E XEMPLARY N ATURAL I MAGES E XPLAIN CNN A CTIVATIONS B ETTER THAN S TATE - OF - THE -A RT F EATURE V ISUALIZATION
A BSTRACT
Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs).
At the same time, there are concerns that these visualizations might not accurately represent CNNs’ inner workings.
Here, we measure how much extremely activating images help humans to predict CNN activations.
Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al.
(2017) with a simple baseline visualization, namely exemplary natural images that also strongly activate a speciﬁc feature map.
Given either synthetic or natural reference images, human participants choose which of two query images leads to strong positive activation.
The experiment is designed to maximize participants’ performance, and is the ﬁrst to probe intermediate instead of ﬁnal layer representations.
We ﬁnd that synthetic images indeed provide helpful information about feature map activations ( 82 ± 4% accuracy; chance would be 50% ).
However, natural images — originally intended to be a baseline — outperform these synthetic images by a wide margin ( 92 ± 2% ).
Additionally, participants are faster and more conﬁdent for natural images, whereas subjective impressions about the interpretability of the feature visualizations by Olah et al.
(2017) are mixed.
The higher informativeness of natural images holds across most layers, for both expert and lay participants as well as for hand- and randomly-picked feature visualizations.
Even if only a single reference image is given, synthetic images provide less information than natural images ( 65 ± 5% vs. 73 ± 4% ).
In summary, synthetic images from a popular feature visualization method are signiﬁcantly less informative for assessing
CNN activations than natural images.
We argue that visualization methods should improve over this simple baseline.
I NTRODUCTION
As Deep Learning methods are being deployed across society, academia and industry, the need to understand their decisions becomes ever more pressing.
Under certain conditions, a “right to explanation” is even required by law in the European Union (GDPR, 2016; Goodman & Flaxman, 2017).
Fortunately, the ﬁeld of interpretability or explainable artiﬁcial intelligence (XAI) is also growing: Not only are discussions on goals and deﬁnitions of interpretability advancing (DoshiVelez & Kim, 2017; Lipton, 2018;
Gilpin et al., 2018; Murdoch et al., 2019; Miller, 2019; Samek et al., 2020) but the number of explanation methods is rising, their maturity is evolving (Zeiler &
Fergus, 2014; Ribeiro et al., 2016; Selvaraju et al., 2017; Kim et al., 2018) and they are tested and or
M a x i m a ll y
A c t i v a t i n g ...
Synthetic
Natural ...
M i n i m a ll y
A c t i v a t i n g ...

Choose or
Queries ...which image is strongly activating?
A
Given these reference images...
B
Synthetic Natural Chance 0.
P r o p o r t i o n C o rr e c t
Synthetic images are helpful Natural even more or used in real-world scenarios like medicine (Cai et al., 2019; Kr¨oll et al., 2020) and meteorology (Ebert-Uphoff & Hilburn, 2020).
We here focus on the popular post-hoc explanation method (or interpretability method) of feature visualizations via activation maximization 1 .
First introduced by Erhan et al. and subsequently improved by many others (Mahendran & Vedaldi, 2015; Nguyen et al., 2015; Mordvintsev et al., 2015; Nguyen et al., 2016a; 2017), these synthetic, maximally activating images seek to visualize features that a speciﬁc network unit, feature map or a combination thereof is selective for.
However, feature visualizations are surrounded by a great controversy: How accurately do they represent a
CNN’s inner workings—or in short, how useful are they?
This is the guiding question of our study.
On the one hand, many researchers are convinced that feature visualizations are interpretable (Graetz, 2019) and that “features can be rigorously studied and understood”
(Olah et al., 2020b).
Also other applications from Computer Vision and Natural Language Processing support the view that features are meaningful (Mikolov et al., 2013;
Karpathy et al., 2015; Radford et al., 2017;
Zhou et al., 2014;
Bau et al., 2017; 2020) and might be formed in a hierarchical fashion (LeCun et al., 2015; G¨uc¸l¨u & van Gerven, 2015; Goodfellow et al., 2016).
Over the past few years, extensive investigations to better understand CNNs are based on feature visualizations (Olah et al., 2020b;a;
Cammarata et al., 2020; Cadena et al., 2018), and the technique is being combined with other explanation methods (Olah et al., 2018;
Carter et al., 2019; Addepalli et al., 2020; Hohman et al., 2019).
On the other hand, feature visualizations can be equal parts art and engineering as they are science: vanilla methods look noisy, thus human-deﬁned regularization mechanisms are introduced.
