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Designing for Responsible Trust in AI Systems: A Communication Perspective
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Abstract
Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users' cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.
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When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction between direct quantitative uncertainty and indirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of knowledge concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty.
As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.
Artificial intelligence (AI) ethics is now a global topic of discussion in academic and policy circles. At least 84 public–private initiatives have produced statements describing high-level principles, values and other tenets to guide the ethical development, deployment and governance of AI. According to recent meta-analyses, AI ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach for the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement.
Trust in a Recommender System (RS) is crucial for its overall success. However, it remains underexplored whether users trust personal recommendation sources (i.e. other humans) more than impersonal sources (i.e. conventional RS), and, if they do, whether the perceived quality of explanation provided account for the difference. We conducted an empirical study in which we compared these two sources of recommendations and explanations. Human advisors were asked to explain movies they recommended in short texts while the RS created explanations based on item similarity. Our experiment comprised two rounds of recommending. Over both rounds the quality of explanations provided by users was assessed higher than the quality of the system's explanations. Moreover, explanation quality significantly influenced perceived recommendation quality as well as trust in the recommendation source. Consequently, we suggest that RS should provide richer explanations in order to increase their perceived recommendation quality and trustworthiness.
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of Artificial Intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on Explainable Artificial Intelligence. A research field that holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to explainable AI. Through the lens of literature, we review existing approaches regarding the topic, we discuss trends surrounding its sphere and we present major research trajectories.
Supervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? Models should be not only good, but also interpretable, yet the task of interpretation appears underspecified. The academic literature has provided diverse and sometimes non-overlapping motivations for interpretability and has offered myriad techniques for rendering interpretable models. Despite this ambiguity, many authors proclaim their models to be interpretable axiomatically, absent further argument. Problematically, it is not clear what common properties unite these techniques. This article seeks to refine the discourse on interpretability. First it examines the objectives of previous papers addressing interpretability, finding them to be diverse and occasionally discordant. Then, it explores model properties and techniques thought to confer interpretability, identifying transparency to humans and post hoc explanations as competing concepts. Throughout, the feasibility and desirability of different notions of interpretability are discussed. The article questions the oft-made assertions that linear models are interpretable and that deep neural networks are not. © 2018 Association for Computing Machinery. All rights reserved.
In this article, we look at trust in artificial intelligence, machine learning (ML), and robotics. We first review the concept of trust in AI and examine how trust in AI may be different from trust in other technologies. We then discuss the differences between interpersonal trust and trust in technology and suggest factors that are crucial in building initial trust and developing continuous trust in artificial intelligence.
In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.
This chapter outlines the two basic routes to persuasion. One route is based on the thoughtful consideration of arguments central to the issue, whereas the other is based on the affective associations or simple inferences tied to peripheral cues in the persuasion context. This chapter discusses a wide variety of variables that proved instrumental in affecting the elaboration likelihood, and thus the route to persuasion. One of the basic postulates of the Elaboration Likelihood Model—that variables may affect persuasion by increasing or decreasing scrutiny of message arguments—has been highly useful in accounting for the effects of a seemingly diverse list of variables. The reviewers of the attitude change literature have been disappointed with the many conflicting effects observed, even for ostensibly simple variables. The Elaboration Likelihood Model (ELM) attempts to place these many conflicting results and theories under one conceptual umbrella by specifying the major processes underlying persuasion and indicating the way many of the traditionally studied variables and theories relate to these basic processes. The ELM may prove useful in providing a guiding set of postulates from which to interpret previous work and in suggesting new hypotheses to be explored in future research.
Copyright © 1986 Academic Press Inc. Published by Elsevier Inc. All rights reserved.
The Elaboration Likelihood Model of persuasion (ELM) is discussed as it relates to source factors in persuasion. The ELM proposes that under low elaboration likelihood, source factors serve as simple acceptance or rejection cues under moderate elaboration likelihood source factors guide the extent of thinking; and under high elaboration likelihood source factors are unimportant as cues or general motivators of thought but (if relevant) serve as persuasive arguments or help in interpreting arguments. Several experiments are described which provide empirical support for these propositions.
As automated controllers supplant human intervention in controlling complex systems, the operators' role often changes from that of an active controller to that of a supervisory controller. Acting as supervisors, operators can choose between automatic and manual control. Improperly allocating function between automatic and manual control can have negative consequences for the performance of a system. Previous research suggests that the decision to perform the job manually or automatically depends, in part, upon the trust the operators invest in the automatic controllers. This paper reports an experiment to characterize the changes in operators' trust during an interaction with a semi-automatic pasteurization plant, and investigates the relationship between changes in operators' control strategies and trust. A regression model identifies the causes of changes in trust, and a ‘trust transfer function’ is developed using lime series analysis to describe the dynamics of trust. Based on a detailed analysis of operators' strategies in response to system faults we suggest a model for the choice between manual and automatic control, based on trust in automatic controllers and self-confidence in the ability to control the system manually.
