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Exploring the artificial intelligence “Trust paradox”: Evidence from a survey experiment in the United States [1]

['Sarah Kreps', 'Cornell University Tech Policy Institute', 'Menlo Park', 'Ca', 'United States Of America', 'Julie George', 'Stanford Center For International Security', 'Cooperation', 'Stanford', 'Paul Lushenko']

Date: 2023-08

Abstract Advances in Artificial Intelligence (AI) are poised to transform society, national defense, and the economy by increasing efficiency, precision, and safety. Yet, widespread adoption within society depends on public trust and willingness to use AI-enabled technologies. In this study, we propose the possibility of an AI “trust paradox,” in which individuals’ willingness to use AI-enabled technologies exceeds their level of trust in these capabilities. We conduct a two-part study to explore the trust paradox. First, we conduct a conjoint analysis, varying different attributes of AI-enabled technologies in different domains—including armed drones, general surgery, police surveillance, self-driving cars, and social media content moderation—to evaluate whether and under what conditions a trust paradox may exist. Second, we use causal mediation analysis in the context of a second survey experiment to help explain why individuals use AI-enabled technologies that they do not trust. We find strong support for the trust paradox, particularly in the area of AI-enabled police surveillance, where the levels of support for its use are both higher than other domains but also significantly exceed trust. We unpack these findings to show that several underlying beliefs help account for public attitudes of support, including the fear of missing out, optimism that future versions of the technology will be more trustworthy, a belief that the benefits of AI-enabled technologies outweigh the risks, and calculation that AI-enabled technologies yield efficiency gains. Our findings have important implications for the integration of AI-enabled technologies in multiple settings.

Citation: Kreps S, George J, Lushenko P, Rao A (2023) Exploring the artificial intelligence “Trust paradox”: Evidence from a survey experiment in the United States. PLoS ONE 18(7): e0288109. https://doi.org/10.1371/journal.pone.0288109 Editor: Hans H. Tung, National Taiwan University, TAIWAN Received: January 20, 2023; Accepted: June 20, 2023; Published: July 18, 2023 Copyright: © 2023 Kreps et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: We have uploaded the minimal anonymized data set necessary to replicate our study findings to a stable, public repository. This is the link where one can access that information: https://doi.org/10.7910/DVN/EOYDJR. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist.

