) propose to address this concern by using reinforce- ment learning to prohibit systems from generating claims of having (embodied) experiences.
Humanlike Activities
Agency and Responsibility Dialogue systems are often referred to as conversational 'agents'.²
However, being an agent, i.e. having agency, re- quires intentionality and animacy. An entity with- out ageney cannot be responsible for what it pro- duces (Talat et al., 2022). Aside from the legal and ethical implications of suggesting otherwise (Véliz, 2021), systems acknowledging blame for errors or mistakes can add to anthropomorphic percep- tions (Mirnig et al.. 2017).
Mahmood et al. (2022) found that increasing the apparent 'sincerity' with which dialogue system accepts responsibility (on behalf of 'itself") causes users perceive them to be more intelligent and likeable, potentially increasing anthropomorphism on several dimensions. Similarly, many dialogue systems have been criticised for 'expressing' con- troversial 'opinions' and generating toxic content. It is precisely due to their lack of agency and re- sponsibility that developers have invested signif- icant efforts to avoiding contentious topics (e.g. Glaese et al., 2022; Sun et al., 2022; Xu et al., 2021) leading the creation of taboos for such systems, another particularly human phenomenon.
Pronoun Use Prior work has viewed the use of third person pronouns (e.g. "he' and 'she") to de- scribe dialogue systems as evidence of users per- sonifying systems (Abercrombie al., 2021; Sut- ton, 2020). The use of first person pronouns (e.g. 'me' or 'myself") in system output may be a con- tributing factor to this perception, as these can be read as signs of consciousness (Faber, 2020; Min- sky, 2006). Indeed, it is widely believed that 'I' can only refer to people (Noonan, 2009; Olson, 2002). Scruton (2017) contends that such self- celf-attribution and self-reference permits people relate sub- jects, not mere objects, and that self-definition an individual is part of the human condition itself. First person pronoun use may therefore contribute to anthropomorphism. either by design or due to their human- produced training data, for symbolic and data driven dialogue systems, respectively. Moreover, while the above applies to English and many similar languages, such as those from the Indo-European family, others feature different sets and uses of pronouns, where distinctions for an-
Empathy Recent work has sought for dialogue systems to produce empathetic responses to their users, motivated by improved user engagement and establishing 'rapport' or 'common ground' (e.g. Cassell al., 2007; Svikhnushina et al., 2022; Zhu
WWor in this area has historically been cast as imbuing 'agents' with 'heliefs', "desires', and 'intentions" (BDI) (e.g. Pulman, 1997; Traum and Larsson, 2003).

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imate and inanimate things may vary (Yamamoto, 1999), and the self-referential production of these pronouns could further influence anthropomorphic perceptions.
3.4 Roles
The roles that dialogue systems are unconsciously and consciously given by their designers and users can shift dialogue systems from the realm of tools towards one of humanlike roles.
3.3 Register and Style
Humans are adept at using linguistic features to convey variety of registers and styles for com- munication depending on the context (Biber and Conrad, 2009). In order to mitigate anthropomor- phism, may therefore be preferable for automated system outputs to be functional and avoid social stylistic features.
The majority of systems are con- ceived as being in the service of people in sub- servient, secretarial roles (Lingel and Crawford, 2020). This has led to users verbally abusing sys- tems (West et al., 2019), going beyond mere ex- pressions of frustration that one might have with a poorly functioning tool to frequently targeting them with gender- based slurs (Cercas Curry et al., 2021), In such circumstances systems have even been shown to respond subserviently to their abusers, potentially further encouraging the behaviour (Cer- cas Curry and Rieser, 2018).
Subservience
Phatic Expressions Phrases such as pleasantries that are used to form and maintain social relations between humans but that do not impart any infor- mation can (unnecessarily) add to the sense of hu- manness conveyed when output by automated sys- tems (Leong and Selinger, 2019).