Signaling theory is useful for describing behavior when two parties (individuals or organizations) have access to different information. Typically, one party, the sender, must choose whether and how to communicate (or signal) that information, and the other party, the receiver, must choose how to interpret the signal. Accordingly, signaling theory holds a prominent position in a variety of management literatures, including strategic management, entrepreneurship, and human resource management. While the use of signaling theory has gained momentum in recent years, its central tenets have become blurred as it has been applied to organizational concerns. The authors, therefore, provide a concise synthesis of the theory and its key concepts, review its use in the management literature, and put forward directions for future research that will encourage scholars to use signaling theory in new ways and to develop more complex formulations and nuanced variations of the theory.
Purpose
The purpose of this paper is to examine the extent to which measures and operationalisations of intra‐organisational trust reflect the essential elements of the existing conceptualisation of trust inside the workplace.
Design/methodology/approach
The paper provides an overview of the essential points from the rich variety of competing conceptualisations and definitions in the management and organisational literatures. It draws on this overview to present a framework of issues for researchers to consider when designing research based on trust. This framework is then used to analyse the content of 14 recently published empirical measures of intra‐organisational trust. It is noted for each measure the form that trust takes, the content, the sources of evidence and the identity of the recipient, as well as matters related to the wording of items.
Findings
The paper highlights where existing measures match the theory, but also shows a number of “blind‐spots” or contradictions, particularly over the content of the trust belief, the selection of possible sources of evidence for trust, and inconsistencies in the identity of the referent.
Research limitations/implications
It offers researchers some recommendations for future research designed to capture trust among different parties in organisations, and contains an Appendix with 14 measures for intra‐organisational trust.
Originality/value
The value of the paper is twofold: it provides an overview of the conceptualisation literature, and a detailed content‐analysis of several different measures for trust. This should prove useful in helping researchers refine their research designs in the future.
Signaling theory provides an opportunity to integrate an interactive theory of symbolic communication and social benefit with materialist theories of individual strategic action and adaptation. This article examines the potential explanatory value of signaling theory for a variety of anthropological topics, focusing on three social arenas in which signaling might plausibly be important: unconditional generosity, "wasteful" subsistence behavior, and artistic or craft traditions. In each case, it outlines the ways in which the phenomena correspond with the expectations of signaling theory by showing how a given pattern of action might signal particular hidden attributes, provide benefits to both signaler and observers, and meet the conditions for honest communication. The ethnographic evidence suggests that the fundamental conditions for reliable signaling of condition-dependent qualities may exist in many social domains. It appears that signaling theory has considerable promise for generating novel and powerful insights into the ethnographic realm. © 2005 by The Wenner-Gren Foundation for Anthropological Research. All rignts reserved.
This article presents a unifying framework of credibility assessment in which credibility is characterized across a variety
of media and resources with respect to diverse information seeking goals and tasks. The initial data were collected through
information-activity diaries over a 10-day period from 24 undergraduate students at three different colleges. Based on 245
information seeking activities from the diaries, the authors conducted individual interviews with participants and analyzed
the transcripts using a grounded theory analysis. Three distinct levels of credibility judgments emerged: construct, heuristics,
and interaction. The construct level pertains to how a person constructs, conceptualizes, or defines credibility. The
heuristics level involves general rules of thumb used to make judgments of credibility applicable to a variety of situations.
Finally, the interaction level refers to credibility judgments based on content, peripheral source cues, and peripheral information
object cues. In addition, context emerged as the social, relational and dynamic frames surrounding the information
seeker and providing boundaries of credibility judgments. The implications of the framework in terms of its theoretical
contribution to credibility research and practices are discussed.
In this paper, we propose a system for helping users to judge the credibility of Web search results and to search for credible Web pages. Conventional Web search engines present only titles, snippets, and URLs for users, which give few clues to judge the credibility of Web search results. Moreover, ranking algorithms of the conventional Web search engines are often based on relevance and popularity of Web pages. Towards credibility-oriented Web search, our proposed system provides users with the following three functions: (1) calculation and visualization of several scores of Web search results on the main credibility aspects, (2) prediction of user's credibility judgment model through user's credibility feedback for Web search results, and (3) re-ranking of Web search results based on user's predicted credibility model. Experimental results suggest that our system enables users - in particular, users with knowledge about search topics - to find credible Web pages from a list of Web search results more efficiently than conventional Web search interfaces.
