Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" - a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instance-level explanations.



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1. Introduction

As Machine Learning (ML) increasingly becomes an integral part of many computer programs, its impact on our society spans a wide spectrum of domains.

Figure 1. The interface: Examples are presented in the blue box at the top. The task is shown in the green box at the bottom. All participants worked on the same tasks and where shown the same examples. Conditions differed only in terms of the additional information that was presented alongside each example. Here, saliency maps and scores are shown.

Some systems have already been shown to outperform humans at certain tasks like lung cancer screening (Ardila et al., 2019). With the ambition to increase efficiency and reduce cost, many public and private organisations are adopting “data-driven” ML systems to support or even take decisions around applications that range from predictive policing (Mohler et al., 2015), to healthcare (Cai et al., 2019), to social services (Kleinberg et al., 2018), and many others (Stone et al., 2016; Campolo et al., 2018). Therefore, there have been several calls to make such systems accountable, so that even users who are not ML experts could decide when to trust their predictions (Shneiderman, 2016; The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, 2017). However, many ML algorithms currently operate as black boxes. When trained with large amounts of data they may perform very well, but understanding the underlying process by which results are achieved is difficult, even for experts. In other words, transparency is still a fundamental and open technical challenge (Lipton, 2018). This is especially the case for one of the most popular, and best performing types of ML systems: deep neural networks.

We are particularly interested in image classification using convolutional neural networks (CNNs), as this is an area for which some of the most impressive ML results have been reported to date, and with a broad range of applications (Pouyanfar et al., 2018). Within this domain, a popular approach to try and make those systems explainable is to produce “saliency maps” (also called “heat-maps”) that highlight which pixels were most important for the image classification algorithm. The claim is that such explanations are easy to interpret by both novice and expert users, and that they can help to detect unexpected behaviour (Lapuschkin et al., 2019), and develop appropriate trust towards the system (Ribeiro et al., 2016). Even though several algorithms have been published to produce such saliency maps from CNNs (Doshi-Velez and Kim, 2017), very limited research effort regarding their evaluation with actual users has been reported (Narayanan et al., 2018; Yin et al., 2019).

To address this research gap, in this paper we report on an online user study designed to evaluate the performance of saliency maps generated by a state of the art algorithm: layerwise relevance propagation (LRP) (Bach et al., 2015). Overall, 64 participants were asked to predict whether an already trained CNN model would recognise an object in an image based on similar examples, and to explain their prediction – a metric previously proposed to evaluate how explainable a system is (the rationale being that if users understand how the system works, they should be able to predict its output (Muramatsu and Pratt, 2001)). We used a full-factorial between-group study design. Consequently, half of the participants were shown saliency maps for the example images, while the other half were not. Moreover, half of them were shown detailed information about the classification scores produced by the CNN. Our results indicate that when saliency maps were available, participants answered correctly more frequently than when they were absent (60.7% vs. 55.1%, p = 0.045). However, the overall performance was generally low even with the presence of saliency maps.

Our data indicates that saliency maps influenced people to notice saliency-maps-features. However, it is unclear if such explanation draw them away from considering other attributes that are usually not highlighted by saliency maps.

Drawing on our findings we highlight several limitations of saliency maps and the resulting implications for design and further research on explainable AI. In particular, we argue that the HCI and AI communities should explore explanation techniques beyond instance-level explanations.

2. Related Work

2.1. Explaining Predictions with Saliency

While saliency maps have been used in several applications such as the prediction of human eye fixation on images (Bylinskii et al., 2019; Volokitin et al., 2016), in this paper, we focus on the application of saliency maps to explain the behaviour of a CNN model. A large body of literature proposed a variety of different solutions to improve the intelligibility of machine learning models. For literature reviews, we refer the interested reader to (Lipton, 2018; Guidotti et al., 2018; Adadi and Berrada, 2018). One stream in this research field seeks to explain black-box model predictions with post-hoc explanations without uncovering the mechanisms behind them (Lipton, 2018). Solutions range from rendering of prototypical examples (Nguyen et al., 2016), textual explanations (Hendricks et al., 2016), to displaying examples that are similar to a given input (Kim et al., 2014; Cai et al., 2019).

A particularly popular group of techniques is feature-attribution (Lipton, 2018)

. For a given input, a relevance score is calculated for each input feature. Several approaches for calculating the relevance of input features have been proposed. One estimation method for calculating relevance scores is sensitivity analysis

(Simonyan et al., 2013). For a given sample, some measure of variation (e.g. the gradient) is evaluated. This way, a relevance score can be assigned to each input variable. Given a sample, Layer-wise Relevance Propagation (LRP) (Bach et al., 2015) produces relevance scores by starting at the output of a NN and propagating the output back to the first layer. The propagation through the network is governed by different propagation rules. The resulting explanations can be tuned to have different properties with theses rules.

2.2. Interacting with interpretable Systems

How users understand systems is a core research interest of the HCI community. Consequently, a large body of relevant literature exists. For example, reflections of the potential impact of deep neural networks (DNN) on interpretability date back as early as 1992 (Dix, 1992). Other more fundamental theoretical work is centered around the theoretical construct of mental models, a users’ internal representation of a system(Norman, 2014; Moray, 1999). If mental models are sufficiently accurate, they enable an interaction with a system that is more efficient (Boulay et al., 1999; Bayman and Mayer, 1984; Kieras and Bovair, 1984) and more satisfactory (Cramer et al., 2008; Kulesza et al., 2012). However, when flawed they may cause confusion, misconceptions, dissatisfaction and erroneous interactions (Kulesza et al., 2015; Tullio et al., 2007). Similarly, the overestimation of a system’s intelligence or capabilities has been shown to impact user interaction negatively (Alan et al., 2016; Kizilcec, 2016). This may lead to over-reliance on a system (Lee and See, 2004), less vigilance towards system failures (Yang and Newman, 2013) and unrealistic expectations (Yang and Newman, 2013). Explanations for better system understanding have been investigated in the context of information retrieval (Koenemann and Belkin, 1996), recommender systems (Herlocker et al., 2000; Cramer et al., 2008; Kulesza et al., 2012) and context-aware systems (Lim et al., 2009; Dey and Newberger, 2009).