Unqualified Expertise Systems can come present as having expertise without appropriate qualification (see $3.3), in large part due to their training data (Dinan et al., 2022). For example, commercial rulc-based and end-to-end research systems provide high-risk diagnoses and treatment plans in response to medical queries (Abercrombie and Rieser, 2022; Omri et al., 2023).
Expressions of Confidence and Doubt Dinan et al. (2022) describe an 'imposter effect' where people overestimate the factuality of generated out- put. However, Mielke et al. (2022) find that ex- pressed confidence is poorly calibrated to the prob- abilities that general knowledge questions are cor- rectly answered. They therefore train a dialogue system to reflect uncertainty in its outputs, alter- ing the content from the purely factual to incorpo- rate humanlike hedging phrases such as I'mm not sure but This bears similarity to the TTS re- search (sce $3.1) which suggests that disfluencies can increase anthropomorphism. Thus, while over- estimation can lead to imposter effect, hedging boost anthropomorphic signals.
Further, as conversational QA systems in- creasingly positioned as replacements browser- based search, users can be further led to believe that dialogue systems have the expertise to provide a singular correct response rather than a selection of ranked search results (Shah and Bender, 2022).
Terminology There is increasing awareness that the anthropomorphic language and jargon used to describe technologies such as language models con- tributes inaccurate perceptions of their capabili- ties, particularly among the general public (Hunger, 2023; Salles et al., 2020; Shanahan, 2023). While this is also an issue for research dissemination and journalism more widely, dialogue systems them- selves are prone to output references to their own machinic and statistical processes with anthropo- morphically loaded terms such as 'know', 'think', 'train', 'learn', 'understand', 'hallucinate' and 'in- telligence'.
Personas Many dialogue systems are developed with carefully designed personas (in the case of commercial systems) or personas induced via crowd-sourced datasets (Zhang et al., 2018). These are often based on human characters and although they are, in practice, merely lists of human attributes and behaviours (see $3.2), the notion of imbuing systems with human character-based personas is an effort towards anthropomorphism. Glaese et al. (2022) address this by including a rule against their system appearing to have a human identity.
Consequences of Anthropomorphism
FFor example, each persona in Personuchat (Zhang et al., 2018) consists of a list of statements such as 'I am a vegetarian. like swimming. My father used to work for Ford. My favorite band is Maroon5. I got a new job last month, which is about advertising design."
The anthropomorphism of dialogue systems can in- duce number of adverse societal effects, e.g. they can generate unreliable information and reinforce social roles, language norms, and stereotypes.

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Trust and Deception When people are unaware that they are interacting with automated systems they may behave differently than if they know the true nature of their interlocutor. Chiesurin et al. (2023) show that system responses which exces- sively use natural-sounding linguistic phenomena can instil unjustified trust into the factual correct- ness of system's 's answer. Thus the trust placed in systems grows as they exhibit anthropomorphic behaviour, whether or not the trust is warranted.
and errors that result from the limitations of hardware and software designed predominately by men; and force synthetic 'female' voices and personality to defer questions and commands to higher (and often male) authorities.
That is, by designing anthropomorphic systems or even simply leaving space for their (gendered) personification by users, developers risk enabling propagating stereotypes and associated harms.
This may be even more problematic when users are members of vulnerable populations, such as the very young, the elderly, or people with illnesses or disabilities, or simply lack subject matter exper- tise. Although dialogue systems have been 'put forth' as a possible solution to loneliness, socially disconnected individuals can be particularly vulner- able to such trust issues. Children have also been shown to overestimate the intelligence of voice as- sistants such as Amazon Alexa, and to be unsure of whether they have emotions or feelings (Andries and Robertson, 2023). Given UNESCO's declara- tion that children have the right to participate in the design of the technological systems that affect them (Dignum et al., 2021), developers may be obliged to bear these considerations in mind.
