Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations

Göbel, Kyra; Niessen, Cornelia; Seufert, Sebastian; Schmid, Ute

doi:10.3389/frai.2022.919534

ORIGINAL RESEARCH article

Front. Artif. Intell., 23 November 2022
Sec. AI for Human Learning and Behavior Change
Volume 5 - 2022 | https://doi.org/10.3389/frai.2022.919534

Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations

Kyra Göbel¹^*

Cornelia Niessen¹

Sebastian Seufert²

Ute Schmid²

¹Department of Psychology, Work and Organizational Psychology Unit, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany
²Information Systems and Applied Computer Science, University of Bamberg, Bamberg, Germany

In the digital age, saving and accumulating large amounts of digital data is a common phenomenon. However, saving does not only consume energy, but may also cause information overload and prevent people from staying focused and working effectively. We present and systematically examine an explanatory AI system (Dare2Del), which supports individuals to delete irrelevant digital objects. To give recommendations for the optimization of related human-computer interactions, we vary different design features (explanations, familiarity, verifiability) within and across three experiments (N₁ = 61, N₂ = 33, N₃= 73). Moreover, building on the concept of distributed cognition, we check possible cross-connections between external (digital) and internal (human) memory. Specifically, we examine whether deleting external files also contributes to human forgetting of the related mental representations. Multilevel modeling results show the importance of presenting explanations for the acceptance of deleting suggestions in all three experiments, but also point to the need of their verifiability to generate trust in the system. However, we did not find clear evidence that deleting computer files contributes to human forgetting of the related memories. Based on our findings, we provide basic recommendations for the design of AI systems that can help to reduce the burden on people and the digital environment, and suggest directions for future research.

Introduction

Digital data carriers such as hard drives or cloud spaces have become important memory partners, and cognitive offloading—that is, externally saving information to reduce information processing requirements—can be used to decrease the cognitive demands of a task (Risko and Gilbert, 2016). However, in today's increasingly digitized world of work, individuals save and accumulate more digital objects in their external memory than they actually need in the short and long run. Thus, deleting or archiving irrelevant and outdated data files on a regular basis is important in several respects: It helps to reduce information overload, limits distractions, enables working in an effective, focused, and goal-oriented manner (Edmunds and Morris, 2000; Hair et al., 2007; Dabbish and Kraut, 2010; Soucek and Moser, 2010; Niessen et al., 2020b), and saves energy (Rong et al., 2016). However, often people do not delete irrelevant files, as deleting tends to be a decision under uncertainty, is effortful, and takes time. Therefore, people might benefit from an AI system designed to support individuals in deleting irrelevant digital objects in external memory (on the computer) on a regular basis. Research has shown that the transparency of system recommendations is important for willingness to use such a system and trust in a system (Pu and Chen, 2007; Wang and Benbasat, 2007; Mercado et al., 2016; Ribeiro et al., 2016; Miller, 2018; Thaler and Schmid, 2021). Thus, the AI system providing explanations plays a central role in the interaction between humans and the AI system.

To investigate whether and how an explanatory interactive AI system helps people to delete irrelevant files from their external memory (i.e., storage device), we developed an assistive AI system (Dare2Del) and conducted three experimental studies focusing on the role of explanations of Dare2Del's recommendations for users' attitudes (information uncertainty, trust), behavior (deleting files) and memory (forgetting irrelevant files). Specifically, building on the concept of distributed cognition (Hutchins, 1995; Zhang and Patel, 2006), which proposes that cognition exists both inside and outside the individual mind, prompting users to actively delete irrelevant digital objects and explaining to them why might also encourage forgetting of the related content in human memory (Sahakyan et al., 2008; Foster and Sahakyan, 2011).