Introduction In August 2022, The New York Times observed “we’re in a golden age of progress in artificial intelligence (AI). It’s time to start taking its potential and risks seriously” [1]. Amid these rapid developments in AI, discussion of human agency is often absent. This oversight is puzzling given that individuals will ultimately be responsible for whether AI-enabled technologies diffuse widely across society or confront resistance. Regulators will have some role to play, and researchers have studied local officials’ reactions to AI-enabled technologies precisely because they will make important decisions about if, when, and how to use these capabilities [2]. Notwithstanding these insights, we contend that public attitudes are crucial to societal adoption of AI-enabled technologies. If the public is hesitant, demonstrating reluctance to adopt these technologies, then it will pressure policymakers to impose restrictions on research and development [3]. In this study, we research public attitudes both toward AI-enabled technologies across various domains and the basis for those attitudes. Existing studies about public attitudes toward emerging technologies have tended to focus on levels of trust, which is defined as the “willingness to make oneself vulnerable” to a capability “based on a judgment of similarity of intentions or values” [4]. Research on nanotechnology focuses on the public’s degree of trust in the capability to minimize risks to humans [5, 6], as does research on genetically modified organisms [7] and online shopping [8]. Studies on AI-enabled technologies are no different, with researchers preferring to use trust as the dependent variable [9]. This focus on trust may not be misguided. In the context of nuclear energy, for instance, researchers have studied public trust, in part because previous work shows a strong relationship between low levels of trust and low public support for nuclear power [10]. Researchers have also shown that public trust for autonomous vehicles correlates with greater levels of acceptance and adoption [11], which is similar for public attitudes regarding mobile banking [12]. We draw on this scholarship to suggest and test the possibility of a “trust paradox,” which we define as the public’s puzzling willingness to support or use AI-enabled technologies that they do not trust. Such a dynamic is well documented in the social media space [13–15], where the public heavily uses social media despite expressing concerns about data privacy, content moderation, and misinformation. Why would the public support the use of AI-enabled technologies it does not trust? In addressing this question, we advance five hypotheses that help explain the puzzle of why individuals support the use of AI-enabled technologies despite having lower levels of trust: the “fear of missing out” (FOMO); a cost-benefit analysis wherein individuals see risk but are persuaded by the potential benefits; assessments about the absence of efficient alternatives; optimism about the future development of more trustworthy technology; and, transparency about the nature of technology. First, one potential explanation for varying levels of support and trust of AI-enabled technologies centers on FOMO. The literature on consumer psychology points to FOMO as a powerful factor that can influence people to embrace an experience or purchase a product, even if they believe that doing so can be self-defeating. This FOMO mechanism has been cited as the reason why individuals often overuse smartphones, sleep too little, or abuse drugs, despite recognizing that such behavior contradicts privately-held beliefs or values [16]. We apply this insight, which is related to the implications of other social-psychological considerations such as status [17] and reputation [18] on personal behavior [19], to AI. We argue that FOMO may explain why individuals support the use of AI-enabled technologies that they do not trust. People may believe that if they do not use a certain AI-enabled technology, others will, resulting in feelings of anxiety or loss because they are somehow missing out on seemingly popular activities. Second, the public’s trust paradox for AI-enabled technologies may also result from a calculation that while these technologies introduce risk, they also provide more benefits overall. Indeed, people may rely on AI-enabled technologies not so much because they trust these capabilities but because they perceive that the anticipated benefits [20] of adopting AI-enabled technologies will exceed the expected costs [21] of doing so. This amounts to an expected utility calculation about the benefits and costs of behavior characteristic of the risk management [3] of new technologies, in which the risks are assessed relative to overall benefits and adopted if the latter outweighs the former. In the context of AI-enabled technologies, this calculation might also help explain individuals’ support of new capabilities that they do not trust, reflecting a belief that they stand to enjoy benefits that offset their perception of costs in the face of distrust. While individuals may not trust an AI-enabled technology, they may still understand it as conferring improvements over a human-driven alternative. Third, individuals might support the use of technologies at rates that exceed trust if they see few economically viable alternatives. For example, AI-enabled technologies can be used as a substitute for human labor across a range of tasks, especially through automation [22]. Brynjolfsson and McAfee [23] have traced the way that automation has increasingly added efficiencies to the economy, performing medical diagnoses, replacing humans on assembly lines [22, 24–27], and carrying loads too heavy for humans. In other examples, accounting, sales, and trading-related tasks are being replaced by AI [28]. These developments create thorny issues of trust and privacy, but also might seem inevitable, as emerging technologies in the past have also remade societies and economies [26, 27]. Individuals, then, might share these concerns but nonetheless acknowledge that AI-enabled technology can perform particular jobs better than humans, and potentially, replace these human jobs. Fourth, we posit that technological optimism may be another explanation. In this case, individuals may believe that even if they face risks in the present, future iterations of an AI-enabled technology will improve in ways that minimize such potential harms, which is consistent with technological improvements in the past [29]. A previous study of individual technology use showed that whereas many individuals believe that digital capitalism currently disadvantages large swaths of society as well as erodes trust in democratic institutions, they also think future technology will provide solutions to these challenges through the protection of speech and empowerment of citizens [30]. Based on these findings, individuals might not trust AI-enabled technologies now, but have confidence that these capabilities will improve over time. Such technological optimism may encourage them to adopt AI-enabled technologies in the short-term given the promise of longer-term improvements. Finally, individuals may not trust AI-enabled technologies but support their use if there is transparency or explanation for how these technologies are used. One of the reasons people have distrusted AI is because the enabling algorithm is perceived as a “black box.” The lack of explanation for coding decisions as well as datasets to train the enabling algorithms creates the potential for bias in AI [31]. This helps explain why Twitter’s CEO, Elon Musk, recently stated “transparency is key to trust” [32], echoing Putnam’s findings that trust is integral to democratic society [33]. Thus, Musk promised to better communicate the social media platform’s data management protocols because of concerns that Twitter “interfered” in the 2020 U.S. presidential election [34]. In the context of AI-enabled technologies, then, the expectation of transparency may account for the public’s willingness to use these systems that they otherwise distrust [35].