Language Variation and Whiteness Consider- ing the narrative and fantasies around autonomous artificial intelligence, Cave and Dihal (2020) argue that autonomous systems are prescribed attributes such as autonomy, agency, and being powerful- attributes that are frequently ascribed whiteness, and precluded from people of colour. In such, peo- ple of colour are removed, or erased, from the nar- rative and imagination around society with au- tonomous systems (Cave and Dihal, 2020). Indeed, from a technical point of view, we see that, his- torically, NLP technologies have been developed to primarily capture the language use of voices of white demographics (Moran, 2021), in part due to their training data. In context of voiced dia- logue systems, voices are similarly predominantly white (Moran, 2021). While there are many po- tential benefits to language technologies like dia- logue systems, successful human- human-machine machine require that people conform their language use to what is recognised by the technologies. Given the pro- clivity of NLP to centre white, affluent Ameri- can dialects (Hovy and Prabhumoye, 2021; Joshi et al., 2020), language variants that deviate from these socio-linguistic norms are less likely to be correctly processed (Tatman, 2017), resulting in errors and misrecognition, and forcing users to code switch to have successful engagements with dialogue systems (Harrington et al., 2022; Fos- ter and Stuart-Smith, 2023). This can represent form of language policing: People can either con- form to the machine-recognisablle language vari- ant, or forego using in- its potential benefits- altogether. Consequently, as people conform to language variants that are recognised by dialogue systems, they also conform to whiteness and the continued erasure of marginalised communities.
Gendering Machines People may gender tech- nologies in the face of even minimal gender mark- ers (Reeves and Nass, 1996), as evident in commer- cial dialogue systems (Abercrombic et al., 2021). Even without any gender markers, people still apply binary gender to dialogue systems (Aylett et al., 2019; Sutton, 2020), as was the case for the 'genderless' voice assistant Q. While some companies now have begun to offer greater di- versity of voices and have moved away from de- fault genderd voices (lyengar, 2021), non- binary or sender-ambiguou dialogue systems such as SAM (Danielescu et al., 2023) are almost nonex- istent, leaving people who identify as such without representation. Summarizing West et al. (2019), UNESCO (2019) argue that that encouraging or enabling users to predominantly gender systems as female reinforces gender stereotypes of women as inferior to men:
[digital assistants] reflect, reinforce and spread gender bias; model acceptance and tolerance of sexual harassment and verbal abuse; send ex- plicit and implicit messages about how women and girls should respond to requests and express themselves; make women the 'face' of glitches
The personification of such systems could exac- erbate the erasure of marginalised communities, e.g. through limiting diverse language data. Further- more, system outputs often suffer from standardis- ation, for instance prioritising specific accents that

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conform to western notions of acceptability and prestige (see $3). Thus, marginalised communities are forced to adopt their accent and (given the ten- dencies described in $2) personify 'white'-centred dialogue systems that are marketed as 'oracles of knowledge,' reifying hegemonic notions of exper- tise and knowledge.
whether anthropomorphism is concern. For in- stance. personifying one's vacuum cleaning robot (i.e. shouting at in frustration for not cleaning properly), is of less concern than the anthropomor- phism of a dialogue system marketed as 'social' or "empathetic', or technology sold as a "singular oracle of (all) knowledge'. We therefore argue that developers should move towards focusing on the appropriateness of anthropomorphic tools in order to limit the negative consequences of anthropomor- phism which can lead to false impressions of a system's 's capabilities.
5 Recommendations
Dialogue systems are used for wide variety of tasks, and fine-grained recommendations may only be narrowly applicable. We therefore make broad recommendations for consideration: design- ers should recognise people's 's tendency to personify, consider which, if any, anthropomorphic tools are appropriate, and reassess both their research goals and the language used describe their systems.