However, currently, the research streams on Explainable, Accountable and Intelligible Systems of the AI/ML and HCI community are relatively isolated (Abdul et al., 2018). Researchers also seem to ignore the large body of work in social sciences, which provides valuable insights into explanations (Miller, 2019). We seek to contribute to bridging the gap between the involved disciplines by evaluating the state of the art explanation techniques with highly complex models.

2.3. XAI User Studies

Several users studies have been conducted around explainable machine learning. For example, Poursabzi-Sangdeh et al. (Poursabzi-Sangdeh et al., 2018) conducted experiments with 1250 lay-users. They found that participants performed better at estimating the outcome of their model if there were fewer input features (2 vs. 8) and feature weights were revealed (transparent condition). However, such results cannot be generalised because predicting the outcome of a linear model is a matter of performing a simple multiplication, which does not reflect the complexity of current machine learning models. Narayanan et al. (Narayanan et al., 2018) studied if a specific presentation affects the amount of time required for the user to perform a task. Bussone et al. (Bussone et al., 2015) raised the question of when explanations could be considered harmful. In a study that targeted primary care physicians to diagnose and treat balance disorders, the system showed two versions to the users: A comprehensive version, which provides an explanation that shows inputs associated with the diagnosis and a second version, which shows fewer details. Their findings indicate that users who received a rich explanation from the system developed an over-reliance bias, which lead them to accept results from the system despite knowing the possibility of error. Yin et al. (Yin et al., 2019) conducted a study examining how lay-users understand the performance metrics of ML models. Their work investigates various aspects of users’ understanding and trust of the model performance on a hold-out set and how that maps to the post-deployment performance.

2.4. Evaluations of Saliency Map for text based classifiers

Several studies demonstrated the benefits of techniques that explains the importance of individual words for text-based classifiers. In a study by Kulesza et al.

(Kulesza et al., 2009)

, participants improved the performance of a Naive Bayes e-mail classifier. In the study, textual explanations and bar charts conveyed the importance of individual words. In some later work

(Kulesza et al., 2015) the authors introduced their principles for explanatory debugging. They implemented them in a new version of the e-mail classifier. In addition to bar charts, the importance of individual words was now visualised in two other ways: A word cloud and highlighted words in the e-mail. Riberio et al. (Ribeiro et al., 2016) conducted two experiments to evaluate an algorithm to generate saliency maps. In the first experiment, given two classifiers, participants choose the one they believed would generalize better. The first classifier provided a better test accuracy but would not generalize well. The authors concluded that explanations are useful in determining which classifier to trust regardless of the hold-out test accuracy. In the second experiment, participants were asked to improve the accuracy of the classifier by removing features that do not seem to generalize. After multiple rounds, participants were able to enhance the post-deployment accuracy. However, both studies lacked statistical significant tests and did not have a baseline condition (i.e., No-explanation condition). Consequently, it remains unknown whether participants would achieve similar performance with no explanation.

Lai and Tan (Lai and Tan, 2019) demonstrated a trade-off between performance and human agency by exposing participants to varying levels of machine assistance (of an SVM) while they were identifying deceptive reviews. They found that explanations without the suggestion of a label slightly improved human performance. Much higher gains were achieved by showing the predicted labels. Explicitly suggesting strong machine accuracy further improved performance. They found that the highlighted words increased the trust of humans in machine predictions, even when they were randomly chosen. Feng and Boyd-Graber (Feng and Boyd-Graber, 2019) studied AI-supported question-answering in a trivia game with three types of explanations. They conducted their study with experts and novices, showing that they trust and use explanations differently. One of their used explanation techniques highlighted matched words in the question and proposed answer. They found that this helped participants to decide faster on whether to trust the prediction. Springer and Whittaker (Springer and Whittaker, 2019)

conducted two user studies. They used a system that predicted the emotional valence of participant’s written experiences. Their explanation technique also used the highlighting of words. Its perceived performance was initially higher and degraded after users interacted with the system. Explanations were distracting and caused users to realise the system operated differently than they had initially anticipated. Most notably, users were disillusioned that the system did note take overall writing context into account. Instead, predictions were based on simple but accurate lexical weightings. The explanation techniques in the before-mentioned studies highlight words to raises the users’ awareness that they were important. In that sense, they are comparable to saliency maps. At the same time, the methods used for determining those features are considerably simpler than saliency-based approaches. While such methods make controlling factors in a user study easier, they all utilise machine learning models of lower complexity which is a significant limitation. It raises the question of whether findings obtained in these studies will also apply to more complex systems as those used for computer vision such as CNNs.

2.5. User Studies evaluating Saliency Maps for image classification

Cai et al. (Cai et al., 2019), evaluated two kinds of example-based explanations for a sketch-recognition algorithm: normative explanations and comparative explanations. Normative explanations led to a better understanding of the system and increased the perceived capability of the system. Comparative explanations did not always improve perceptions, possibly because they exposed limitations. While highly relevant for this work, this study did not evaluate saliency maps. Riberio et al. (Ribeiro et al., 2016) also evaluated their algorithm for a simple image classifier. The authors intentionally trained a biased binary classifier that distinguished between wolves and huskies. Images of wolves had snow in the background, whereas images of huskies did not. The classifier was therefore biased towards snow. In the within-subject study, participants were first shown ten incorrect predictions and asked whether or not they would trust the model. Secondly, explanations for the same predictions were shown. The explanations decreased participants trust in the model as intended. This study used a very simple scenario where a simple binary classier had an obvious bias. Again it is unclear whether results would apply to more complex scenarios like multi-class classification with a CNN.

To date, CNNs are becoming the default approach for many computer vision problems (Pouyanfar et al., 2018). While numerous post-hoc explanations for CNNs exist, they are rarely evaluated with users. To the best of our knowledge, the use of saliency maps has not been evaluated with CNNs or models of comparable complexity.

3. Method

We designed a between-group online study to evaluate whether saliency maps can help users understanding of a highly complex CNN used for multi-label image classification. In the multi-label image classification problem, an image can contain multiple objects. For example, the assignment of the labels “horse, train” is considered correct if both, a horse and a train are visible in the image. We choose this problem because in this context, saliency maps have the potential to highlight specific parts of the image that correspond to one label, as well as parts that correspond to alternative labels.

