Start running your first data job on Powerdrill Enterprise
$PD_API_KEY
with the API key you’ve obtained in Step 1 and $UID
with your user ID in the target project.curl --request POST \
--url https://ai.data.cloud/api/v2/team/datasets \
--header 'Content-Type: application/json' \
--header 'x-pd-api-key: $PD_API_KEY' \
--data '{
"name": "My dataset",
"description": "my default dataset",
"user_id": "$UID"
}'
{
"code": 0,
"data": {
"id": "dset-cmc1nh2e2lqf507retfodc0dn"
}
}
id
value (dataset ID) from the response and save it for later use.
POST /v2/team/datasets/{id}/datasources
endpoint. Replace the id
value with the ID of the dataset you’ve created in the previous sub-step.
$PD_API_KEY
with the API key you’ve obtained in Step 1 and $UID
with your user ID in the target project.url
or file_key
, but not both. Use url
to upload a file through a publicly accessible URL. For privately accessible files, use file_key
.curl --request POST \
--url https://ai.data.cloud/api/v2/team/datasets/dset-cmc1nh2e2lqf507retfodc0dn/datasources \
--header 'Content-Type: application/json' \
--header 'x-pd-api-key: $PD_API_KEY' \
--data '{
"name": "test.pdf",
"type": "FILE",
"user_id": "$UID",
"url": "https://arxiv.org/pdf/2406.12660v1"
}'
{
"code": 0,
"data": {
"id": "ds-cmc1ntbw105ug07j49zei4kcb",
"name": "test.pdf",
"type": "FILE",
"status": "synching",
"dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn"
}
}
$PD_API_KEY
with the API key you’ve obtained in Step 1 and $UID
with your user ID in the target project.curl --request POST \
--url https://ai.data.cloud/api/v2/team/sessions \
--header 'Content-Type: application/json' \
--header 'x-pd-api-key: $PD_API_KEY' \
--data '{
"name": "My session",
"output_language": "EN",
"job_mode": "AUTO",
"max_contextual_job_history": 10,
"user_id": "$UID"
}'
{
"code": 0,
"data": {
"id": "bc9a8127-4214-42b2-bbbe-a022f23d9795"
}
}
id
value (session ID) from the response and save it for use in the following step.
stream
parameter. For more details about how to understand the streaming mode, see Streaming.
stream
is set to true, streaming is enabled.
stream
is set to false, streaming is disabled.
$PD_API_KEY
with the API key you’ve obtained in Step 1 and $UID
with your user ID in the target project.
session_id
value with the ID of the session you’ve created in Step 3.
dataset_id
to the ID of the dataset obtained in Step 2.
datasource_ids
field.
curl --request POST \
--url https://ai.data.cloud/api/v2/team/jobs \
--header 'Content-Type: application/json' \
--header 'x-pd-api-key: $PD_API_KEY' \
--data '{
"session_id": "bc9a8127-4214-42b2-bbbe-a022f23d9795",
"user_id": "$UID",
"stream": true,
"question": "introducing the dataset",
"dataset_id": "dset-cmc1nh2e2lqf507retfodc0dn",
"datasource_ids": [
"ds-cmc1ntbw105ug07j49zei4kcb"
],
"output_language": "EN",
"job_mode": "AUTO"
}'
Example response:
event:JOB_ID
data:job-cmc1ohgsllys907reaaowv4fl
id:9fbe40de-5c5b-4e27-b82e-ecf727b15248
event:TASK
data:{"id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","model":"","choices":[{"delta":{"content":{"name":"Analyze","id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","status":"running","parent_id":null,"stage":"Analyze","properties":{}}},"finish_reason":null,"index":0}],"created":1750234590,"group_id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","group_name":"Analyze","stage":"Analyze"}
:keep-alive
id:9fbe40de-5c5b-4e27-b82e-ecf727b15248
event:TASK
data:{"id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","model":"","choices":[{"delta":{"content":{"name":"Analyze","id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","status":"running","parent_id":null,"stage":"Analyze","properties":{"files":"test.pdf"}}},"finish_reason":null,"index":0}],"created":1750234590,"group_id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","group_name":"Analyze","stage":"Analyze"}
id:9fbe40de-5c5b-4e27-b82e-ecf727b15248
event:TASK
data:{"id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","model":"","choices":[{"delta":{"content":{"name":"Analyze","id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","status":"done","parent_id":null,"stage":"Analyze","properties":{"files":"test.pdf"}}},"finish_reason":null,"index":0}],"created":1750234591,"group_id":"9fbe40de-5c5b-4e27-b82e-ecf727b15248","group_name":"Analyze","stage":"Analyze"}
id:f5e680eb-3762-481b-826f-483a8e74e268
event:TASK
data:{"id":"f5e680eb-3762-481b-826f-483a8e74e268","model":"","choices":[{"delta":{"content":{"name":"Search summary","id":"f5e680eb-3762-481b-826f-483a8e74e268","status":"running","parent_id":null,"stage":"Analyze","properties":{}}},"finish_reason":null,"index":0}],"created":1750234592,"group_id":"f5e680eb-3762-481b-826f-483a8e74e268","group_name":"Search summary","stage":"Analyze"}