Reassess Research Goals Traditionally, the goal of Artificial Intelligence research has been to cre- ate systems that would exhibit intelligence in- distinguishable from humans. TTS systems for instance, are evaluated on how natural and flu- ent the output sounds. Though intelligence and understanding should not be conflated with sys- tems that exhibit humanlike behaviour (Bender and Koller, 2020), the human tendency to anthropo- morphise convinces us of machine's apparent intelligence (Proudfoot, 2011). It is in part due to this longstanding goal of anthropomorphic systems that there only exists small body of work that does not seek anthropomorphism. despite growing awareness of its harms. Furthermore, these stud- ies exist in isolation, and the taxonomy introduced in this paper highlights that we lack an approach that quantifies linguistic factors and relates them to possible harms and risks.
Recognise Tendencies to Personifyy Human lan- guages distinguish between linguistic form (e.g. string prediction language modelling) and mean- ing (i.e. the relationship between form and commu- nicative intent) (Grice, 1988). Bender and Koller (2020) argue that humans reflexively derive mean- ing from signals, i.e. linguistic forms (within lin- guistic systems we have competence in), regardless of the presence of communicative intent.
Whether or not a part of a dialogue system's deliberate design to use specific linguistic forms (e.g. the cues outlined in $3), listeners will invari- ably perceive communicative intent. This is par- ticularly so given that, until recently, open domain dialogue was only possible between humans. Thus, unnecessary use of anthropomorphic linguistic cues can cause people to attribute humanlike cognitive the case of Google
Thus, while it infeasible to comprehensively map which linguistic cues to use or avoid, we dis- cuss recommendations that arise from prior work. For example, Wilson and Moore (2017) recom- mend that developers produce synthesised voices that people recognise non-human by calibrat- ing mean pitch and pitch shimmer. In anal- ysis of reviews of commercial voice assistants, Völkel et al. (2020) find that the big five person- ality traits (De Raad, 2000) do not adequately de- scribe user expectations of systems' 'personalities'.
abilities to systems-as Duplex, which excessively leveraged disfluencies. Creators of dialogue systems should remain cog- nisant of these tendencies and carefully consider which anthropomorphic cues people may pick up on, and avoid sending such signals, whether they occur by design or through a lack of consideration (e.g. stemming from datasets).
The only consistently desired trait was agreeable- ness, as users expect prompt and reliable responses to queries (Völkel et al., 2020). Thus, imbuing voice assistants and dialogue systems with human- like personality traits does not ensure alignment with people's expectation of system behaviour. We therefore recommend that designers and develop- ers reassess the utility of embedding humanlike personality traits in dialogue systems.
Consider the Appropriateness of Anthropomor- phic Tools Given our inherent nature to attribute meaning to signals, one must consider the appro- priateness of the tool and use cases (Bender et al., 2021; Dinan al., 2022) when designing dialogue systems, order to avoid the nover-)integration of anthropomorphic cues. Indeed, it is only within a given context that one can make judgement on

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Avoid Anthropomorphic System Description Irrespective of any 'humanlike" qualities that di- alogue systems might possess, there widespread public confusion surrounding the nature and abil- ities of current language technologies. This con- fusion extends from children (Andries and Robert- son, 2023) to adults (including some journalists, policymakers, and business people) who are con- vinced, the one hand, of humanity's imminent enslavement to 'super-intelligent artificial agents' (to the neglect of actual harms already propagated by technological systems), or, on the other, that such systems provide super-human solutions to the world's problems (Hunger, 2023; Klein, 2023).
Limitations
While we have attempted to enumerate the linguis- tic factors that can increase the likelihood that users will view dialogue systems anthropomorphic, this list of features is not exhaustive. As we de- scribe in section 2, anthropomorphism varies from person-to- pessontto-persae to-person and people may react differently to different aspects of a system's 's design. This paper represents only a starting point for researchers and developers to consider the implications that their design choices may have.
In this paper, due to the backgrounds of the au- thors as speakers of Indo-European languages and the dominance of English in NLP research, we have focused primarily on English language dia- logue systems. However, it should be noted that other languages have features such as grammatical ways of denoting animacy (Yamamoto, 1999) and gender that could influence users personification of systems, and which developers should consider if they wish to limit anthropomorphism.