Materials and methods To study the potential differences between public attitudes in terms of support and trust, both within and across different applications of AI, we constructed a two-part empirical study conducted on a representative sample of 1,008 U.S. citizens. Our project received an exempt status from the Cornell University’s Institutional Review Board for Human Subject Research (IRB Protocol #2004009569), and we obtained consent from respondents electronically. The form of consent obtained was electronic. No minors were included in this study. Subjects were properly instructed and have indicated that they consent to participate in our study by electronically approving the appropriate informed consent. First, although observational evidence points to a trust paradox in some contexts, we seek to establish whether this is a generalized phenomenon across AI-enabled technologies by fielding a conjoint survey that assesses perceptions of support and trust in these technologies. Second, building on the five mechanisms outlined above, we explore the reasons why individuals exhibit differences in support and trust using causal mediation analysis. We administered the study from October 7–21, 2022, via Lucid. Lucid uses a quota sampling protocol to produce samples that correspond to U.S. demographics in terms of age, gender, race, and location. Existing research shows that this sampling protocol produces experimental effects that largely mirror those found with probability-based sampling [36]. We present summary statistics in the Supplementary Information. As Fig 1 shows, respondents first participated in a conjoint experiment to investigate the potential gap between support and trust in emerging technology. Marketing surveys commonly use conjoint surveys [37] because they enable researchers to vary a number of attributes and assess how levels of those attributes affect individual choice. Research also finds that conjoint surveys help reduce social desirability bias among respondents, in which they are encouraged to answer in a certain way or do so because they feel obligated [38]. In adopting this approach, we ensure to fulfill a key assumption for the orthogonality of attributes in expectation, which enables us to differentiate complex treatment effects into their constituent parts [39, 40]. In our case, we are able to assess multiple factors that could plausibly relate to the anticipated effects of AI attributes, either discretely or when interacted with each other, on respondents’ preference formation. PPT PowerPoint slide

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TIFF original image Download: Fig 1. Survey flowchart of conjoint and causal mediation analyses via lucid (October 7–21, 2022). https://doi.org/10.1371/journal.pone.0288109.g001 Initially, we informed respondents that they would be presented with a series of hypothetical applications of AI-enabled technologies. We varied four different attributes, outlined in Table 1, along with their respective levels [41]. Research suggests that hypothetical but realistic combinations of attributes can help enhance the external validity of survey results by calibrating contextual detail with experimental control [42, 43]. PPT PowerPoint slide