The study included two independent variables that varied between groups, with a full factorial design. Both were related to the amount of information shown to participants: presence of saliency maps and presence of classification scores.

A screenshot of the experimental setup is shown in Figure 1. In the following sections, we lay out a more elaborate description of the study. At this point, it is essential to point out that we needed to strike a balance between the number of participants, the duration of the study and the variation of experimental factors.

3.1. Materials

3.1.1. Dataset, CNN Model Architecture and Training

Various public datasets, algorithms and configuration options exist for the multi-class image classification problem. We used the PASCAL Visual Object Classes dataset (19714 images), because of its popularity, and its limited number of classes (20).

Additionally, we used the Keras library for Python, starting from an existing Keras model trained on the ImageNet dataset

(Deng et al., 2009), utilizing the VGG16 architecture (Simonyan and Zisserman, 2014)111https://keras.io/applications/#vgg16. We then fine-tuned the model on the train-val part of the PASCAL VOC 2012 dataset (Everingham et al., 2012), achieving an Average Precision (AP) score of 0.91 on the training-set and 0.73 on a the validation-set. On a hold-out test-set (the PASCAL VOC 2007 test data (Everingham et al., 2007)), the AP was 0.74. We did not train the model to reach state of the art performance. This was an intentional design choice to understand how explanation techniques could facilitate user understanding about the strengths and limitations of the model.

3.1.2. Saliency Maps and Scores Generation

A variety of algorithms have been proposed for generating saliency maps. In our pilot studies, we investigated two popular implementations: LIME (Ribeiro et al., 2016) and LRP (Bach et al., 2015). With LRP, saliency maps are not restricted to super-pixel patches but highlight contours of objects, which was preferred by most of our pilot study participants. For this reason and to simplify our setting, we chose to focus on the LRP algorithm only. Concretly, we used the - propagation rule (Bach et al., 2015) with and .

Figure 2 shows a true positive (TP) example, where the model correctly predicts a train. The saliency map suggests that the red part of the image containing the rail supports the classification of this image as a train. Figure 3 shows a false positive (FP) example where the system falsely predicts a train. The red part of the image contains what looks like a rail. They support the classification of this image as a train. The blue parts are against this classification.

Figure 2. Example of a saliency map explanation of a True Positive (TP) image for the label “train”. It highlights the contours of the lines below the train. A possible interpretation is that the CNN has learned to recognise trains when rails are present.
Figure 3. Example of a saliency map explanation of a False Positive (FP) image for the label “train”. A possible interpretation is that edges in the lower part appeared similar to rails, which could explain this error.

Since an image in the PASCAL VOC dataset can contain multiple objects, for each object class, the CNN computes a classification score between 0 and 1. Hence, a threshold needs to be defined so that the score can be translated into an outcome: detected when the score is above the threshold, or missed otherwise. We calculated threshold values for each class (e.g. horse, cat) because the CNN performs differently across classes. In particular, we obtained each threshold by maximising the F1-score for the class on the training dataset. In Figure 1, the small vertical red lines represent these selected thresholds.

3.1.3. Presentation

The interface of the study (Figure 1) was implemented as a Web application, using HTML5 and Python with the Django framework. We served the application from a standard Web server. The view-port of the participant browser window needed to be at least a 1000px wide and 600px high during the study.

3.2. Tasks

The user’s ability to predict the outcome of a ML classifier has been proposed as a measure to assess how transparent or explainable a system is (Lipton, 2018). It has also been utilised in other studies (Muramatsu and Pratt, 2001). Thus, we gave our participants the task to predict the classification outcome of the CNN described above for a fixed set of 14 task images from the hold-out test set. More specifically, for each task image, we asked them to list 2-3 features they believe the system is sensitive to and 2-3 features the system ignores. We then asked participants to predict whether the system will recognise an object of interest (‘cat’ or ‘horse’) in the given task image. We also asked them to rate their confidence in their forecast on a 4-point forced Likert item. Figure 1 depicts the interface for one task image (with a reduced number of example images). Half of the participants started with images of horses, while the other half, began with images of cats.

To increase participants engagement in the study, in addition to an £8 payment for their time, participants received an additional performance-based bonus of £0.5 for each correct answer as an incentive.

Seven task images were concerned with the class “cat” and another seven with the class “horse”. For each task image, participants were shown 12 example images from the CNN training set to inform their judgement. All participants worked on the same task images and were shown the same example images.

3.2.1. Selection of Example Images

We selected the example images for every task image from the PASCAL training set, based on their cosine distance from the task image in the embeddings space generated from the penultimate layer of the network. The assumption was that user understanding might benefit from looking at visually similar images. Showing the outcome of the classifier (i.e. TP, FN and FP) for the examples has been found to be important for the utility of explanation techniques (Lai and Tan, 2019). For this reason, we sampled examples of different outcomes for each task image:

  • 6 examples of True Positives (TP), where a label had been correctly assigned;

  • 3 examples of False Negatives (FN), where the CNN had failed to assign the label;

  • 3 examples of False Positives (FP), where the CNN had incorrectly assigned the label.

We also based our decision, regarding the number of shown examples, on experience from pilot studies. We had noticed that if we presented too many examples, participants were likely to only look at a random subset of them. At the same time, if the number was too low, there was a risk that not enough information was made available to participants. For this study, we selected 12 as a compromise. We also noticed that the saliency maps of TP examples are more informative than FN and FP. Thus we decided to show more TP than FN or FP examples.

3.2.2. Selection of Task Images

We intended our study to be no longer than 40 minutes to avoid fatigue effects. This design choice limited the possible number of task images. Consequently, we had to choose between sampling from a variety of classes or sampling from a subset of classes. In our pilot studies, participants found predicting model behaviour very confusing when the class in question was continually switching. Furthermore, the more classes they had to reason about the more challenging the tasks became, because they were not able to “learn” much about the model’s behaviour regarding a specific class. We also wanted to capture a variety of cases where the model had given correct as well as incorrect output. For these reasons, we decided to limit our experiment to two classes but included three TP, two FN and two FP for each class.