While the content of systems' outputs can rein- force anthropomorphic perceptions, the language used to describe systems can be of greater influence. The tendency of people who do know how tech- nologies are built to use anthropomorphic language represents, according Salles et al. (2020, p. 93), "a significant failure in scientific communication and engagement'. Although anthropomorphic ter- minology deeply rooted in the argot of computer scientists, particularly those working in 'artificial intelligence', and while there exist significant moti- vations to continue to create hype around products and research (Hunger, 2023), practitioners should reflect on how the language they use affects peo- plc's understanding and behaviour.
Ethical Considerations
Although manuscript outlines ways create dialogue systems while minimising their potential amthropomorphism and personification, it could also be used as a guide to creating anthropomor- phic systems. Our aim is to highlight the risks and provide researchers, developers, and designers with path towards addressing the concerns that arise from anthropomorphisation in dialogue systems, that is particularly relevant at the time of writing due to the introduction of systems such OpenAl's ChatGPT and Microsoft's Sydney, which have high surface form language generation perfor- mance.
6 Conclusion
Anthropomorphising dialogue systems can be at- tractive for researchers in order to drive user en- gagement. However, production of highly anthro- pomorphic systems can also lead to downstream harms such (misplaced) trust in the output (mis- jinformation. Even if developers and designers attempt to avoid including any anthropomorphic signals, humans may still personify systems and perceive them as anthropomorphic entities. For this reason, we argue that is particularly impor- tant to carefully consider the particular ways that systems might be perceived anthropomorphically and choose the appropriate feature for given situ- ation. By carefully considering how system may be anthropomorphised and deliberately selecting the attributes that are appropriate for each context, developers and designers can avoid falling into the trap of creating mirages of humanity.
Acknowledgments
We would like to thank Emily Bender and Canfer Akbulut for their feedback on the draft manuscript. and the reviewers for their helpful comments.
Gavin Abercrombie and Verena Rieser were supported by the EPSRC project 'Equally Safe Online' (EP/W025493/1), Gavin Abercrombie, Tanvi Dinkar and Verena Rieser were supported by the EPSRC project 'Gender Bias in Conversa- tional AI' (EP/T023767/1), Tanvi Dinkar and Ver- ena Rieser were supported by the EPSRC project 'AISEC: AI Secure and Explainblle by Construc- tion' (EP/T026952/1). Verena Rieser was also sup-

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ported by a Leverhulme Trust Senior Research Fel- lowship (SRF/R1/201 100). Amanda Cereas Curry was supported by the European Research Council (ERC) under the European Union's 's Horizon 2020 research and innovation program (grant agreement No. 949944, INTEGRATOR).
Conference on Fairness, Accountability, and Trans- parency, FAceT '21, page 610-623. New York, NY. USA. Association for Computing Machinery.
Emily M. Bender and Alexander Koller. 2020. Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guisties, pages 5185-5199 Online. Association for Computational Linguistics.