Dare2Del is been developed since 2018 with the main intention to demonstrate how methods of explainable AI can be combined with interactive machine learning to keep humans in the loop in AI supported decision making (Niessen et al., 2020b; Schmid, 2021). As domain, the identification of irrelevant digital objects in the context of work has been selected for several reasons. First, in work contexts, whether a file should be deleted or not is determined by explicit laws and regulations as well as by personal preferences. Therefore, the domain is suitable for AI approaches which combine knowledge-based methods and machine learning (Muggleton et al., 2018). Second, in the context of work erroneous deletion of files might have severe consequences in contrast to private contexts and therefore, the domain is suitable to investigate the effect of explanatory and interactive AI methods on trustworthiness. Third, cloud storing of data comes with high monetary as well as environmental costs and therefore, intelligent tools to identify irrelevant files which can be deleted are of high practical relevance. Over the last years, some products which support the identification of irrelevant files have been developed, mostly in the context of file management systems, some in the context of cloud environments. For instance, Google Photos includes a feature which offers suggestions to delete photos. Suggestions are based on general characteristics such as file size, quality, unsupported format and source. In contrast, Dare2Del can take into account general rules (such as that invoices must be stored for 10 years) as well as individual preferences (such as that for presentation where a pptx exists the pdf can be deleted) which can be given as explicit rules as well as learned from feedback given to suggestions. The tool most similar to Dare2Del is offered by the teaching and learning software Canvas (https://community.canvaslms.com/t5/Canvas-Instructional-Designer/Tool-to-Identify-and-Delete-Unused-Files/ba-p/276260). However, this tool is only designed to delete unused files and empty folders directly from Canvas.

Dare2Del has been explored by five test users who work in the administration of a large company. They used a restricted version of Dare2Del on a file system which has been constructed as a mirror of their own. They used Dare2Del for a month and the general feedback has been positive. However, we are interested in a more controlled evaluation of Dare2Del in an experimental setting. For this reason, a fictitious work context had to be created which does not need specialized knowledge (e.g., accounting). At the same time, the digital objects have to be associated with some relevance such that erroneous deletion would have negative consequences. We decided to use the context of a library system where students' theses have to be archived as a suitable domain which is introduced in detail below.

Our research offers the following contributions to research on human AI collaboration: First, we provide a comprehensive analysis of both behavioral (i.e., accepting the system's suggestions) and cognitive (i.e., trust and credibility building) outcome variables. This allows us to not only identify if an assistive system can support users to delete irrelevant files, but also how it can help. Thus, our research also offers possible starting points for future improvement and individual or contextual adaptations which can help to increase deleting behavior. This is especially important, as people often do not delete irrelevant or obsolete digital objects in their working and private life. If an explainable AI system can initiate and support behavior change (i.e., lead to increased deleting of files), this might have positive consequences for individuals' stress levels and performance, but also for organizational effectiveness and energy saving. Moreover, we aim to explore underlying mechanisms of action and explain why explanations might be beneficial and help to change behavior (i.e., lead to increased deleting of files): We propose that explanations reduce information uncertainty, which in turn leads to more acceptance of the AI system's recommendations and the deletion of the proposed files. An understanding of these mechanisms informs design and interventions to enhance trust, credibility and behavior change in human-AI interaction.

Second, our research adds to the literature on distributed cognition by testing the assumption that an action in external memory (i.e., digital storage devices) has consequences for the corresponding internal mental representation. Previous research has already shown that external memory is used to store information outside ourselves and that this information is still connected to our memory (Sparrow et al., 2011). However, whether actively deleting information in the external memory can facilitate human forgetting of the connected memory has not been empirically investigated yet. To prove and extend existing research on the theory of distributed cognition, we ask whether deleting a digital object—especially when being convinced about why it should be deleted—also prompts forgetting of the corresponding memory content.

Theoretical background

The role of explanations

An essential prerequisite for cooperative interaction between humans and AI systems is that system decisions are transparent and comprehensible (Muggleton et al., 2018). This requirement is most obvious in the context of machine learning, particularly black-box systems such as deep neural networks. Consequently, explainable AI (XAI; Miller, 2018) has been established as a new area of research, providing methods to make the decisions of machine-trained models more transparent. Several methods for highlighting the relevance of input features have been developed. For instance, visualizing the regions in an input image that had the strongest impact on the classification decision can help to identify overfitting (Lapuschkin et al., 2019). One of the most well-known methods is LIME (Ribeiro et al., 2016)—a model-agnostic method which can be applied not only to image data but also to text. XAI methods providing information about relevance are primarily helpful to model developers, and are often not informative enough for domain experts and do not provide information helpful for end-users (Schmid, 2021). For instance, in medical diagnostics, highlighting might reveal that a model that returns a specific tumor type was right for the wrong reasons because the relevant information used is some textual mark at the image border. For experts, more expressive explanations such as rules or natural language are often more helpful. For instance, the decision between two different severity classes for a tumor might depend on spatial relations such as intrusion into fat tissue or quantifications such as the number of metastases (Bruckert et al., 2020).

