Minding the source: toward an integrative theory of human–machine communication

Source: Eun-Ju Lee; Human Communication Research 50(2), journal article · 2023-09-13

Abstract According to the computers are social actors (CASA) paradigm, a dominant theoretical framework for research on human–computer interaction, people treat computers as if they were people. Recent studies on human–machine communication (HMC) and human–artificial intelligence (AI) interaction, however, appear to focus on when and how people respond to machines differently than to human agents. To reconcile this apparent contradiction, this study reviews critically the two overarching theoretical explanations proposed and tested in each respective tradition, the mindlessness account and the machine heuristic. After elaborating on several conceptual and operational issues with each explanatory mechanism, an alternative theoretical model of HMC is proposed that integrates both research traditions and generates predictions that potentially deviate from the dual-process models. Lastly, it is discussed how recent developments in AI technology invite modifications to the current understanding of HMC and beyond. Keywords: artificial intelligence, computers are social actors, human–machine communication, machine heuristic, mindlessness.

Once a sacred realm that belonged solely to human beings, communication is no longer an act that only humans can perform. Going beyond merely transferring messages between humans, machines can now serve as both mediators and communicators that actively participate in the construction of messages designed for either mass audience or an individual recipient (Sundar & Lee, 2022). If artificial intelligence (AI) refers to machines that are capable of performing tasks that require human intelligence (Turing, 1950), then the ability to communicate would certainly be among AI’s key qualifications.

Introduction. Arguably one of the most influential theoretical frameworks that guided HCI research for the past couple of decades, the CASA paradigm has established its robustness in numerous empirical investigations. Not as conclusive, however, is why people emit such seemingly irrational responses, treating lifeless machines as if they had gender, personalities, feelings, and intentions. Among several explanations proposed, mindlessness appears to have survived (Lee, 2008, 2010; Nass & Moon, 2000; Sundar & Nass, 2000). Just as people were more likely to comply with a request that contained “because” than the one without, regardless of how legitimate the following reason was (Langer et al., 1978), people mindlessly process and respond to incoming stimuli, thereby failing to adjust their default social reactions while dealing with an asocial being that evinces minimal human-like cues. In this view, mindlessness represents a general state of mind, rather than a variable. Boldly put, “individuals’ interactions with computers, television, and new media are fundamentally social and natural” (Reeves & Nass, 1996, p. 5), such that the tendency to equate mediated objects and experiences with real ones is expected to prevail, regardless of one’s age, education level, or technological proficiency. However, as subsequent studies focus largely on whether or not people exhibit social responses in yet another context with a different social rule, the mindlessness account has not been subjected to rigorous scientific scrutiny, but rather assumed as a legitimate theoretical explanation. Several issues seem to deserve our attention.

Apart from some exceptions (e.g., Johnson et al., 2004; Lee & Nass, 2002; Morkes et al., 1999), most CASA studies did not directly compare human–human interaction with HCI, but instead aimed to replicate the rules of interpersonal interaction within the HCI context. That is, insofar as people exhibited the same pattern of responses to computers as they would to humans, like favoring the computer with similar than dissimilar “personality” (Moon & Nass, 1996), it was less of a concern whether the degree to which people evince such reactions varies depending on the source. As AI comes to play increasingly diverse roles in communication processes in lieu of human actors, however, the pendulum has begun to swing in the opposite direction. Rather than confirming the “media equation,” much HMC research focuses on how the source attribution (human vs. machine) elicits different cognitive, affective, and behavioral reactions (e.g., Banas et al., 2022; Cloudy et al., 2021; Jones-Jang & Park, 2023; Waddell, 2018). This apparent shift makes sense in light of the surprise value in scientific research. When computers were unmistakably different from humans, it was surprising and thus worth reporting that people still failed to treat them differently. Contrarily, when “AI agents are developed to be more intimate, proximate, embodied, and humanlike” (Jones-Jang & Park, 2023, p. 1), it is surprising that people, nonetheless, differentiate between AI and human. In general, “empirical confirmation of surprising predictions constitutes a non-trivial advance” (Trafimow, 2013, p. 1). Despite the diverging orientations, HMC studies still tend to assume that people mostly engage in heuristic (vs. systematic) processing when dealing with machines, a relatively mindless mode of processing, and examine what specific heuristics are invoked when people make sense of machinegenerated messages. In so doing, the machine heuristic, which refers to generalized beliefs or “stereotypes about the operation of machines” (Sundar, 2020, p. 79), was proposed as an explanation for why people respond differently to an AI agent than its human counterpart. For instance, those with stronger beliefs in machine heuristic were more likely to entrust an AI (vs. human) agent with their personal information (Sundar & Kim, 2019), experienced lower levels of emotional involvement with the AI-authored (vs. human-authored) news article (Liu & Wei, 2018), and evaluated the news to be less biased and more credible when the uncivil comment section was moderated by a machine (vs. human) agent (Wang, 2021). The introduction of machine heuristic as a user characteristic that systematically alters the processes and outcomes of HMC represents a refinement of the CASA paradigm. That is, while inheriting the key proposition that people are generally mindless when interacting with machines, and thus prone to rely on heuristics, recent studies demonstrated that not all people treat humans and computers equally, and more As discussed above, recent studies on HMC that utilize the machine heuristic inherit the key proposition of the CASA paradigm by assuming heuristic (mindless) processing as a default mode, but they also depart from it by challenging the universality of social responses to machines.