We drew task images randomly from the hold-out test dataset, with the constraint of having a mid-range classification score. In our pilot studies we had found that images with a low classification score (close to the threshold) were almost unpredictable for participants, while images with a high score were easily predictable. Consequently, we chose to sample from the middle, as we expect to see the most performance variation this way.

3.3. Conditions

The study included the following two independent variables:

3.3.1. Presence of Saliency Maps

This factor had two levels: shown or omitted. When shown, the saliency map for the relevant class was displayed next to each example image. It is important to note that saliency maps were not shown for the task image but only for the examples.

3.3.2. Presence of Classification Scores

This factor also had two levels: shown or omitted. When shown, a bar chart of the top 10 classification scores was displayed next to each example image. Classification scores produced by the CNN are the default sources of explanatory information on the instance level. Hence, we aimed to investigate whether visualising this additional numerical information would outperform, compliment or interact with the presence of saliency maps.

The two independent variables were combined in a full factorial design, resulting in the following four conditions:

  • Saliency maps not shown and scores not shown (Baseline)

  • Saliency maps not shown and scores shown

  • Saliency maps shown and scores not shown

  • Saliency maps shown and scores shown.

Figure 1 illustrates the saliency maps shown and scores shown condition. In other conditions, the interface looked the same, except not showing the saliency maps or scores.

4. Results

4.1. Outcome prediction accuracy

We were interested in investigating the effect that the presence of saliency maps and scores has on the ability of participants to forecast the CNN classification outcomes of images. We based our performance assessment on the percentage of correct forecasts per participant. We summarized the data in Figure 4

. A Shapiro-Wilk test revealed that the percentage of correct forecasts within groups were approximately normally distributed (

). A Levene’s Test showed performance variances between groups were similar (


Figure 4. Left: When saliency maps were shown, participants were significantly more accurate in predicting the outcome of the classifier . Right: Scores did not significantly influence the participant’s prediction performance. Success rates were relatively low across conditions, showing that tasks were very challenging.
Figure 5. Frequencies of individual features mentioned by participants for images of cats (top) and horses (bottom). Left: Features belonging to the Saliency-Features. Right: Features belonging to the General-Attributes (frequencies were normalised for each participant).

A two-way independent ANOVA revealed a statistically significant main effect of the presence of saliency maps on the performance (). In the presence of saliency maps participants were more accurate in predicting the outcome of the classifier ( vs. ). There was no significant main effect of the presences of scores on performance (). Furthermore, there was no interaction effect ().

Figure 6. The ratio of mentioned Saliency-Features. It summaries the share of saliency-features participants mentioned per task. They mentioned significantly more such features when saliency maps were present (Left). Scores did not have an influence (Right).

4.2. Confidence

We also asked participants to rate their confidence in their forecast on a 4-point forced Likert item. Answers were coded by numbers 1-4 and summed up per participant. A one-way independent Kruskal-Wallis test showed that confidence was similar across conditions (H(3) = 1.130, p = 0.770). On average participants tended to be

“slightly confident” in their answers (Median = 3.000). We also consider participants’ accuracy on the subsets of images corresponding to different outcomes (i.e. TP, FP, FN). Overall the accuracy was higher for TP images, on average 79.4%, it was lower for FP, on average 46.9%, and even lower for FN, on average 36.7%.

4.3. Mentioned Saliency Maps Features

Besides making a prediction, we asked participants what features they think the classifier is sensitive to and what features it ignored.

4.3.1. Excluded data

An analysis of the qualitative data revealed that two participants misunderstood these tasks. Consequently, they were excluded from this analysis. It also became apparent that many of the remaining participants misinterpreted the question about the features the system ignored. Therefore, we focused only on replies participants gave regarding the sensitivity of the classifier to features.

4.3.2. Mixed-Method Analysis of Answers

We carried carried out a qualitative content analysis (Mayring, 2014) on the free text replies. In the first pass, two of the authors coded the answers inductively. Each response could be assigned several open codes based on the features or concepts it addressed. Subsequently, coders discussed their individually established codes and agreed on a shared and simplified codebook. We decided to assign each code to one of two mayor code groups: Saliency-Features and General-Attributes.

The Saliency-Features group included codes referring to features, which could be localized to pixels in the proximity of the object of interest and that saliency maps could highlight. The rationale for this was that we aimed to compare how frequently participants mentioned concepts related features that saliency maps could potentially highlight. Besides the somewhat obvious feature codes such as Ears and Legs, this group also included: Equipment - which applied to all objects associated with domestication such as “leash”or “saddle”, Outline which applied to answers referring to the “shape” or “contour” of the object of interest and “Fur” which was used for utterances referring explicitly to the “fur”, “skin” or texture pattern on the animal.

The General-Attributes group included codes that refer to utterances of generic properties of the image. An example is the code Background - which applied to answers referring generically to “surroundings” or “context” but also objects in the background such as “trees”. Another example is Image Quality which was used for replies addressing issues of “contrast”, “blur” , “lighting condition” or “occlusion”. The code Texture was assigned when answers referred to images “texture” generically (i.e. “Fur patterns” are considered as a Saliency-Features).

For the quantitative analysis, we counted the number of Saliency-Features codes and General-Attributes codes. We noticed that some participants wrote a lot in the qualitative response and therefore mentioned a lot of features, while others did not. To prevent this from skewing the results, we calculated a ratio. We obtained the

Saliency-Features ratio for each participant by dividing the number of Saliency-Features codes by the total number of Saliency-Features and General-Attribute codes that we had assigned to their answers. Therefore a ratio of means that of the features that a participant mentioned were Saliency-Features. In the same fashion, we calculated ratios for all codes. The top of Figure 5 shows the ratios for the answers participants gave for images of cats, while the bottom of Figure 5 shows them for images of horses.

The Saliency-Features ratio was subjected to a statistical analysis. The data is summarized in Figure 6. A Shapiro-Wilk test revealed that the rate of Saliency-Features within groups were approximately normally distributed (). A Levene’s Test showed that the variances between groups were significantly different (

). To account for heteroscedasticity we ran a two-way independent measures ANOVA using white-corrected coefficient covariance matrix

(White, 1980). It revealed a statistically significant main effect of the presence of saliency maps on the rate of mentioned Saliency-Features (). Participants mentioned a larger share of Saliency-Features when saliency maps were present (M = vs. ). There was no significant main effect for the presences of scores () and no interaction effect ().