Do different design and information content factors
influence trust and mistrust of online health sites? Fifteen women faced with a risky health decision were observed while searching the Internet for information and advice over four consecutive weeks. In some sessions their searches were unstructured, whilst in other sessions they were directed to review specific sites, chosen for their trust design elements. Content analysis of concurrent
verbalisations and group discussion protocols provided
support for a staged model wherein design appeal predicted
rejection (mistrust) and credibility of information and
personalisation of content predicted selection (trust) of
advice sites.
The spread of AI-embedded systems involved in human decision making makes studying human trust in these systems critical. However, empirically investigating trust is challenging. One reason is the lack of standard protocols to design trust experiments. In this paper, we present a survey of existing methods to empirically investigate trust in AI-assisted decision making and analyse the corpus along the constitutive elements of an experimental protocol. We find that the definition of trust is not commonly integrated in experimental protocols, which can lead to findings that are overclaimed or are hard to interpret and compare across studies. Drawing from empirical practices in social and cognitive studies on human-human trust, we provide practical guidelines to improve the methodology of studying Human-AI trust in decision-making contexts. In addition, we bring forward research opportunities of two types: one focusing on further investigation regarding trust methodologies and the other on factors that impact Human-AI trust.
The wide adoption of Machine Learning (ML) technologies has created a growing demand for people who can train ML models. Some advocated the term "machine teacher'' to refer to the role of people who inject domain knowledge into ML models. This "teaching'' perspective emphasizes supporting the productivity and mental wellbeing of machine teachers through efficient learning algorithms and thoughtful design of human-AI interfaces. One promising learning paradigm is Active Learning (AL), by which the model intelligently selects instances to query a machine teacher for labels, so that the labeling workload could be largely reduced. However, in current AL settings, the human-AI interface remains minimal and opaque. A dearth of empirical studies further hinders us from developing teacher-friendly interfaces for AL algorithms. In this work, we begin considering AI explanations as a core element of the human-AI interface for teaching machines. When a human student learns, it is a common pattern to present one's own reasoning and solicit feedback from the teacher. When a ML model learns and still makes mistakes, the teacher ought to be able to understand the reasoning underlying its mistakes. When the model matures, the teacher should be able to recognize its progress in order to trust and feel confident about their teaching outcome. Toward this vision, we propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the surging field of explainable AI (XAI) into an AL setting. We conducted an empirical study comparing the model learning outcomes, feedback content and experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without explanation). Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and additional cognitive workload. Our study also reveals important individual factors that mediate a machine teacher's reception to AI explanations, including task knowledge, AI experience and Need for Cognition. By reflecting on the results, we suggest future directions and design implications for XAL, and more broadly, machine teaching through AI explanations.
This article attempts to bridge the gap between widely discussed ethical principles of Human-centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are developed and implemented in multiple organizational structures, I propose 15 recommendations at three levels of governance: team, organization, and industry. The recommendations are intended to increase the reliability, safety, and trustworthiness of HCAI systems: (1) reliable systems based on sound software engineering practices, (2) safety culture through business management strategies, and (3) trustworthy certification by independent oversight. Software engineering practices within teams include audit trails to enable analysis of failures, software engineering workflows, verification and validation testing, bias testing to enhance fairness, and explainable user interfaces. The safety culture within organizations comes from management strategies that include leadership commitment to safety, hiring and training oriented to safety, extensive reporting of failures and near misses, internal review boards for problems and future plans, and alignment with industry standard practices. The trustworthiness certification comes from industry-wide efforts that include government interventions and regulation, accounting firms conducting external audits, insurance companies compensating for failures, non-governmental and civil society organizations advancing design principles, and professional organizations and research institutes developing standards, policies, and novel ideas. The larger goal of effective governance is to limit the dangers and increase the benefits of HCAI to individuals, organizations, and society.
Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and provenance, are also critical elements to engender consumers' trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multi-dimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers' trust. In this paper, inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI in the appendix of the paper.
Work in social psychology on interpersonal interaction has demonstrated that people are more likely to comply to a request if they are presented with a justification - even if this justification conveys no information. In the light of the many calls for explaining reasoning of interactive intelligent systems to users, we investigate whether this effect holds true for human-computer interaction. Using a prototype of a nutrition recommender, we conducted a lab study (N=30) between three groups (no explanation, placebic explanation, and real explanation). Our results indicate that placebic explanations for algorithmic decision-making may indeed invoke perceived levels of trust similar to real explanations. We discuss how placebic explanations could be considered in future work.