Explanations serve to provide reasons for an observed state of affairs (Keil, 2006; Asterhan and Schwarz, 2009; Lombrozo, 2016). Often, causal explanations also serve to justify decisions made, i.e., provide reasons why a decision is “right” (Keil, 2006; Biran and Cotton, 2017). In the field of recommender systems, different types of explanations, in particular feature-based, personalized, and non-personalized explanations, have been identified and empirically investigated in terms of their effectiveness for recommending movies (Tintarev and Masthoff, 2012). In the context of recommender systems, an extensive user study demonstrated that explanations increase willingness to use the system again and that trust in system recommendations reduces cognitive effort (Pu and Chen, 2007).

Distributed cognition

Distributed cognition describes the phenomenon that knowledge exists not only inside the individual, but also in his or her surroundings and within a more complex context—for example, the social, physical, or digital environment (Hutchins, 1995; Zhang and Patel, 2006). These different knowledge domains are interconnected and can influence each other not only in individual, but also in broader collective and cultural contexts (Hoskins, 2016; Sutton, 2016). Therefore, they benefit from being analyzed and treated as a holistic system. Phenomena such as saving-enhanced memory (Storm and Stone, 2015) or the photo-taking impairment effect (Henkel, 2014; Soares and Storm, 2022) show that our digital environments can be used to outsource information and provide cognitive relief (Clark and Chalmers, 1998).

Surprisingly, most basic research on human-computer interaction has not explicitly attempted to investigate conditions and outcomes of these cross-connections and information transfer processes, and the ways that people use external anchors, tools, and storage options to support and relieve their cognitive resources are rather poorly understood (Perry, 2003). We argue that analyzing the connections between internal (i.e., human memory) and external (i.e., computer memory) cognition might not only lead to a better understanding of how the different domains are coordinated and connected; it would also provide an important basis for recommendations on how human-computer interaction processes can be supported. The fact that cognitions are not only distributed, but also connected, makes it possible to determine several starting points for possible interventions. Interventions with respect to dealing with large amounts of data and information overload could start either with the user or with the computer system. For example, a reduction in load could be achieved by deleting files, which externally limits the amount of information, removes potential distractors, organizes the work environment, and therefore contributes to mental relief (Chen et al., 2012). This could further help individuals stop distracting, task-irrelevant thoughts, focus on their actual work tasks, and improve well-being (e.g., Randall et al., 2014; Kluge and Gronau, 2018; Niessen et al., 2020b; Göbel and Niessen, 2021).

In this way, the concept of distributed cognition is important from various perspectives and provides an appropriate framework for comprehensively examining human-computer interactions and developing, designing, and optimizing corresponding assistive systems.

Development of hypotheses and research questions

As causal explanations show that there are relevant and intelligent considerations behind the system's suggestions (e.g., Keil, 2006; Biran and Cotton, 2017), we propose that explanations make it more likely that individuals will delete the proposed files (Hypothesis 1a). Moreover, we aim to replicate the finding that explanations lead to more trust in the system. Trust is defined as the willingness to rely on a technical system in an uncertain environment (Komiak and Benbasat, 2004; Meeßen et al., 2020) and has two components, one of which is more emotional and affective and one of which is more cognitive. Affective trust describes the user's feelings while relying on the technical system, whereas cognitive trust can be seen as the system's perceived trustworthiness (Komiak and Benbasat, 2006; Meeßen et al., 2020). Both affective and cognitive trust have been shown to have positive effects on intentions to adopt and work with technical agents (Meeßen et al., 2020) as well as on work outcomes and well-being (Müller et al., 2020), and thus should be considered when evaluating such systems. In line with previous research demonstrating that transparency is conducive to the development of trust (e.g., Pu and Chen, 2007; Pieters, 2011; Shin, 2021), we assume that providing explanations increases both affective and cognitive trust ratings (Hypothesis 1b).