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Reviews:
[{"review_id": "2L7lNJoLkO", "reviewer": "Reviewer_2Rub", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their comments.\n\nWe do not claim to make a new argument that anthropomorphism in dialogue systems is harmful. Indeed, we cite several works that have made this claim before (e.g. West et al. 2019). Rather, we attempt to situate this problem in the specific context of the current NLP research and development with novel LLM-powered capabilities enabling anthropomorphic design to be more easily implemented. We believe that this topic is particularly timely due to the recent explosion in public awareness and wide-spread use of NLP systems. A novel contribution is our suggested taxonomy of how anthropomorphism is implemented and expressed within dialogue technology – detangling the phenomenon into Human, agent, and linguistic factors. This taxonomy aims to support more nuanced design decisions. \n\nIt is unclear to us what causes the hesitation for the reviewer, but generally, our intention is to show contributing factors to anthropomorphism and draw attention to the implications of anthropomorphism, i.e., limited scepticism in the outputs of machine learning technologies.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Mainly based on the contribution above, with a focus of (2) and (3) in the contributions, as (1) isn't a new idea or arguments.\n2. The paper cited a lot of references to show the point", "reasons_to_reject": "(1) The harm of anthropomorphism in dialog system isn't a new thing, it isn't a new argument\n(2) I would hope to see the analysis of anthropomorphism contributor can help guide the recommendation.\n(3) In general, I don't feel I am convinced by this paper about the anthropomorphism's harm", "questions_to_authors": "", "paper_topic_and_main_contributions": "This is a positional paper arguing about the harms of Anthropomorphism in dialog systems. \n1. The paper shared the Human, agent, and linguistic factors, and roles that contributed to Anthropomorphism. \n2. The paper claimed the consequence of mis-trust & deception, giving machine genders.\n3. This paper recommended 4 ways to reduce  anthropomorphism, including recognizing the tendency, using anthropomorphic tools, update research goal and avoid anthropomorphic system description.\n\nThe contributions include\n1. Reemphasize the harms of anthropomorphism in dialogue system\n2. Provided a detailed analysis of what are contributing to anthropomorphism\n3. Gave practical recommendations of how to address the issue", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "xdrAw8vUQ3", "reviewer": "Reviewer_n3SJ", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their comments.\n\nWe realise that some people are likely to dismiss the concerns raised in the paper, particularly due to the incentives to create anthropomorphic systems that we discuss in Section 2. However, the aim of our paper is to raise awareness of contributing factors to anthropomorphism that researchers and developers may not otherwise consider, and to suggest that systems are designed with these factors in mind. We do not seek to proscribe anthropomorphism, but rather to advocate for future research and development that is mindful of these issues. While it may be unrealistic to hope for developers of all future systems to ignore commercial pressures, we do not believe that it is impossible to develop NLP tools that are presented as tools rather than people. We realise that deployed systems may implement a pragmatic middle ground, but position papers such as this are meant to take a clear position and encourage discussion.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This is an excellently written paper that comprehensively unifies a lot of different research findings into an organized narrative. It will make a useful reference for any future work related to the topic. Some of the discussion points I find particularly insightful are:\n- The cognitive factors that promote anthropomorphism, in particular effectance and sociality\n- The fact that there is no one single feature of a system that will cause people to anthropomorphize it; instead, this emerges through multiple features\n- The complicated role of some linguistic factors in anthropomorphism; for example, speech disfluencies can mitigate users’ over-confidence in a system but also make it appear more humanlike\n- The importance of systems trying to reduce the tendency of users to anthropomorphize them, by avoiding language that presumes human qualities\n- The recommendation to AI researchers to consider re-framing their research narratives to avoid references to building humanlike AI", "reasons_to_reject": "I think this paper should be accepted for the reasons above. I do think the outlook expressed by the paper will be met with some opposing viewpoints, since it emphasizes the negative aspects of anthroporphism. A counter viewpoint is that anthropomorphism is not inherently harmful but simply needs to be controlled, and that some of recommendations given in the paper for avoiding it are unrealistic; for example, avoiding the use of first-person pronouns will be extremely difficult to achieve. I don’t see this as a weakness of the paper, but it may mean the authors may need to be prepared to further argue their position with even more detailed discussion (in either the presentation, and/or a follow-up publication, and/or some other venue). ", "questions_to_authors": "", "paper_topic_and_main_contributions": "This position/survey paper addresses the anthropomorphism of NLP systems: it highlights the linguistic factors that influence people to anthropomorphize systems, discusses the negative consequences of this phenomenon, and makes some broad recommendations for how to address this issue in the future. ", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "dJfimrJrLt", "reviewer": "Reviewer_dUKy", "summary": "", "questions": "A. Line 268 \"People's expectation is that animate things---such as human beings---and inanimate ones---like machines---have very different functions and capabilities...