id:f5e680eb-3762-481b-826f-483a8e74e268
event:SOURCES
data:{"id":"f5e680eb-3762-481b-826f-483a8e74e268","model":"","choices":[{"delta":{"content":[{"id":"1","source":"test.pdf","page_no":null,"content":"summary: test.pdf\nAn experiment with 562 participants investigated the impact of Explainable AI (XAI) and AI literacy on user compliance. Results revealed that XAI boosts compliance, influenced by AI literacy, with the relationship mediated by users' mental model of AI. This study highlights the importance of XAI in AI-based system design. It explores the connection between AI literacy, mental models, XAI techniques, and user compliance with AI recommendations. The research also examines the effect of presenting different XAI types on user compliance. An AI artifact was developed to predict age from photographs, offering personalized explanations to enhance decision-making and compliance with AI recommendations. The study delves into AI interpretability, AI literacy, explainable AI models, and their influence on user behavior. It discusses advancements in AI, machine learning, and user interaction, addressing areas like facial recognition, digital resilience, and algorithmic fairness.","datasource_id":"ds-cmc1ntbw105ug07j49zei4kcb","dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn","file_type":".pdf","external_id":null},{"id":"2","source":"test.pdf","page_no":null,"content":"summary: test.pdf\nAn experiment with 562 participants investigated the impact of Explainable AI (XAI) and AI literacy on user compliance. Results revealed that XAI boosts compliance, influenced by AI literacy, with the relationship mediated by users' mental model of AI. This study highlights the importance of designing AI systems with XAI for better user engagement.","datasource_id":"ds-cmc1nrv4a05ue07j4vscij2z7","dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn","file_type":".pdf","external_id":null},{"id":"3","source":"test.pdf","page_no":null,"content":"7 \nThe decision for a data set for building an AI for age estimation is tightly bound to the current research basis on ML \nmodels for age estimation. Age estimation has been of particular interest in the ML community, and many researchers have \ntackled the task of predicting the age of a person on an image [11,47]. The largest and most popular data set is the IMDB-\nWIKI data set [47], which we utilize for training our AI. For our implementation, we take advantage of the source code \npublished by Serengil [51], with minor adjustments in Python, using the popular keras package. The model itself is based \non a CNN, which uses the VGG-16 architecture and is pre-trained on the FaceNet database [50]. The network architecture \nis then adjusted to the age estimation task and our specific data set. \nWhile the IMDB-WIKI data set is widely used as a training basis for age estimation models and the use of existing, \npublished models makes them convenient to use, there are multiple reasons for which the pictures in this data set cannot \nbe used for display (≠ model training) in our study; the quality of the images varies vastly, the ages of the persons are not \nvalidated, and the data set contains many pictures of celebrities. Especially the latter could falsify the participants' \nperformance as they might have existing knowledge of the age of a person. Another factor that might have an unintended \neffect on the study is the fact that the images are taken “in the wild”, meaning that there is no standard way of how the \npeople are shown in the image. The people are pictured in various ways, with different poses, facial expressions like smiles \nor laughter, and clothing like sunglasses, headgear, or jewelry. To address these shortcomings, we use the MORPH data \nset Feld for model adoption and presentation to the study participants[46]. It has been specifically developed for research \npurposes and contains the actual age of the people depicted in the pictures. While there are multiple versions of MORPH, \nthe non-commercial release MORPH-II has become a benchmark data set for age recognition [7]. The MORPH-II data set \ncontains unique images of more than 13,000 individuals. \nAfter the model is built, we test its performance in a 10% holdout set, which will also be used within the experiment \nlater. The performance of the models for age prediction is often evaluated by their mean absolute error (MAE). After \ntraining and optimization procedures, we reach an MAE of ~2.9 on the MORPH-II data set, which is in line with other \nresearchers [1,54]. This means, on average, our model has an error boundary of +/-3 years when predicting the age. \nAs stated above, we generate two fundamentally different types of explanations, a chart showing the probability \ndistribution for each age (“XAI1”, in-model [2]) and an overlay on an image showing particularly relevant parts of the \npicture for the AI’s prediction (“XAI2”, post-model [45]). For the probability distributions, we plot a bar chart that depicts \nthe probabilities—more precisely, the softmax values [56]—for each of the 40 most probable ages. The bars which \ncorrespond to the five most probable ages are highlighted in red. An example of such a bar chart, as presented to the \nparticipants, is depicted in Figure 3. Note that the probabilities are relatively low, which is rooted in the fact that the \nprobabilities for each of the 101 classes add up to 100%. The model often generates somewhat similar probabilities for \nages that are close to each other.","datasource_id":"ds-cmc1ntbw105ug07j49zei4kcb","dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn","file_type":".pdf","external_id":null},{"id":"4","source":"test.pdf","page_no":"10","content":"10 \nAs both between-subject and within-subject analyses show significant results, we can support hypothesis 1.1. From an \nanalysis of the boxplot in Figure 5 on p. 11, we see that compliance not only changes but increases with the introduction \nof XAI. Thus, our first finding is: \nFinding 1.1: The introduction of explainability in AI (XAI) increases users’ compliance with the recommendations of \nAI. \nAs our Post Hoc Analysis in Table 2 also reveals, we cannot find significant differences between our treatments \nregarding XAI1 and XAI2. This means we reject hypothesis 1.2. \n \nTable 2: Significance levels of ANOVA and Multiple Comparison of Means with Tukey for Between-subject perspective \n \nCompliance \nANOVA \nAll groups compared \n*** \nMultiple Comparison \nof Means with Tukey \n \nCG ⟷ XAI1 \n*** \nCG ⟷ XAI2 \n*** \nXAI1 ⟷ XAI2 \nn.s. \nNotes: *p < 0.05, **p < 0.01, ***p < 0.001, n.s. = not significant \n \nTable 3: Two-sided t-test comparing compliance with AI before and after treatment \n \nCompliance \nAI1 (Baseline, Stage 1) ⟷ XAI1 (Stage 2) \n*** \nAI2 (Baseline, Stage 1) ⟷ XAI2 (Stage 2) \n* \nNotes: *p < 0.05, **p < 0.01, ***p < 0.001, n.s. = not significant","datasource_id":"ds-cmc1ntbw105ug07j49zei4kcb","dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn","file_type":".pdf","external_id":null},{"id":"5","source":"test.pdf","page_no":null,"content":"11 \n \n \nFigure 5: Mean absolute difference (MAD) boxplot of ANOVA for Between-subject perspective of compliance \n5.2 XAI effects on mental model \nWe are not only interested in if and how XAI changes participants’ compliance with the recommendations of AI, but we \nalso investigate potential changes in their MMs. To do so, we first need to set a few statistical prerequisites to ensure the \neligibility of our data. To assess the validity and the reliability of our MM construct, we conduct a confirmatory factor \nanalysis and assess the results with respect to multiple measures. As measures for convergent reliability, we examine \nCronbach’s alpha (CA), average variance extracted (AVE) and composite reliability (CR). \nTable 4: Measurement Information for Latent Factors of Mental Model Construct \n \nAs depicted in Table 4, for all included cases, the constructs of MM GOAL, TASK and PROCESS, the CA, AVE, and CR \nare above the recommended thresholds. A confirmatory factor analysis reveals that factor loadings on all items load highly \n(>0.65) on one factor and with low cross-loadings. These findings demonstrate that our constructs are robust and can be \n \nControl group w/o XAI \nTreatment with XAI1 \nTreatment with XAI2 \nGOAL \nTASK \nPROC \nGOAL \nTASK \nPROC \nGOAL \nTASK \nPROC \n1st order \nReliability \nCA \n0.825 \n0.950 \n0.894 \n0.813 \n0.945 \n0.904 \n0.876 \n0.939 \n0.902 \nCR \n0.832 \n0.951 \n0.894 \n0.816 \n0.945 \n0.905 \n0.879 \n0.939 \n0.902 \nAVE \n0.625 \n0.866 \n0.739 \n0.597 \n0.851 \n0.762 \n0.709 \n0.838 \n0.755 \n2nd order \nReliability \nCR \nMM: \n0.705 \nMM: \n0.757 \nMM: \n0.772 \nNotes: CA = Cronbach’s alpha, CR = composite reliability, AVE = average variance extracted","datasource_id":"ds-cmc1ntbw105ug07j49zei4kcb","dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn","file_type":".pdf","external_id":null},{"id":"6","source":"test.pdf","page_no":null,"content":"16 \nstatistical figures and language, it will be useful to conduct future experiments where simple explanations using plain \neveryday language are utilized to test whether they assist in enhancing compliance of users with low AI literacy. \nBeyond the discovery of the above two phenomena, our study further extends the work in IS on MMs. Existing literature \nin IS has mostly focused on how to measure MMs, while the impact of MMs on actual user behavior is scarce so far. With \nour findings, we increase the understanding of how MMs influence human-AI-interaction, more specifically, their impact \non the compliance of users with AI’s recommendations. With this new understanding, we emphasize that users' MM is a \nvariable that researchers and practitioners need to consider when designing and introducing AI. \n7 CONCLUSION \nThe importance of AI-based systems is on the rise. However, more exploration into the relationship between humans and \nAI systems is needed, especially to understand the impact of explanations on users’ compliance with the AI \nrecommendations. \nIn the current study, we elaborate on the relationship between different explainable AI (XAI) methods, users’ AI \nLiteracy, MMSs, and compliance with AI recommendations. We layout a research model and an experimental survey \nsetup. We perform a study with 562 participants who estimate the age of multiple persons—once with the help of an AI \nand once with different treatments of XAI. \nOur overall results show that people’s compliance with the recommendation of AI increases with the introduction of \nXAI. Furthermore, we demonstrate that the introduction of XAI changes users’ MMs of AI. As analyzed with our full \nstructured equation model, the mental model, in turn, significantly influences users’ compliance with the recommendation \nof AI as well. As MMs originate from the background and experience of people, it is not surprising that their AI Literacy, \ni.e., their AI skills and usage, influences their compliance with the recommendations of AI as well. In a subsequent analysis, \nwe even find that by differentiating participants into “low” and “high” AI Literacy groups, we can identify that XAI plays \ndifferent roles for these groups; The type of XAI has no effect on the compliance of participants with low AI Literacy. \nHowever, for participants with high AI Literacy, the type of XAI played a significant role. \nWith these insights, we contribute to the body of knowledge by shedding more light on the relationships between XAI \nand compliance and the related personal characteristics of the users. We show the importance of personalizing XAI as a \nfunction of users’ background and experience, i.e., their AI Literacy. We believe this article should start a debate on the \nnecessity of personalized XAI (PXAI). For instance, in the case of medicine, certain types of XAI—like the visual \nexplanation XAI2 from our treatment—might help doctors better understand the recommendation of an AI-based systems. \nHowever, when presenting explanations to different patients, different XAI techniques might be required, as their MMs \nand expertise are probably at different levels. Therefore, we believe PXAI should be the next frontier in user-centric XAI \nresearch. \nThe generalizability of these results is subject to certain limitations. For instance, other relationships might exist that \nwe did not model in our setup. An additional restrictive factor A limitation of this study is the fact that we only included \none use case with two different types of XAI. Future work needs to implement additional use cases, especially within \nspecialized domains like medicine, and also study the impact of other XAI techniques. Such work would further deepen \nour understanding of the influence of XAI on compliance—and might also help to shed more light on the role of the mental \nmodel. A promising field of research lies ahead.","datasource_id":"ds-cmc1ntbw105ug07j49zei4kcb","dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn","file_type":".pdf","external_id":null},{"id":"7","source":"test.pdf","page_no":null,"content":"12 \nfurther used in the upcoming analyses. To examine if and how the MMs of participants change with the introduction of \nXAI with a statistical test, we first need to test for measurement invariance. Measurement invariance is a statistical property \nof measurement that indicates that the same construct is being measured across some specified groups. Precisely, this \nmeans we need to eliminate the possibility that changes in the latent variable between measurement occasions (before and \nafter the treatment) are not attributed to actual change in the latent construct. In the case of an experimental study, this \nmeans we need to eliminate a change in the “psychometric” properties of the measurement instrument, i.e., the construct \nhad a different meaning for the participants at measurement occasions. We test the construct of MM, consisting of the \nsubconstructs GOAL, TASK, and PROCESS for metric, scalar, and strict invariance. To compare the means, we require \nat least scalar invariance [44]. \nIn our case, both metric and scalar invariance are not significant, while strict invariance