5. Discussion

Through a combination of quantitative and qualitative analysis, the results of our study highlight the potential to use saliency maps as an explanatory tool for non-expert AI users, as well as their limitations. In the following subsections, we reflect on the key issues and highlight implications for design and further research.

5.1. The utility of saliency maps exists, but it is limited

Our results show that when saliency maps were shown, participants predicted the outcome of the classifier significantly more accurately. Scores, instead, did not have a statistically significant effect. However, even with the presence of saliency maps, success rates were still relatively low (60.7%). Hence, the task of estimating the system’s predictions on a new image remained challenging. This is also reflected by our participant’s self-reported confidence in their answers, which was not affected by the presence of saliency maps or scores, and was on average still quite low. To explain this moderate outcome, we investigated participants’ performance in more detail on subsets of images corresponding to different outcomes. Participants across conditions seemed to be better in predicting the system’s outcome when it was correct (true positives: 79.4%). They were mainly struggling with the prediction of errors, performing worse than chance (false postives: 46.9% and false negatives: 36.7%). An interpretation of this result is that participants are possibly inclined to over-estimate the performance of the systems on challenging cases. Such cases are represented by FP and FN images. In fact, in 67.3% of all cases, participants predicted that the system would be correct, whereas it was only correct in 42.9% of the cases. One of the envisioned applications of explanations is aiding users in building appropriate trust into a system (Dzindolet et al., 2003; Bussone et al., 2015). Unexpected and unpredictable failures of a system affect trust more negatively than those that can be understood and anticipated (Lee and See, 2004; Dzindolet et al., 2003). Therefore, it is important that users can understand when the system will fail. As detecting errors is a claimed utility of instance-level explanations (Ribeiro et al., 2016; Lapuschkin et al., 2019), we suggest that future work should evaluate this empirically in more detail. Our study design did not allow to draw conclusions in this regard because we did not fully counterbalance the order of tasks and True Negatives (TN) were not part of the task set.

5.1.1. Reasoning on Examples

In our study, we based the sampling strategy on the similarity distance between the task image and the training set. The rationale behind this was that people might learn more effectively from examples that are similar in appearance to the task image. It might help them to reflect upon the visually similar images that the system had successfully classified (i.e. TPs) and images the system had classified incorrectly (i.e FN, FP). We hypothesised that such contrasting reasoning can help users to understand the system’s causes of successes and failures. However, when considering the examples presented to participants, we noticed that the usefulness of FN saliency maps is negligible. They usually highlight very little evidence (see i.e. the FN example in Figure 1). For FN examples, the actual image and the other saliency maps (TP, FN) become the only source of information for understanding why an example has not been recognised by the system. This insight suggests that the utility of saliency maps varies according to the classification score. In other words, a saliency map may highlight what supports the prediction of some class, but it will fail to provide counter-factual evidence, namely, the absence of evidence.

We would like to emphasise that for a human, it is easy to spot and point to the absence of a feature concept, while it is not for a CNN. Humans can easily break down an image into meaningful regions (semantics) (Fei-Fei et al., 2007). In contrast, CNNs look for patterns in a sub-symbolic fashion that lead to an outcome (Bishop, 2006; Lipton, 2018). Because CNNs do not process data in a ‘semantic‘ fashion, other patterns in an image (which may not belong to the concept) can contribute towards a classification outcome in unexpected ways (Lapuschkin et al., 2019). An implication for the design is that we need to develop explanation algorithms that bridge the gap between humans and machines by leading the user to understand that the system is not basing its classification decision on higher-level ‘semantics’ of the image. Furthermore, we would like to emphasise that choosing representative examples with their corresponding saliency maps, which summarise the behaviour of the system well, is an under-explored topic. New approaches for generating saliency maps and for applying them to various machine learning problems are presented (see review (Adadi and Berrada, 2018)). However, very little work exists that investigates for which instances users should examine salience maps. Researchers have acknowledged that users can only inspect a limited number of saliency maps (Ribeiro et al., 2016), but to the best of our knowledge, only two works explore sampling strategies (Ribeiro et al., 2016; Lapuschkin et al., 2019) - none of which where applicable for this work. An important implication, then, is that further research needs to characterise the effect of different sampling strategies of saliency map examples on users interpretation of the system operation.

5.2. Saliency maps can help participants notice features

Our results clearly indicate that saliency maps influenced our participants to notice the highlighted saliency features and to suggest that such features are important for the classification outcome. The ratio of mentioned Saliency-Features (e.g. legs, outline) compared to General-Attributes (e.g. color, image quality) was significantly higher when saliency maps were present while scores had no influence (Figure 6).

This effect can be explored in more detail in Figure 5. It shows that saliency maps seem to lead people to pay attention to specific parts of the object of interest. For example, Figure 5 depicts the share of mentioned features for images of horses. It is evident that some features such as legs, outline, tail and belly were mentioned much more frequently by participants exposed to saliency maps, while general-attributes such as background and colour are mentioned more often when the saliency maps are not shown.

5.2.1. Facilitating global model understanding by explaining local features

It is worth emphasising that even when users notice features, this does not necessarily imply that they will perform better in predicting the outcome of the CNN or reach a global understanding of the model. Saliency maps provide only a visualisation of the importance of pixels in a single image. Transferring knowledge about potential features to new images, where they are presented in different orientations, scales, forms and perspectives, is very challenging. Furthermore, it is hard to get a quantifiable measure of the importance of individual features in an image. Again complexity increases if one attempts to quantify the importance of a feature on new images. In other words, it is difficult to estimate how the classification score would change if a feature would be absent. Would the score go down by a factor of 0.1, 0.2 or 0.6? Moreover, does the presence of different features cause an interaction effect? It is challenging for users to reason about this, especially when considering that CNNs process the input data in a non-linear fashion (Bishop, 2006).