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TIFF original image Download: Table 1. Considered attributes and attribute levels for artificial intelligence applications. https://doi.org/10.1371/journal.pone.0288109.t001 In the first part of our experiment, the conjoint survey, we consider different domains in which AI operates. Research on attitudes toward AI typically focuses on one domain, such as autonomous vehicles [44]. We advance the literature on individual preferences for AI-enabled technologies by considering how respondents may have different perceptions of risk across domains, which allows us to understand—theoretically and empirically—how receptiveness to AI varies depending on context. We select domains where AI is already making inroads, including autonomous vehicles, armed drones, general surgery, social media content moderation, and police surveillance. In doing so, we draw on categories identified in the One Hundred Year Study of Artificial Intelligence published by Stanford University, which studies how AI will affect society in the future [45]. Within the conjoint survey we also evaluate the degree of autonomy. Drawing on the autonomy literature [46–48], we locate a range of autonomy that is bookended by extreme variations and includes a hybrid form of automation as well. At one extreme, we present respondents a form of manual autonomy that is characterized by human control with no machine assistance or involvement. At the other extreme, we present respondents with a form of full autonomy where the machine has full responsibility and control over decision-making in these settings. Splitting the difference between these two extreme variations in autonomy is mixed-initiative autonomy. Respondents presented with this level of autonomy are informed that the application can toggle between human and machine control in these settings. We then vary the degree of algorithmic precision. Model performance is often a function of convergence between the algorithm and truth. In a context of natural language models, for example, algorithmic detection of AI-generated text is measured based on the percentage of text examples that the algorithm correctly identified as AI versus human generated [47]. Social media platforms such as Facebook advertise that they remove 99.8% of terrorism-related content before users flag it, and 97% for hate speech that violates community standards [49]. Algorithms trained to detect cancer are measured in terms of accuracy of diagnoses, with some neural networks reaching 99.8% [50]. In our study, we use language concerning the precision of algorithms, which refers to the percentage of the results which are relevant. Designing an algorithm for precision yields confident diagnoses. In the context of oncology, this means that an algorithm correctly diagnoses someone with cancer while avoiding Type II errors or false positives. A model that has the precision of 0.95 when predicting a diagnosis, then, would be correct 95% of the time. How individuals view these precision rates is likely to be a function of the domain in which algorithms operate, interacting with variables such as the controllability to affect what they deem to be “sufficiently high” levels of precision. We additionally consider the locus of regulation. One approach is for government agencies to regulate the way that businesses use algorithms by enforcing legislation such as the Credit Reporting Act. A second option is for a private firm that developed the software or platform to regulate it, similar to the approach adopted by Facebook and Twitter. A third approach is public-private collaboration where public agencies direct private firms to alter their behavior. The Centers for Disease Control often request that Twitter flag particular content relating to COVID-19 vaccines as misinformation [51]. Finally, individuals, users, or communities engaging on a particular social media platform may self-regulate, which is consistent with Reddit’s approach to content management. Reddit communities adopt this approach in terms of creators developing guidelines for user behavior and communities regulating on subreddits [52]. Taken together, the conjoint task randomly varied four attributes and levels therein, yielding 135 unique scenarios for an AI-enabled technology, which a recent study shows is well within the tolerance level for quality responses [53]. Given our within-subject survey design, we presented respondents with four randomly assigned choice sets resulting in over 5,000 observations. After reading each scenario, we then asked respondents two questions that define our key dependent variables. First, we ask whether respondents support the use of AI in these settings, using a 5-point Likert scale to gauge their attitudes (1 corresponds to “strongly disagree” and 5 to “strongly agree”). To gain leverage over a possible trust paradox where respondents do not trust an AI-enabled technology but nevertheless support its adoption, we ask subjects if they trust the use of AI for these purposes as well. We study trust as one of our outcome variables rather than a willingness to use AI-enabled technologies for several reasons. Measuring trust is consistent with previous research for emerging technologies and more importantly, is a direct test of public attitudes. Measuring respondents’ willingness to use AI-enabled technologies, on the other hand, may increase the potential for confirmation bias regarding these capabilities. To analyze our data, we first evaluated the potential for a trust paradox, initially calculating marginal means for the degree of support and trust for each AI-attribute level in a manner similar to other conjoint studies in political science [54]. Doing so allows us to identify the “trust paradox,” which we do by determining the statistical difference between public support and trust for each AI-attribute level. We then calculate the average marginal component effect (ACME) per attribute level, which is generated by our conjoint design, in a regression framework. The ACME represents the mean difference in a respondents’ level of support and trust for an AI-enabled technology when comparing two attribute values averaged across all combinations of other AI-enabled technology attribute values. Based on the randomization of our attribute-levels, we assume that these ACMEs are nonparametric, meaning they are not the result of an underlying distribution of data [55–57]. We also include several control variables in our regression framework based on existing research that suggests these may have potentially important mediating effects on public attitudes for AI-enabled technologies. Specifically, we are interested in how differences in age, gender, race, and political ideology may shape respondents’ support and trust for AI-enabled technologies [58, 59]. Beyond establishing whether a trust paradox exists, we then presented respondents with another survey experiment to assess the potential microfoundations, or underlying values and beliefs developed in the introduction, that shape degrees of support and trust in AI-enabled technology. This consisted of a 3x2 factorial and between subject survey including six treatments and a control group. Those randomly assigned to the control group only learned that “In recent years, advancements in Artificial-Intelligence (AI) have led to the emergence of new technologies.” From the five domains we studied in our conjoint survey, we selected three that scholars broadly recognize as the most “hotly debated” [45]: armed drones, social-media content moderation, and driverless vehicles. These three domains also capture the use of AI-enabled technologies in various settings, such as in conflict (armed drones), across society (driverless cars), and in the online space (social media content moderation), which further allows us to investigate potential differences in support and trust. For each technology, the experimental groups also varied two intended purposes, whether to enhance human judgment or provide a substitute for human judgment, which draws on debates about “complementing” versus “replacing” human systems. One argument suggests that AI-enabled technologies have different cognitive qualities than biological systems—including a set of mental models about ethics and empathy—and should be viewed less as a replacement for human intelligence but rather as a partner to humans [60]. After reading their vignettes, we asked respondents to gauge their support, trust, and understanding for AI-enabled technologies with varying purposes. Previous research on mediators in terms of AI-enabled technologies has focused largely on the implications of affect, such as anger, on degrees of public support [61]. Rather, we cast a wider net of potential mediators, building on arguments about trust and support in the literature on emerging technology discussed in the introduction. Specifically, we draw on the five mechanisms outlined in the introduction to develop corresponding statements and ask individuals their degree of agreement or disagreement with each of these statements, using a 5-point Likert scale to capture their feedback (1 corresponds to “strongly disagree” and 5 to “strongly agree). For the FOMO mechanism, for example, we asked subjects to respond to the following statement: “Others are using this AI-enabled technology, so not using it means the prospect of missing out” [16]. We repeated this approach for the other potential mediators, as outlined above in Fig 1. Based on the responses to these questions, we then carried out causal mediation analysis. This method shows the complete causal chain for the effect of an independent variable on a mediator and the effect of a mediator on the dependent variable [62, 63]. Though we ensure to fulfill key assumptions to operationalize this method, namely randomizing the order of survey questions for respondents across all groups [64], causal mediation analysis is sometimes criticized for failing to account for confounding variables, even in an experimental setting that researchers usually champion for resolving stochastic error. Miles argues that confounders “cannot be eliminated even in a well-controlled randomized experiment” [65], causing Simonsohn to contend that “we over-estimate the mediators” [66]. In recognition of these valid concerns, we draw on previous studies that attempt to adjudicate public attitudes for AI-enabled technologies. These studies adopt what Imai et al. refer to as the “sequential ignorability assumption,” whereby both possible pretreatment confounders and treatment assignment are assumed to be statistically independent from the potential outcomes and mediators [67]. Additionally, we opt not to inductively derive possible mediators from respondents’ answers to open-ended questions, as other researchers have done [68], given the possibility of bias [69].