Related work. Falsifiability of the mindlessness account First, mindlessness as a psychological state was not separately measured, nor was it directly tested. Instead, mindlessness was either presumed or inferred from people’s reactions to computers. That is, if people are mindful, they should not apply social rules to a computer—given that they did, they must have been mindless. It seems analogous to saying that if argument quality made a difference in persuasion outcomes, people must have engaged in systematic processing. However, to validate the causal connection between mindlessness and social responses to a computer, the most straightforward method would be to systematically vary the degree of mindlessness and see if it alters the extent to which people treat computers like humans in the predicted direction. In one such attempt, Lee (2008) manipulated the number of tasks imposed on the participants (one vs. two; Experiment 1) and the modality of computer output (speech vs. text; Experiment 2) and examined if those cognitively busier with multitasking or less attentive to the computer output while processing text (vs. voice) became more mindless, and thus, more likely to exhibit gender-typed responses to computers. Although the results supported the mindlessness predictions, even if they had been otherwise, one cannot determine whether it was because the manipulated stimuli (i.e., the number of tasks, the output modality) failed to induce the state of mindlessness, or because mindlessness failed to trigger social treatment of computers. Unless we measure mindlessness independently and test these two processes separately, the presumed connection between mindlessness and social responses to computers remains virtually unfalsifiable.

Monolithic treatment of social responses Second, it is not entirely clear if “social responses” can be treated as a single, monolithic entity. As an umbrella term, social responses refer to the reactions people naturally show toward other humans based on specific attributes (e.g., gender, personality, shared membership) or general norms (e.g., politeness, reciprocity), as documented in the communication or psychology literature. However, not all social responses are equally indicative of mindlessness. If one reported more positive affect, more positive evaluations of the interaction, and more positive regard for the computer when it randomly praised their performance (flattery), rather than provided generic feedback (Fogg & Nass, 1997b), such reactions might represent a mindless, “click-whirr” response (Cialdini, 2007) driven by the ego-enhancement motive. But what about discounting the flattering computer’s output as less trustworthy? In Lee’s (2010) study, for instance, participants found the flattering (vs.

Method. Instead, researchers have incorporated the machine heuristic (i.e., the extent to which people attribute machine-like qualities to computing systems) as either an explanatory mechanism or a contingent condition for proposed source effects (human vs. machine), or a lack thereof (e.g., Liu & Wei, 2018; Sundar & Kim, 2019; Wang, 2021).

Heuristic vs. personal beliefs vs. folk theory As a key construct in the heuristic–systematic model (HSM; Chaiken, 1980), heuristics refer to “relatively general rules (scripts, schemata) developed by individuals through their past experiences and observations” (p. 753). They represent rules of thumb or mental shortcuts that help reduce cognitive load associated with information processing and decision making (Metzger et al., 2010). An implicit assumption is that heuristics are shared widely and available to most people, such that they are readily activated when people are unable or unwilling (or both) to process incoming stimuli thoroughly. As such, if there is considerable variability in how strongly people embrace and use the machine heuristic in their evaluations and judgments, then it might be more appropriate to define it as personal beliefs about machines. Also, if machine heuristic represents lay people’s understanding or expectations of how machines perform and operate with what consequences, then it sounds virtually indistinguishable from “folk theories.” Defined as “intuitive, informal theories that individuals develop to explain the outcomes, effects, or consequences of technological systems” (DeVito et al., 2017, p. 3165), folk theories are believed to affect how individuals use the systems and respond to their outputs (Huang et al., 2022). If machine heuristic is measured by the degree to which people accept such statements as “As machines are precise, their prediction will be more reliable than humans’,” and “As machines are unbiased, machines’ guide will be more trustworthy” (S. Lee et al., 2023), which capture not just individuals’ beliefs about key traits of machines, but also those about causal consequences inferred from them, then it becomes indistinguishable from folk theories.