Another important factor in this context is credibility. Described as the believability of information and its source (e.g., Fogg et al., 2001), credibility has been identified as one of the strongest predictors of trust in information systems at work (Thielsch et al., 2018). We assume that explanations generally increase the comprehensibility and transparency of the system's decisions. By providing information on why the system's suggestions are valid, users can better understand the reliability of the underlying processes. This should lead to increased credibility ratings (Hypothesis 1c).

Furthermore, explanations can reduce information uncertainty (Van den Bos, 2009), here the lack of information about why the system considers a file irrelevant (“why am I getting this particular file suggested”), and thus increase the likelihood of accepting the system's recommendations. As information uncertainty is often experienced negatively (e.g., Wilson et al., 2005) and can lead to ruminative thinking (Kofta and Sedek, 1999; Berenbaum et al., 2008), it might also negatively impact trust and credibility. Therefore, we not only hypothesize that explanations directly reduce information uncertainty (Hypothesis 1d), but also that information uncertainty in the system's proposals mediates the effect of explanations of the system's proposals on its acceptance (Hypothesis 2a), trust (Hypothesis 2b), and credibility (Hypothesis 2c).

It has already been shown that person-situation interactions predict how people deal with too much information in the related field of thought control (Niessen et al., 2020a). Building on these findings, we also assume that there are individual differences in the extent to which explanations support individuals' deletion of irrelevant files, trust in the AI system and finding the suggestions credible. Therefore, we investigated the moderating role of conscientiousness and need for cognition on the relation between explanations and acceptance, trust and credibility. As a personality trait, the need for cognition refers to people's tendency to engage in and enjoy thinking (Cacioppo and Petty, 1982). Individuals with a high need for cognition seek out for information to make sense of stimuli and events. Such individuals enjoy situations in which problem solving and reflection are required (Cacioppo et al., 1996). Therefore, we propose that individuals high in need for cognition have a stronger preference for thinking about the explanations, which helps them to delete irrelevant files (Hypothesis 3a), build trust (Hypothesis 3b) and credibility (Hypothesis 3c) and to reduce information uncertainty (Hypothesis 3d).

Conscientiousness is one of the Big Five personality dimensions (Barrick and Mount, 1991; Costa et al., 1991; Costa and McCrae, 1992). Conscientiousness includes the will to achieve, self-motivation, and efficaciousness, but also a dependability component that is related to orderliness, reliability, and cautiousness. We expect that conscientious individuals read and think about the explanations more deeply, as they are more cautious than less conscientious individuals. Moreover, individuals high in conscientiousness might find the explanations helpful for achieving their work goals, as deleting irrelevant files has positive consequences in terms of reduced information overload, and distractions. Therefore, we propose that conscientiousness moderates the impact of explanations on deletion of irrelevant files (Hypothesis 4a), building trust (Hypothesis 4b), and credibility (Hypothesis 4c), and on reducing information uncertainty (Hypothesis 4d).

We also hypothesize that deletion is not only an action that causes digital objects to be forgotten in external memory, but may also support intentional forgetting of associated memory content (Hypothesis 5; Bjork et al., 1998; Anderson and Hanslmayr, 2014).

Sparrow et al. (2011) showed that individuals were worse at recalling information that had been stored in external memory than information that had not been stored on the computer. This indicates that individuals need to be convinced that they will not need the information designated as irrelevant in the future in order to forget: they need to trust the system. Numerous studies on directed forgetting (Sahakyan et al., 2008; Foster and Sahakyan, 2011) and motivated forgetting (for a review, Anderson and Hanslmayr, 2014) support these assumptions. Here, we explore whether explanations can help users not only delete irrelevant information but also forget it in their memory. When they are informed why a file is irrelevant, individuals can make an informed decision, and if they accept the suggestion to delete, actually forget the file as well (research question).

The present research

We conducted three experiments to test our hypotheses. Participants regularly interacted with the AI system Dare2Del and processed deletion suggestions in all experiments. In the first experiment, we investigated how explanations affect users' attitudes (information uncertainty, trust), behavior (deleting files), and memory (forgetting irrelevant files). In the second experiment, we further enhanced the system's transparency and verifiability by giving users the opportunity to check the correctness of the suggestions. In the third experiment, we additionally varied memory processing depth of the to-be-deleted material to further elaborate and systematize effects on memory.