Discussion To the best of our knowledge, this research is the first study of trust and behavior toward AI-enabled technologies across different use cases and modalities. Previous studies investigate public opinion toward specific technologies, such as autonomous drones and vehicles, or for some AI-applications, including autonomy in the workplace [16, 88, 89]. By contrast, our conjoint-based study offers methodological advancements because it allows us to understand the particular features of the AI-enabled technology that affect attitudes ranging from trust to support for its use, disconnects between the two, and variation on the basis of demographic factors. We then go further to understand the basis of public trust and support, investigating theoretically-grounded mechanisms in a second novel study. Our analysis shows, first, the existence of a trust paradox, wherein support for the use of AI-enabled technologies is higher than the trust for those same technologies in certain domains, and at certain autonomy and precision levels. The highest level of precision, with fewer mistakes, was considerably more likely to elicit both trust and support. These findings parallel those of public uptake of vaccines, for example, where efficacy is strongly correlated with willingness to receive a vaccine [90]. Perceptions of controllability also play a role, in which willingness to use a particular application increases in a mixed-initiative setting in which humans can override or interact with the machine versus either full autonomy or full human control. Indeed, a recent report published by the Center for Strategic and International Studies echoes this sentiment among U.S. defense experts, stating there are “incredible new opportunities for human-machine teamwork, in which the roles of humans and machines are optimized based upon what each does best” [91]. In light of this potential, the U.S. Air Force’s “loyal wingman” concept enables a pilot of a manned aircraft, such as a F-22 Raptor or F-35 Lightning II, to deploy and maneuver drones in support of mission objectives [92]. Further, the conjoint pointed to a strong preference for AI-enabled technology in the police surveillance domain. Whereas the public was agnostic on most domains, including general surgery, battlefield drones, social media content moderation, and autonomous vehicles, they were substantially more supportive of AI-enabled police surveillance. This tracks with public opinion polls showing a large plurality of adults report that they believe this technology would be beneficial for society [93]. Populations in other countries such as Australia and the United Kingdom have registered greater levels of skepticism, which raises the question about cross-national variation that we suggest should be taken up by future research [94]. In terms of mechanisms, we both theorized about why individuals might trust or support AI-enabled technologies and found that several factors play a role. These include FOMO, belief that the benefits outweigh the risks, support for the view that the technology creates efficient substitutes for tasks that are too dull, dirty, or dangerous for humans, and optimism about the way that safety features are improving to reduce the potential risks imposed by emerging technologies. These attitudes are characterized by a degree of technological optimism that improvements in innovation will provide more sustainable options over time, “an article of faith” according to critics [95]. Further, individual attitudes are broadly consistent with an expected utility calculation that acknowledges the risk that technology poses but expects to derive some form of value from its adoption [96]. While these mediators do not constitute an exhaustive list, they are relevant factors drawn from the literature, and future research could investigate additional mechanisms. Taken together, our analysis offers both theoretical and empirical insights on public attitudes toward AI-enabled technologies. Beyond the trust paradox, our findings point to variation in support and trust on the basis of domain, precision, and human involvement. Understanding the nature of public concerns and support across a range of applications and modalities is long overdue and this research offers an initial look at how Americans consider AI-enabled technologies. Although it is an important step in understanding public attitudes and behaviors, as well as key factors in societal uptake of or resistance to new technologies [97], future research should consider additional domains, such as AI in the energy sector, manufacturing, communication [98], and politics [99] to understand additional variation depending on the use case. Further, others could introduce the role of bias and a spectrum of consequences to flesh out public tolerance for the range of unintended outcomes of these technologies [100]. The field of AI is rapidly evolving and research on the public uptake and resistance to these technologies will have to evolve alongside those developments.

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