In this day and age of identity theft, are we likely to trust machines more than humans for handling our personal information? We answer this question by invoking the concept of "machine heuristic," which is a rule of thumb that machines are more secure and trustworthy than humans. In an experiment (N = 160) that involved making airline reservations, users were more likely to reveal their credit card information to a machine agent than a human agent. We demonstrate that cues on the interface trigger the machine heuristic by showing that those with higher cognitive accessibility of the heuristic (i.e., stronger prior belief in the rule of thumb) were more likely than those with lower accessibility to disclose to a machine, but they did not differ in their disclosure to a human. These findings have implications for design of interface cues conveying machine vs. human sources of our online interactions.
How can we add the most important ingredient to our relationship with machine learning?
Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.
To better support older adults' consumption of high quality health information on the Internet, it is important to understand how older adults make credibility judgments with online health information. For this purpose, we conducted two laboratory studies to explore how the credibility cues in message contents, website features, and user reviews could differentially impact younger and older adults' credibility judgments. Results from the first experiment showed that older adults, compared to younger ones, were less sensitive to the credibility cues in message contents, as well as those in the website features. Results from the second experiment showed that user reviews that were consistent with the credibility cues in message contents could reinforce older adults' credibility judgments. Older adults, compared to younger adults, seemed to be less swayed by user reviews that were inconsistent with the message contents. These results provided implications for designing health information websites that better support older adults' credibility judgments.
James J Gibson introduced for the first time the word "affordances" in this 1977 paper.
Data from 574 participants were used to assess perceptions of message, site, and sponsor credibility across four genres of websites; to explore the extent and effects of verifying web-based information; and to measure the relative influence of sponsor familiarity and site attributes on perceived credibility.The results show that perceptions of credibility differed, such that news organization websites were rated highest and personal websites lowest, in terms of message, sponsor, and overall site credibility, with e-commerce and special interest sites rated between these, for the most part.The results also indicated that credibility assessments appear to be primarily due to website attributes (e.g. design features, depth of content, site complexity) rather than to familiarity with website sponsors. Finally, there was a negative relationship between self-reported and observed information verification behavior and a positive relationship between self-reported verification and internet/web experience.
The findings are used to inform the theoretical development of perceived web credibility.
Scholars in various disciplines have considered the causes, nature, and effects of trust. Prior approaches to studying trust are considered, including characteristics of the trustor, the trustee, and the role of risk. A definition of trust and a model of its antecedents and outcomes are presented, which integrate research from multiple disciplines and differentiate trust from similar constructs. Several research propositions based on the model are presented.
In Exp I, 183 undergraduates read a persuasive message from a likable or unlikable communicator who presented 6 or 2 arguments on 1 of 2 topics. High involvement (HI) Ss anticipated discussing the message topic at a future experimental session, whereas low-involvement (LI) Ss anticipated discussing a different topic. For HI Ss, opinion change was significantly greater given 6 arguments but was unaffected by communicator likability. For LI Ss, opinion change was significantly greater given a likable communicator but was unaffected by the argument's manipulation. In Exp II with 80 similar Ss, HI Ss showed slightly greater opinion change when exposed to 5 arguments from an unlikable (vs 1 argument from a likable) communicator, whereas LI Ss exhibited significantly greater persuasion in response to 1 argument from a likable (vs 5 arguments from an unlikable) communicator. Findings support the idea that HI leads message recipients to employ a systematic information processing strategy in which message-based cognitions mediate persuasion, whereas LI leads recipients to use a heuristic processing strategy in which simple decision rules mediate persuasion. Support was also obtained for the hypothesis that content- vs source-mediated opinion change would result in greater persistence. (37 ref) (PsycINFO Database Record (c) 2004 APA, all rights reserved)
The tremendous amount of information available online has resulted in considerable research on information and source credibility. The vast majority of scholars, however, assume that individuals work in isolation to form credibility opinions and that people must assess information credibility in an effortful and time-consuming manner. Focus group data from 109 participants were used to examine these assumptions. Results show that most users rely on others to make credibility assessments, often through the use of group-based tools. Results also indicate that rather than systematically processing information, participants routinely invoked cognitive heuristics to evaluate the credibility of information and sources online. These findings are leveraged to suggest a number of avenues for further credibility theorizing, research, and practice.
The credibility of web sites is becoming an increasingly important area to understand. To expand knowledge in this domain, we conducted an online study that investigated how different elements of Web sites affect people's perception of credibility. Over 1400 people participated in this study, both from the U.S. and Europe, evaluating 51 different Web site elements. The data showed which elements boost and which elements hurt perceptions of Web credibility. Through analysis we found these elements fell into one of seven factors. In order of impact, the five types of elements that increased credibility perceptions were “real-world feel”, “ease of use”, “expertise”, “trustworthiness”, and “tailoring”. The two types of elements that hurt credibility were “commercial implications&rdquo ;and “amateurism”. This large-scale study lays the groundwork for further research into the elements that affect Web credibility. The results also suggest implications for designing credible Web sites.