To estimate the required sample size, we conducted multilevel power analyses for cross-level interaction effects (Multilevel Power Tool; Mathieu et al., 2012). We elected to use an anticipated effect size in the small-to-medium range (0.25, p = 0.05, Power 95%) and followed parameter recommendations from Mathieu et al. (2012) and Arend and Schäfer (2019) and in order to conservatively estimate our sample sizes. Moreover, Experiment 1 (https://aspredicted.org/3YN_FYC) and Experiment 3 (https://aspredicted.org/MQT_TYG) were preregistered on aspredicted.org. All data are publicly available on OSF (https://osf.io/dk6en/).

Experiment 1

Method

Participants

The study was conducted with 61 undergraduates (majoring in psychology; 49 female, 12 male) from a German university. Mean age was 20.30 years (SD = 3.00, range 18–32). Participation was voluntary and participants received course credit as compensation.

Materials and procedure

The experiment was programmed with SoSciSurvey software (with additional php elements), conducted online, and lasted about an hour. During the experiment, participants were in contact with the experimenter via video chat. Before starting the experiment, demographic information (age, gender, and occupation), need for cognition, and conscientiousness were assessed with a questionnaire.

At the beginning of the experiment, participants were instructed that they would be testing a library system (see Supplementary material) at the university which digitally saves and manages dissertations, diploma, bachelor's, and master's theses (cover story). First, the participants' main task was to archive students' theses. Specifically, they had to process 36 emails from students who had sent their theses along with Supplementary information (short cover letter including author name, name of thesis, type of thesis, publication year). Participants then entered the important meta-information (title, author name, type of thesis, publication year) from all 36 emails into the digital library system and saved the email attachments automatically by pressing the respective button. After each email, they received brief feedback from the system that the entry had been saved. The emails were presented in random order. To ensure that archiving the theses always involved a comparable workload, all titles had a similar structure and consisted of two technical terms (e.g., “neuroticism and burnout”).

Second, participants were instructed to interact with an assistive system (Dare2Del) that helps to keep the digital library system tidy, without outdated or duplicate theses. The assistive system Dare2Del was described as an automatic software that detects irrelevant, identical and damaged files and suggests them for deletion. Participants were explicitly advised that the decision on whether to accept or reject the suggestion was completely up to them. Nevertheless, they were also encouraged to keep the archival system organized by using Dare2Del. While processing the emails, the assistive system popped up 12 times. Each time, a file was presented and suggested for deletion (see Supplementary material).

We systematically varied the explanation for why the file should be deleted: Six files were suggested without explanation, and for six files the assistive system provided a short explanation (three different explanations: thesis file is identical to another and was obviously saved twice; thesis file is outdated, and a newer version exists; thesis file was submitted at another university and therefore should not be in the system). Also, we systematically varied the familiarity of the files. Six of the to-be-deleted files were thesis files that the participants had previously saved into the archival system—that is, they had already entered the thesis titles into the system and saved the respective information. Six files, on the other hand, were completely new files that had not been presented before (unfamiliar files).

After processing all emails from students and suggestions for deletion from the assistive system, participants completed a recognition test. The 12 thesis file names the assistive system had suggested during the experiment (e.g., “narcissism and loneliness”) and 12 distractors (thesis file names with slightly modified titles; e.g., “egoism and loneliness”) were presented to the participants in a random order. Participants were asked to indicate whether they had processed the this exact title before or not. In this way, we assessed whether participants could correctly identify the original files they had dealt with before. At the end of the experiment, participants had the opportunity to make general comments and were then fully debriefed.

Research design

The experimental design included an explanation condition (suggestion with explanation, coded as 1, and without explanation, coded as 0; within-person) and need for cognition and conscientiousness (between-person, see Figure 1). Need for cognition was assessed with 33 items (e.g., “I really enjoy the task of finding new solutions for problems”; Bless et al., 1994; Cronbach's Alpha = 0.92), and conscientiousness with the NEO-PI-R (60 items; Ostendorf and Angleitner, 2004; e.g., “I work goal-oriented and effectively”, Cronbach's Alpha = 0.85).

FIGURE 1

Figure 1. Study model.