is significant at the 0.05 level. \nThis means we can compare the latent means of the constructs from before and after the treatment. The results of this \ncomparison are depicted in Table 5. \nThe values show that the MM changes significantly with the introduction of XAI. For XAI1, the constructs TASK and \nPROCESS increase by 0.172 and 0.240, respectively. In the case of XAI2, the PROCESS construct changes significantly; \nmore precisely, it increases by 0.3. We can observe no significant change in the GOAL construct, which is, however, not \nsurprising, as the goal of the decision task didn’t change. In summary, we can support hypothesis 2.1 and conclude: \nFinding 2.1: The introduction of XAI has a positive association with users’ mental models of AI. \nTable 5: Comparison of latent mean differences across measurement occasions with Wald-Test \n5.3 Analysis of mental model and AI Literacy \nWe estimate a full structural equation model (SEM) to better understand the interplay of the different variables considered \nin our study, we estimate a full structural equation model (SEM). Besides the mental model (MM), compliance (COMP), \nand the type of XAI (XAI_T), we also include AI Literacy (AILIT). AI Literacy is modeled as the sum of AI Skills and AI \nUsage. A correlation analysis of the control group reveals that AILIT, MM, and COMP are not considerably correlated \n(<0.3). Table 6 provides the assessment of our model fit. All indices except for Chi-square are within their required \nTreatment \nConstruct \nEstimate \nSE \nz-value \nStd.lv \nStd.all \nXAI1 \nGOAL \n0.029 \n0.061 \n0.467 \n0.027 \n0.027 \nTASK \n0.168** \n0.055 \n3.036 \n0.172 \n0.172 \nPROC \n0.229*** \n0.050 \n4.601 \n0.240 \n0.240 \nXAI2 \nGOAL \n0.063 \n0.072 \n0.873 \n0.061 \n0.061 \nTASK \n0.093 \n0.068 \n1.362 \n0.091 \n0.091 \nPROC \n0.294*** \n0.058 \n5.051 \n0.300 \n0.300 \nNotes: *p < 0.05, **p < 0.01, ***p < 0.001, SE = standard error, Std.lv = standardized estimates (latent), Std.lv = standardized \nestimates (all)","datasource_id":"ds-cmc1ntbw105ug07j49zei4kcb","dataset_id":"dset-cmc1nh2e2lqf507retfodc0dn","file_type":".pdf","external_id":null}]},"finish_reason":null,"index":0}],"created":1750234596,"group_id":"f5e680eb-3762-481b-826f-483a8e74e268","group_name":"Search summary","stage":"Analyze"}
id:f5e680eb-3762-481b-826f-483a8e74e268
event:TASK
data:{"id":"f5e680eb-3762-481b-826f-483a8e74e268","model":"","choices":[{"delta":{"content":{"name":"Search summary","id":"f5e680eb-3762-481b-826f-483a8e74e268","status":"done","parent_id":null,"stage":"Analyze","properties":{}}},"finish_reason":null,"index":0}],"created":1750234596,"group_id":"f5e680eb-3762-481b-826f-483a8e74e268","group_name":"Search summary","stage":"Analyze"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:TASK
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"content":{"name":"Answer","id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","status":"running","parent_id":null,"stage":"Respond","properties":{}}},"finish_reason":null,"index":0}],"created":1750234597,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"Okay, let's try a different perspective and provide more details about the dataset, focusing on its potential uses and limitations:\n"},"finish_reason":null,"index":0}],"created":1750234597,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"\n"},"finish_reason":null,"index":0}],"created":1750234597,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"**Dataset as a Tool for Understanding Human-AI Interaction:**\n"},"finish_reason":null,"index":0}],"created":1750234597,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"\n"},"finish_reason":null,"index":0}],"created":1750234597,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"Instead of just listing the components, let's frame the dataset as a tool for researchers and practitioners interested in building *trustworthy* AI systems. The core value of this dataset lies in its ability to:\n"},"finish_reason":null,"index":0}],"created":1750234597,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"\n"},"finish_reason":null,"index":0}],"created":1750234597,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Quantify the impact of XAI:** The dataset allows researchers to measure how different XAI techniques (the probability distribution chart vs. the image overlay) affect user compliance with AI recommendations. This is crucial for determining which types of explanations are most effective in different contexts.\n"},"finish_reason":null,"index":0}],"created":1750234598,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Uncover the role of AI literacy:** By including measures of AI skills and usage, the dataset enables analysis of how prior knowledge and experience with AI influence a user's response to explanations. This is vital for tailoring XAI to specific user groups.