An implication for the design of explanation systems, then, is that saliency maps should be complemented by a global measure that explains how sensitive the presence of a feature is to the prediction of some class. For example, how sensitive the presence of nose is to the prediction of cat? In that regard, complementing saliency maps with this additional information could be valuable for users to build quantifiable measures of saliency maps, and perhaps avoid biases that might arise from exploring an unrepresentative subset of the dataset. Kim et al. (Kim et al., 2017) proposed an algorithm in that direction, where a user can test how sensitive the model’s predictions are to a global concept defined by the user. For example, how important the strips concept is to the ”zebra” class.

5.2.2. The importance of general attributes

Another reason why noticing Saliency-features does not necessarily facilitate a better understanding of a model is that general-attributes (e.g. colour, contrast) might influence the classification outcome. However, these general-attributes are usually not directly highlighted by saliency maps, because as a more general image property, they can not be localised to individual pixels. This points to the previously stated limitation of the expressive capabilities of saliency maps (Schuessler and Weiß, 2019). In fact, saliency maps might even prime participants to primarily consider only highlighted features, and give less weight to other attributes that are not highlighted but important. In contrast, users preconceptions may cause them to focus on attributes such as the brightness of the image, even if it is not a major cause of failure. An implication for design is to develop explanations that convey the right expectation to users. We suggest that saliency maps should be complemented by more global representations of the image features. For example, saliency information could be related to global descriptors of the images, such as overall contrast or brightness measures.

6. Limitations

The design space for the study we presented was vast. Our design choices outlined in Section 3 introduced some limitations, which we make explicit in this section.

The first limitation is the small number of image classes we considered. We decided for this compromise considering the limited time for each session, and the limited knowledge participant would have been able to obtain about class-specific behaviour. Future work should run a long-term evaluation (i.e. lasting several days or weeks) to allow participants to explore a large dataset with multiple classes in more depth.

Another limitation of our design is that we used one specific network architecture (VGG16 (Simonyan and Zisserman, 2014)) and one specific technique to generate saliency maps (LRP (Bach et al., 2015)). With a series of pilot studies, we have tried to identify a combination of both techniques which provided saliency maps that participants found to be informative. However, this also means that results might be diffrent with a different combination of techniques.

A limitation of our analysis is that the study design did not allow us to draw conclusions about users performance for different outcomes types (e.g. TP, FN, FP). The reason for this was that we did fully counterbalanced tasks, and True Negatives (TN) were not part of the task set. Future studies should address this limitation and study this aspect in more detail.

Finally, our participants were required to have a technical background, but we did not control for ML expertise. We see potential to repeat our study with different participant populations, such as ML-experts, or lay users.

7. Conclusion and Future Work

This paper reported on a between-group user study designed to evaluate the utility of “saliency maps” - a popular explanation algorithm for image classification applications of CNNs. We focused on saliency maps generated by the LRP algorithm, for one specific architecture and dataset. Our results indicate that saliency maps can help users to learn about some specific image features the system is sensitive to, and enhance their ability to predict the outcome of the network for new images. However, even with saliency maps present, the CNN model remained largely unpredictable for participants (60.7% prediction accuracy). For misclassified images, prediction accuracy remained well below chance level (43.8% for False Negatives and 49.2% for False Positives ). We argue that reaching a solid understanding of how a CNN Model classifies images is not possible with the sole use of instance-level based explanations (of which saliency maps are an example). Even with very informative examples, saliency maps can only highlight the importance of features that are localisable to pixel-regions. For these highlighted features, they do not convey a quantifiable measure of their importance for future classifications. At the same time, this focus on regions may divert users attention from other important image properties (such as contrast or lighting conditions). We suggest using saliency maps in conjunction with other more global explanation methods. Furthermore, we view saliency maps sampling strategies as a promising direction for future research.

Overall, these findings serve as a reminder that making AI explainable is still very much an open technical challenge, and as AI models become increasingly complex, further studies are necessary to address this topic. We argue that the HCI community is well placed to contribute to solving this challenge, and we hope that the work presented in this paper can serve as a practical example in terms of study design, and stimulate further HCI interest in this area and collaborations with the AI community.

8. Acknowledgements

This work was supported by the Engineering and Physical Sciences Research Council A-IoT (EP/N014243/1) project, the German Federal Ministry of Education and Research (BMBF) - NR 16DII113, and by the Center of Excellence in Telecommunication Applications at King Abdulaziz City for Science and Technology, National Science, Technology and Innovation plan (NSTIP). The study was approved by the Ethics Committees of UCLIC. We want to thank members of the Weizenbaum Institue who helped shape this work: In particular, Fenne große Deters for her valuable advice on the experimental design. Hannes-Vincent Krause helped with questions regarding the quantitative analysis. Milagros Miceli and Tianling Yang helped to analyse the example images qualitatively (Results were inconclusive and not reported). Esra Eres provided manipulated images during the exploratory phase of this work. Furthermore, we would like to thank all the participants of the pilots as well as the online study. We are also grateful to the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. Data URI: https://doi.org/10.5522/04/11638275.v2.


  • (1)
  • Abdul et al. (2018) Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli. 2018. Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18). ACM, Article 582, 18 pages. https://doi.org/10.1145/3173574.3174156
  • Adadi and Berrada (2018) A. Adadi and M. Berrada. 2018.

    Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI).

    IEEE Access 6 (2018), 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
  • Alan et al. (2016) Alper T. Alan, Enrico Costanza, Sarvapali D. Ramchurn, Joel Fischer, Tom Rodden, and Nicholas R. Jennings. 2016. Tariff Agent: Interacting with a Future Smart Energy System at Home. ACM Trans. Comput.-Hum. Interact. 23, 4 (Aug. 2016), 25:1–25:28. https://doi.org/10.1145/2943770
  • Ardila et al. (2019) Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich, and Shravya Shetty. 2019.

    End-to-End Lung Cancer Screening with Three-Dimensional Deep Learning on Low-Dose Chest Computed Tomography.