Dependent variables

We assessed five dependent variables. Firstly, we recorded whether the assistive system's suggestion to delete the file was accepted or not (no = 0; yes = 1). Secondly, after participants accepted or refused the suggestion, we measured trust with two components, namely cognitive trust (“I feel comfortable relying on the assistive system”) and affective trust (“I trust the assistive system completely”). The third dependent variable we measured was credibility (“The information given by the assistive system was credible”) and the fourth was information uncertainty (“I feel uncertain as to why the file was suggested for deletion, because I do not have enough information”). Trust, credibility, and information uncertainty were answered on a 5-point Likert scale ranging from 1 = do not agree at all to 5 = fully agree. Finally, we assessed the hit rate of the thesis names in the recognition test (no = 0; yes = 1).

Control variables

As control variable, we assessed the familiarity of the theses' titles (familiar, coded as 1, files processed by the participants; unfamiliar, coded as 0, new files not presented to participants). To consider possible effects of practice with the task, we further included a time variable in the model representing the position of Dare2Del's suggestion to delete a file (0–11). This variable makes it possible to detect systematic changes over time.

Results

Statistical analyses

Multilevel modeling and logistic multilevel modeling were used to conduct the within-person comparison of experimental conditions. Multilevel modeling presents a valuable alternative approach to traditional repeated measures analysis of variance (RM-ANOVA), as it is more robust to violations of assumptions, can handle missing data, and allows for testing more complex hierarchical structures (Cohen et al., 2003). We used R software and the packages lme4 (Bates et al., 2015) and mediation (Tingley et al., 2014) to conduct our analyses. All models were two-level models, with suggestions by Dare2Del with and without explanations, familiarity of the theses' titles, deletion decisions and recognition of files in the final recognition test nested within individuals at Level 2.

The continuous Level 1 (within) predictor variable information uncertainty was centered around the person mean (Nezlek, 2012), and the continuous Level 2 (between) predictor variables (need for cognition, conscientiousness) were centered around the grand mean. Dummy-coded predictor variables were entered uncentered, as were all outcome variables for the respective models.

We applied the two-step approach to causal mediation analysis documented by Imai et al. (2010) and Tingley et al. (2014): In the first step, the mediator variable is predicted by the predictor variable, and in the second step, the outcome variable is predicted by the predictor and mediator variables. The final mediation analysis is then run using quasi-Bayesian Monte-Carlo simulations (we used 10,000 simulations each), which is superior to previous mediation approaches as it overcomes problems such as dependence on specific statistical models or restrictive assumptions (cf. Imai et al., 2010; Pearl, 2014; Tingley et al., 2014).

N_Level2was 61, the number of the participants. Due to technical problems, we had to exclude two Level 1 datapoints, leading to an N_Level1of 730 (61 participants × 12 deletion proposals-−2 excluded datapoints). Overall, participants accepted about one third (36%) of the system's deletion proposals. In the final recall test, 48% of the files were identified correctly. An overview of all Level 1 and Level 2 variable correlations is provided in Tables 1, 2, respectively.

TABLE 1

Table 1. Experiment 1: Means, standard deviations, and correlations of Level 1 variables.

TABLE 2

Table 2. Experiment 1: Means, standard deviations, and correlations of Level 2 and aggregated Level 1 variables.

Hypothesis testing

First, we tested the hypothesis that explanations lead to higher acceptance of the assistive system's suggestions, to more cognitive and affective trust, more credibility, and less information uncertainty (Hypotheses 1a–d). To do so, we calculated (logistic) multilevel regression analyses. In line with our expectations, the presence of explanations led to higher acceptance of the deletion suggestions (γ = 3.96, SE = 0.31, z = 12.63, p < 0.001). However, explanations did not increase trust (cognitive trust: γ = −0.02, SE = 0.06, t = −0.37, p = 0.709; affective trust: γ = −0.02, SE = 0.07, t = −0.24, p = 0.814) or the credibility of the system (γ = 0.02, SE = 0.08, t = 0.20, p = 0.840). Contrary to our expectations, explanations for the suggestions increased rather than decreased information uncertainty (γ = 0.21, SE = 0.09, t = 2.30, p = 0.021). However, it should be noted that due to simultaneous testing of up to five dependant variables, the p-value needs to be adjusted down to 0.01 (0.05/5; Haynes, 2013).