\n"},"finish_reason":null,"index":0}],"created":1750234598,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Model the influence of mental models:** The inclusion of a mental model construct allows researchers to investigate how XAI shapes users' understanding of how the AI works, and how this understanding, in turn, affects their willingness to follow the AI's advice. This provides a deeper understanding of the cognitive processes involved in human-AI collaboration.\n"},"finish_reason":null,"index":0}],"created":1750234598,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Inform the design of personalized XAI (PXAI):** The findings from analyzing this dataset can be used to develop personalized XAI systems that adapt explanations based on a user's AI literacy and mental model. This is a key step towards building AI that is both effective and understandable.\n"},"finish_reason":null,"index":0}],"created":1750234599,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"\n"},"finish_reason":null,"index":0}],"created":1750234599,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"**Limitations and Considerations:**\n"},"finish_reason":null,"index":0}],"created":1750234599,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"\n"},"finish_reason":null,"index":0}],"created":1750234599,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"It's also important to acknowledge the limitations of the dataset:\n"},"finish_reason":null,"index":0}],"created":1750234599,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"\n"},"finish_reason":null,"index":0}],"created":1750234599,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
:keep-alive
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Single Use Case:** The study focuses on a single task (age estimation) and a limited set of XAI techniques. The results may not generalize to other domains or other types of explanations. As the original paper mentions, future work should include additional use cases, especially within specialized domains like medicine.\n"},"finish_reason":null,"index":0}],"created":1750234600,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Image Quality and Demographics:** While the MORPH dataset addresses some issues with the IMDB-WIKI dataset, there might still be biases related to the demographics represented in the images. The dataset should be carefully examined for potential biases before being used to train or evaluate AI systems.\n"},"finish_reason":null,"index":0}],"created":1750234600,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Online Experiment Setting:** The online experiment setting may introduce biases related to participant attention and motivation. The use of attention checks helps to mitigate this, but it's still a factor to consider.\n"},"finish_reason":null,"index":0}],"created":1750234600,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Self-Reported Measures:** The measures of AI literacy and mental models are based on self-reported data, which may be subject to biases.\n"},"finish_reason":null,"index":0}],"created":1750234601,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"* **Specific XAI Implementations:** The specific implementations of XAI1 (probability distribution) and XAI2 (LIME overlay) might influence the results. Different implementations of these techniques could lead to different outcomes.\n"},"finish_reason":null,"index":0}],"created":1750234601,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"\n"},"finish_reason":null,"index":0}],"created":1750234601,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:MESSAGE
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"role":null,"content":"**In summary:** This dataset is a valuable resource for studying the impact of XAI on user compliance and understanding. However, it's crucial to be aware of its limitations and to interpret the results in the context of the specific task, XAI techniques, and participant population used in the study. The dataset provides a foundation for further research into personalized and trustworthy AI.\n"},"finish_reason":null,"index":0}],"created":1750234601,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
id:abab8d59-57d6-4e4e-8454-a03a264fb04b
event:TASK
data:{"id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","model":"","choices":[{"delta":{"content":{"name":"Answer","id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","status":"done","parent_id":null,"stage":"Respond","properties":{}}},"finish_reason":null,"index":0}],"created":1750234601,"group_id":"abab8d59-57d6-4e4e-8454-a03a264fb04b","group_name":"Answer","stage":"Respond"}
event:END_MARK
data:[DONE]