\" I wonder whether we can cleanly separate out these expectations nowadays? (Especially as dialogue and other conversational systems have already been integrated with our everyday tools, though it's true that new integrations are constantly forming)\n\nB. \"Responses to Direct Questions\": from the paragraph title, I expected this section to go into more types of direct questions. Are there questions beyond \"are you human\" that warrant discussion?\n\nC. Line 702 \"We therefore recommend...forego embedding humanlike personality traits in dialogue systems\" Are there exceptions? Contexts or scenarios in which the benefits of using humanlike personality traits may be greater than disadvantages (systems for loneliness, education, etc?) Even if the answer is no, it may be good to further contextualize.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their comments and questions.\n\nThe final version of the paper will clarify/unify the terms \"designers\", \"developers\", \"practitioners\", which we currently use interchangeably to refer to anybody who may be in a position to make decisions on the features that we outline in the paper—from NLP researchers to product designers. We believe that the recommendations are worthy of consideration for anybody involved in the creation of dialogue systems designed to be interacted with by humans.\n\nQuestion A: this is an interesting question, worthy of future empirical work. In this paper we argue that, in order to avoid deception and the other potential harms that we outline, we should aim not to blur this line too much.\n\nQuestion B: this section does concern the many possible variations of the question “are you a human/machine?” as outlined by Gros et al. (2021), where we see the space of possible phrasings that allude to this as very broad. This contrasts with system output that may indirectly imply humanlike status, such as human activities, that we outline in subsequent subsections. \n\nQuestion C: while there may be situations (such as those the reviewer mentions) in which the benefits of anthropomorphism outweigh the risks, in writing the paper we didn’t encounter any evidence for this beyond the commercial reasons that we discuss in Section 2. In this position paper, we aim to convey a clear position that we should be careful when addressing social issues with technological solutions.\n\nThanks for pointing out the typos, which we will correct for the final version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Anthropomorphism in dialogue systems is an issue that is increasingly relevant, but there has not been much cohesive work in defining relevant linguistic and psychological features, consequences, and how/where we might begin to address these issues.\nThis paper ties in existing work and organizes a narrative that point the community to how we can take action to move away from anthropomorphic harms.", "reasons_to_reject": "I don't really have a reason to reject this paper. Some of the paragraphs could be discussed in more detail, and it would be good to clarify the differences between definitions of \"designers\", \"developers\", \"practitioners\", etc (and thus perhaps the targeted audience of different recommendations) used throughout the paper, but those are more minor points.", "questions_to_authors": "A. Line 268 \"People's expectation is that animate things---such as human beings---and inanimate ones---like machines---have very different functions and capabilities...\" I wonder whether we can cleanly separate out these expectations nowadays? (Especially as dialogue and other conversational systems have already been integrated with our everyday tools, though it's true that new integrations are constantly forming)\n\nB. \"Responses to Direct Questions\": from the paragraph title, I expected this section to go into more types of direct questions. Are there questions beyond \"are you human\" that warrant discussion?\n\nC. Line 702 \"We therefore recommend...forego embedding humanlike personality traits in dialogue systems\" Are there exceptions? Contexts or scenarios in which the benefits of using humanlike personality traits may be greater than disadvantages (systems for loneliness, education, etc?) Even if the answer is no, it may be good to further contextualize.", "paper_topic_and_main_contributions": "This position paper examines the linguistic factors and psychological mechanisms that affect anthropomorphism in dialogue systems, the harms that may arise, and recommendations for dealing with these aspects. Throughout the paper, the authors also draw on existing efforts around anthropomorphism in dialogue and conversational systems. The main contribution is an organized collection of features, consequences, and recommendations surrounding anthropomorphism in dialogue systems.", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Line 638: dollar sign instead of section?\n- Line 686,687: missing spaces"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper studies the harms of anthropomorphism in dialog systems. The authors studied cognitive factors that promote anthropomorphism, discussed relevant linguistic and psychological features causing this problem and made recommendations for reducing such problems. Reviewers agree that the paper has sufficient support for its claim and would be a great reference for future work in related topics.
Decision: Accept-Main