The familiarity of the files, which we added as a control variable to our analyses to examine possible effects of different levels of cognitive processing, led to more cognitive and affective trust, more credibility and less uncertainty (see Table 3). However, it did not affect acceptance of the suggestions. The results of the time variable revealed a decrease in cognitive and affective trust and an increase in information uncertainty over time (see also Table 3). These findings are somewhat unexpected and need to be further examined and discussed.

TABLE 3

Table 3. Experiment 1: Effects of explanations on acceptance of deleting proposal, cognitive trust, affective trust, credibility, and uncertainty.

Second, we tested the mediating role of information uncertainty with regard to acceptance of the system's suggestions, cognitive trust, affective trust, and credibility. Information uncertainty did not mediate the effect of explanations on acceptance of the deletion suggestions (indirect effect = −0.00, 95% CI [−0.01; 0.00], p = 0.310). However, we found indirect effects for cognitive trust (indirect effect = −0.07, 95% CI [−0.13; −0.01], p = 0.020), affective trust (indirect effect = −0.09, 95% CI [−0.16; −0.01], p = 0.020), and credibility (indirect effect = −0.12, 95% CI [−0.22; −0.02], p = 0.020), but as with the results for Hypothesis 1, the direction of effects was unexpected: Explanations created more information uncertainty, which resulted in less cognitive trust, less affective trust, and less credibility. Thus, Hypotheses 2a–c were not confirmed, although they highlighted the mediating role of information uncertainty.

In the next step, we tested whether need for cognition (Hypotheses 3a–d) and conscientiousness (Hypotheses 4a–d) moderated the effect of explanations on acceptance, cognitive trust, affective trust, credibility, and information uncertainty. To do so, we calculated cross-level interactions. However, none of them turned out to be significant for either need for cognition (all zs/ts < |1.14|, all ps > 0.252) or conscientiousness (all zs/ts < |1.28|, all ps > 0.202) as a moderator. Therefore, we had to completely reject Hypothesis 3 and Hypothesis 4. We further investigated whether deleting a file led to subsequent forgetting. Confirming Hypothesis 5, deleting a file was associated with a lower recognition probability (γ = −1.69, SE = 0.19, z = −8.69, p < 0.001).

To explore possible effects of explanations on the subsequent accessibility of the corresponding memory traces (research question), we calculated additional multilevel regression analyses. The results revealed that the presence of explanations for a suggestion to delete a thesis file was associated with a lower probability of recognizing its title in the recognition test (γ = −2.17, SE = 0.22, z = −9.68, p < 0.001), thus indicating difficulties in retrieval (which corresponds to forgetting).

Additional analyses

As explanations were positively associated with deleting a file (Hypothesis 1a), we further conducted a two-step causal mediation analysis to test whether the act of deletion mediates the effect of explanations on subsequent forgetting. The indirect effect was not significant (indirect effect = −0.04, 95% CI [−0.09; 0.01], p = 0.110), but the direct effect from explanations on the recognition rate was again confirmed (direct effect = −0.46, 95% CI [−0.54; −0.37], p < 0.001). Unexpectedly, time and familiarity of files did not affect final recall rates.

In sum, as expected, explanations led to higher acceptance of the deletion suggestions and to more forgetting of the files. Contrary to our hypotheses, explanations did not increase trust or credibility of the system, but increased information uncertainty, which led to less trust. Moreover, over the course of the experiment, trust actually decreased and information uncertainty increased.

Experiment 2

To further explore the surprising effects of explanations on trust, credibility, and information uncertainty, we conducted a second experiment with two major changes. First, we assumed that the negative effects of explanations on trust, credibility, and information security in Experiment 1 were because participants were not able to check the explanations and suggestions in the file system. As a result, they simply accepted the suggestions blindly, but did not trust them, did not find the system credible, and felt more uncertain. In Experiment 2, we provided the possibility to check the explanations and suggestions by looking at the files in the folder (see Supplementary material). Therefore, we were able to assess participants' trust in a more objective manner (with less checking of explanations indicating more trust). Second, we modified the kind of explanations, so that acceptance of the suggestion would imply definite and final removal (in contrast to the deletion of duplicates, where one of the original files continues to exist in the file system). Therefore, we partly changed the explanations' content (e.g., thesis was submitted at a foreign university that was not part of the literature network system).