What does “machine-like” mean? As simple schemas or decision rules, heuristics encapsulate evaluative principles, such as “(message) length implies strength,” “more arguments are better arguments,” and “if other people think the message is correct then it is probably valid” (Chaiken, 1987, p. 4). Although variations exist, machine heuristic is often operationalized by how strongly people associate with machines some “machine-like” qualities, such as being objective, unbiased, accurate, error-free, unemotional, unyielding, and reliable (e.g., Banks et al., 2021; Cloudy et al., 2021; Sundar, 2020; Waddell, 2018; Wang, 2021). What merits note is that some studies (e.g., S. Lee et al., 2023; Sundar & Kim, 2019) operationalized the machine heuristic to include an explicit comparison with humans (e.g., “Machines are more trustworthy than humans,” “When machines perform a task, the results are more objective than when humans perform the same task”). If machine heuristic hinges on what separates machines from humans, then stronger beliefs in machine heuristic will inhibit, rather than facilitate, social responses to computers, directly challenging the mindless explanation for the CASA paradigm. Moreover, some researchers included specific behavioral outcomes that would reasonably follow certain beliefs about machines as part of the operational definition of machine heuristic (e.g., “Machines can handle information in a secure manner, therefore it is generally okay to disclose my private information [e.g., card number, address, phone number, etc.] to them.”; S. Lee et al., 2023). Apart from its double-barreled nature, if people indeed share more private information with the AI agent (behavioral outcome) because they believe “it is generally okay to disclose their private information to machines,” it seems dangerously close to a tautology.

Discussion. Second, it is unclear why beliefs in machine heuristic (mediator) should affect the evaluative outcomes (DVs) in both human and machine conditions alike. By definition, the machine heuristic captures people’s expectations and beliefs about machines. If so, beliefs in machine heuristic should be either (a) irrelevant to the evaluations of a human agent or (b) inversely associated with them, if people automatically compare machines with humans (see Lee et al., 2022 for how a human agent’s unsatisfactory performance enhances individuals’ ratings of AI). Either way, the association between the mediator (machine heuristic) and the DVs should vary depending on the agency type (machine vs. human), but the simple mediation model fails to uncover this interaction. In fact, studies found that beliefs in machine heuristic significantly altered the evaluations of an AI agent’s performance, but not a human’s (e.g., Lee et al., 2023b; Sundar & Kim, 2019). On the other hand, others highlighted the variability in people’s endorsement of machine heuristic as a contingent condition for the source effect to occur. In this scenario, the machine heuristic moderates the degree to which users differentially respond to machine and human agents (Figure 1B). Although it was not framed this way, the moderating effect of machine heuristic can be deemed as a special case of a confirmation bias— that is, those who believe that AI in general is more objective, accurate, and reliable would think more highly of and respond more favorably to a specific AI agent’s performance, be it AI journalist (Lee et al., 2023b), AI content moderator (Wang, 2021), or AI travel agent (Sundar & Kim, 2019). In contrast, those who do not espouse such beliefs would either treat machines and humans similarly or even evaluate machines more negatively than their human counterparts. expectations about the subject of a message. In such cases, factors known to facilitate message elaboration, such as the accuracy motivation or personal relevance, are likely to determine on which route their message processing will take place. Second, the current model is better suited for communication contexts where the source salience is relatively low. Even though people are told who/what the source is, reading a byline of a news article (“authored by algorithm”) is not the same as interacting with a chatbot in terms of how vividly people feel the machine’s presence. As such, the present model might be more useful in explaining one-to-many, rather than one-to-one, communication contexts (i.e., AI Curator and AI Creator; Sundar & Lee, 2022). When the agent type is sufficiently salient to begin with, individuals’ beliefs about AI will set in instantly, thereby shaping communication processes and outcomes. For instance, the agency type might influence the degree of message elaboration, such that people might process a message more effortfully when it is attributed to AI than a human, due to the novelty. If so, content cues will exert stronger influence when AI has authored the message.

To develop a theory is to answer the fundamental “why” question about observed regularities in the focal phenomenon of interest (Berger et al., 2010). As such, to theorize HMC, one needs to identify consistent patterns in people’s reactions to machines, propose the mechanisms to explain why such patterns exist, and put them to empirical tests for validation. Can mindlessness and/or machine heuristic serve as the core mechanism that predicts and explains the processes and effects of HMC? Are there any alternative explanations?