    Nature Medicine 25, 6 (June 2019), 954. https://doi.org/10.1038/s41591-019-0447-x
  • Bach et al. (2015) Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLOS ONE 10, 7 (July 2015), e0130140. https://doi.org/10.1371/journal.pone.0130140
  • Bayman and Mayer (1984) Piraye Bayman and Richard E. Mayer. 1984. Instructional Manipulation of Users’ Mental Models for Electronic Calculators. International Journal of Man-Machine Studies 20, 2 (1984), 189–199. https://doi.org/10.1016/S0020-7373(84)80017-6
  • Bishop (2006) Christopher M Bishop. 2006. Pattern recognition and machine learning. Springer, New York, NY.
  • Boulay et al. (1999) Benedict Du Boulay, Tim O’shea, and John Monk. 1999. The Black Box inside the Glass Box: Presenting Computing Concepts to Novices. International Journal of Human-Computer Studies 51, 2 (1999), 265–277. https://doi.org/10.1006/ijhc.1981.0309
  • Bussone et al. (2015) A. Bussone, S. Stumpf, and D. O’Sullivan. 2015. The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems. In 2015 International Conference on Healthcare Informatics. 160–169. https://doi.org/10.1109/ICHI.2015.26
  • Bylinskii et al. (2019) Z. Bylinskii, T. Judd, A. Oliva, A. Torralba, and F. Durand. 2019.

    What Do Different Evaluation Metrics Tell Us About Saliency Models?

    IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 3 (March 2019), 740–757. https://doi.org/10.1109/TPAMI.2018.2815601
  • Cai et al. (2019) Carrie J. Cai, Jonas Jongejan, and Jess Holbrook. 2019. The Effects of Example-Based Explanations in a Machine Learning Interface. In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI ’19). ACM, New York, NY, USA, 258–262. https://doi.org/10.1145/3301275.3302289
  • Campolo et al. (2018) Alex Campolo, Madelyn Sanfilippo, Meredith Whittaker, and Kate Crawford. 2018. AI Now 2017 Report. Microsoft Research (Feb. 2018).
  • Cramer et al. (2008) Henriette Cramer, Vanessa Evers, Satyan Ramlal, Maarten Someren, Lloyd Rutledge, Natalia Stash, Lora Aroyo, and Bob Wielinga. 2008. The Effects of Transparency on Trust in and Acceptance of a Content-Based Art Recommender. User Modeling and User-Adapted Interaction 18, 5 (Nov. 2008), 455–496. https://doi.org/10.1007/s11257-008-9051-3
  • Deng et al. (2009) Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Miami, FL, 248–255. https://doi.org/10.1109/CVPR.2009.5206848
  • Dey and Newberger (2009) Anind K. Dey and Alan Newberger. 2009. Support for Context-Aware Intelligibility and Control. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’09). ACM, New York, NY, USA, 859–868. https://doi.org/10.1145/1518701.1518832
  • Dix (1992) Alan Dix. 1992. Human Issues in the Use of Pattern Recognition Techniques. Ellis Horwood, USA, 429–451.
  • Doshi-Velez and Kim (2017) Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. (2017). arXiv:1702.08608
  • Dzindolet et al. (2003) Mary T. Dzindolet, Scott A. Peterson, Regina A. Pomranky, Linda G. Pierce, and Hall P. Beck. 2003. The Role of Trust in Automation Reliance. Int. J. Hum.-Comput. Stud. 58, 6 (June 2003), 697–718. https://doi.org/10.1016/S1071-5819(03)00038-7
  • Everingham et al. (2007) M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2007. The PASCAL Visual Object Classes Challenge 2007 (VOC2007). (2007). http://host.robots.ox.ac.uk/pascal/VOC/voc2007/
  • Everingham et al. (2012) M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2012. The PASCAL Visual Object Classes Challenge 2012 (VOC2012). (2012). http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
  • Fei-Fei et al. (2007) Li Fei-Fei, Asha Iyer, Christof Koch, and Pietro Perona. 2007. What do we perceive in a glance of a real-world scene? Journal of Vision 7, 1 (Jan. 2007), 10–10. https://doi.org/10.1167/7.1.10
  • Feng and Boyd-Graber (2019) Shi Feng and Jordan Boyd-Graber. 2019. What Can AI Do for Me?: Evaluating Machine Learning Interpretations in Cooperative Play. In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI ’19). ACM, New York, NY, USA, 229–239. https://doi.org/10.1145/3301275.3302265
  • Guidotti et al. (2018) Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A Survey of Methods for Explaining Black Box Models. ACM Comput. Surv. 51, 5 (Aug. 2018), 93:1–93:42. https://doi.org/10.1145/3236009
  • Hendricks et al. (2016) Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell. 2016. Generating Visual Explanations. In Computer Vision – ECCV 2016 (Lecture Notes in Computer Science). Springer, Cham, 3–19. https://doi.org/10.1007/978-3-319-46493-0_1
  • Herlocker et al. (2000) Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. 2000. Explaining Collaborative Filtering Recommendations. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW ’00). ACM, New York, NY, USA, 241–250. https://doi.org/10.1145/358916.358995
  • Kieras and Bovair (1984) David E. Kieras and Susan Bovair. 1984. The Role of a Mental Model in Learning to Operate a Device. Cognitive Science 8, 3 (1984), 255–273. https://doi.org/10.1207/s15516709cog0803_3
  • Kim et al. (2014) Been Kim, Cynthia Rudin, and Julie A Shah. 2014. The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. In Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 1952–1960. https://arxiv.org/pdf/1503.01161.pdf
  • Kim et al. (2017) Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2017. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). (2017). arXiv:1711.11279
  • Kizilcec (2016) René F. Kizilcec. 2016. How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16). ACM, New York, NY, USA, 2390–2395. https://doi.org/10.1145/2858036.2858402
  • Kleinberg et al. (2018) Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. Human Decisions and Machine Predictions. The Quarterly Journal of Economics 133, 1 (Feb. 2018), 237–293. https://doi.org/10.1093/qje/qjx032
  • Koenemann and Belkin (1996) Jürgen Koenemann and Nicholas J. Belkin. 1996. A Case for Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’96). ACM, New York, NY, USA, 205–212. https://doi.org/10.1145/238386.238487
  • Kulesza et al. (2015) Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of Explanatory Debugging to Personalize Interactive Machine Learning. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI ’15). ACM, New York, NY, USA, 126–137. https://doi.org/10.1145/2678025.2701399
  • Kulesza et al. (2012) Todd Kulesza, Simone Stumpf, Margaret Burnett, and Irwin Kwan. 2012. Tell Me More?: The Effects of Mental Model Soundness on Personalizing an Intelligent Agent. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’12). ACM, New York, NY, USA, 1–10. https://doi.org/10.1145/2207676.2207678
  • Kulesza et al. (2009) Todd Kulesza, Weng-Keen Wong, Simone Stumpf, Stephen Perona, Rachel White, Margaret M. Burnett, Ian Oberst, and Andrew J. Ko. 2009. Fixing the Program My Computer Learned: Barriers for End Users, Challenges for the Machine. In Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI ’09). ACM, New York, NY, USA, 187–196. https://doi.org/10.1145/1502650.1502678
  • Lai and Tan (2019) Vivian Lai and Chenhao Tan. 2019. On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19). ACM, New York, NY, USA, 29–38. https://doi.org/10.1145/3287560.3287590
  • Lapuschkin et al. (2019) Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2019. Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. Nature Communications 10, 1 (March 2019), 1096. https://doi.org/10.1038/s41467-019-08987-4
  • Lee and See (2004) John D. Lee and Katrina A. See. 2004. Trust in Automation: Designing for Appropriate Reliance. Human Factors 46, 1 (March 2004), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
  • Lim et al. (2009) Brian Y. Lim, Anind K. Dey, and Daniel Avrahami. 2009. Why and Why Not Explanations Improve the Intelligibility of Context-Aware Intelligent Systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’09). ACM, New York, NY, USA, 2119–2128. https://doi.org/10.1145/1518701.1519023
  • Lipton (2018) Zachary C. Lipton. 2018. The Mythos of Model Interpretability. Commun. ACM 61, 10 (Sept. 2018), 36–43. https://doi.org/10.1145/3233231
  • Mayring (2014) Philipp Mayring. 2014. Qualitative content analysis: theoretical foundation, basic procedures and software solution. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-395173
  • Miller (2019) Tim Miller. 2019. Explanation in Artificial Intelligence: Insights from the Social Sciences. Artificial Intelligence 267 (2019), 1–38. https://doi.org/10.1016/j.artint.2018.07.007
  • Mohler et al. (2015) G. O. Mohler, M. B. Short, Sean Malinowski, Mark Johnson, G. E. Tita, Andrea L. Bertozzi, and P. J. Brantingham. 2015. Randomized Controlled Field Trials of Predictive Policing. J. Amer. Statist. Assoc. 110, 512 (Oct. 2015), 1399–1411. https://doi.org/10.1080/01621459.2015.1077710
  • Moray (1999) Neville Moray. 1999. Mental Models in Theory and Practice. Attention and performance XVII: Cognitive regulation of performance: Interaction of theory and application (1999), 223–258.
  • Muramatsu and Pratt (2001) Jack Muramatsu and Wanda Pratt. 2001. Transparent Queries: Investigation Users’ Mental Models of Search Engines. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 217–224. https://doi.org/10.1145/383952.383991
  • Narayanan et al. (2018) Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2018. How Do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. (Feb. 2018). arXiv:1802.00682
  • Nguyen et al. (2016) Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. 2016. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc., 3387–3395. https://export.arxiv.org/pdf/1605.09304
  • Norman (2014) Donald Norman. 2014. On the Relationship between Conceptual and Mental Models. In Gentner et al (e.d) Mental Models. Psychology Press.
  • Poursabzi-Sangdeh et al. (2018) Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018. Manipulating and Measuring Model Interpretability. arXiv:1802.07810
  • Pouyanfar et al. (2018) Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2018. A Survey on Deep Learning: Algorithms, Techniques, and Applications. ACM Comput. Surv. 51, 5 (Sept. 2018), 92:1–92:36. https://doi.org/10.1145/3234150
  • Ribeiro et al. (2016) Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. ”Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16). ACM, New York, NY, USA, 1135–1144. https://doi.org/10.1145/2939672.2939778
  • Schuessler and Weiß (2019) Martin Schuessler and Philipp Weiß. 2019. Minimalistic Explanations: Capturing the Essence of Decisions. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA ’19). ACM, New York, NY, USA, Article LBW2810, 6 pages. https://doi.org/10.1145/3290607.3312823
  • Shneiderman (2016) Ben Shneiderman. 2016. Opinion: The Dangers of Faulty, Biased, or Malicious Algorithms Requires Independent Oversight. Proceedings of the National Academy of Sciences 113, 48 (Nov. 2016), 13538–13540. https://doi.org/10.1073/pnas.1618211113
  • Simonyan et al. (2013) Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv:1312.6034
  • Simonyan and Zisserman (2014) Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556
  • Springer and Whittaker (2019) Aaron Springer and Steve Whittaker. 2019. Progressive Disclosure: Empirically Motivated Approaches to Designing Effective Transparency. In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI ’19). ACM, New York, NY, USA, 107–120. https://doi.org/10.1145/3301275.3302322
  • Stone et al. (2016) Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Kevin Leyton-Brown, David C. Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. 2016. ”Artificial Intelligence and Life in 2030.” One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel. https://ai100.stanford.edu/2016-report
  • The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (2017) The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. 2017. Ethically Aligned Design: A Vision for Prioritizing Human Well-Being with Autonomous and Intelligent Systems, Version 2. https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v2.pdf
  • Tullio et al. (2007) Joe Tullio, Anind K. Dey, Jason Chalecki, and James Fogarty. 2007. How It Works: A Field Study of Non-Technical Users Interacting with an Intelligent System. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’07). ACM, New York, NY, USA, 31–40. https://doi.org/10.1145/1240624.1240630
  • Volokitin et al. (2016) A. Volokitin, M. Gygli, and X. Boix. 2016. Predicting When Saliency Maps are Accurate and Eye Fixations Consistent. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 544–552. https://doi.org/10.1109/CVPR.2016.65
  • White (1980) Halbert White. 1980. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica 48, 4 (1980), 817–838. http://www.jstor.org/stable/1912934
  • Yang and Newman (2013) Rayoung Yang and Mark W. Newman. 2013. Learning from a Learning Thermostat: Lessons for Intelligent Systems for the Home. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 93–102. https://doi.org/10.1145/2493432.2493489
  • Yin et al. (2019) Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. 2019. Understanding the Effect of Accuracy on Trust in Machine Learning Models. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM, New York, NY, USA, 279:1–279:12. https://doi.org/10.1145/3290605.3300509