But do the resulting beautiful visualizations accurately show what a CNN is selective for?
How representative are the seemingly well-interpretable, “hand-picked” (Olah et al., 2017) synthetic images in publications for the entirety of all units in a network, a concern raised by e.g. Kriegeskorte (2015)?
What if the features that a CNN is truly sensitive to are imperceptible instead, as might be suggested by the existence of adversarial examples (Szegedy et al., 2013; Ilyas et al., 2019)?
Morcos et al.
(2018) even suggest that units of easily understandable features play a less important role in a network.
Another criticism of synthetic maximally activating images is that they only visualize extreme features, while potentially leaving other features undetected that only elicit e.g. 70% of the maximal activation.
Also, polysemantic units (Olah et al., 2020b), i.e. units that are highly activated by different semantic concepts, as well as the importance of combinations of units (Olah et al., 2017; 2018; Fong & Vedaldi, 2018) already hint at the complexity of how concepts are encoded in CNNs.
One way to advance this debate is to measure the utility of feature visualizations in terms of their helpfulness for humans .
In this study, we therefore design well-controlled psychophysical experiments that aim to quantify the informativeness of the popular visualization method by Olah et al.
Speciﬁcally, participants choose which of two natural images would elicit a higher activa- tion in a CNN given a set of reference images that visualize the network selectivities.
We use natural query images because real-world applications of XAI require understanding model decisions to natural inputs.
To the best of our knowledge, our study is the ﬁrst to probe how well humans can predict intermediate CNN activations.
Our data shows that:
• Synthetic images provide humans with helpful information about feature map activations.
• Exemplary natural images are even more helpful.
• The superiority of natural images mostly holds across the network and various conditions.
• Subjective impressions of the interpretability of the synthetic visualizations vary greatly between participants.
R ELATED W ORK
Signiﬁcant progress has been made in recent years towards understanding CNNs for image data.
Here, we mention a few selected methods as examples of the plethora of approaches for understanding CNN decision-making: Saliency maps show the importance of each pixel to the classiﬁcation decision (Springenberg et al., 2014; Bach et al., 2015; Smilkov et al., 2017; Zintgraf et al., 2017), concept activation vectors show a model’s sensitivity to human-deﬁned concepts (Kim et al., 2018), and other methods - amongst feature visualizations - focus on explaining individual units (Bau et al., 2020).
Some tools integrate interactive, software-like aspects (Hohman et al., 2019; Wang et al., 2020; Carter et al., 2019; Collaris & van Wijk, 2020; OpenAI, 2020), combine more than one explanation method (Shi et al., 2020; Addepalli et al., 2020) or make progress towards automated explanation methods (Lapuschkin et al., 2019; Ghorbani et al., 2019).
As overviews, we recommend Gilpin et al.
; Zhang & Zhu (2018); Montavon et al.
(2018) and Carvalho et al.
Despite their great insights, challenges for explanation methods remain.
Oftentimes, these techniques are criticized as being over-engineered; regarding feature visualizations, this concerns the loss function and techniques to make the synthetic images look interpretable (Nguyen et al., 2017).
Another critique is that interpretability research is not sufﬁciently tested against falsiﬁable hypotheses and rather relies too much on intuition (Leavitt & Morcos, 2020).
In order to further advance XAI, scientists advocate different directions.
Besides the focus on developing additional methods, some researchers (e.g. Olah et al. (2020b)) promote the “natural science” approach, i.e. studying a neural network extensively and making empirical claims until falsiﬁcation.
Yet another direction is to quantitatively evaluate explanation methods.
So far, only decision-level explanation methods have been studied in this regard.
Quantitative evaluations can either be realized with humans directly or with mathematically-grounded models as an approximation for human perception.
Many of the latter approaches show great insights (e.g. Hooker et al.
; Nguyen &
Mart´ınez (2020); Fel & Vigouroux (2020); Lin et al.
Tritscher et al.
Tjoa & Guan (2020)).
However, a recent study demonstrates that metrics of the explanation quality computed without human judgment are inconclusive and do not correspond to the human rankings (Biessmann & Reﬁano, 2019).
Additionally, Miller (2019) emphasizes that XAI should build on existing research in philosophy, cognitive science and social psychology.
The body of literature on human evaluations of explanation methods is growing: Various combinations of data types (tabular, text, static images), task set-ups and participant pools (experts vs. laypeople, on-site vs. crowd-sourcing) are being explored.
However, these studies all aim to investigate ﬁnal model decisions and do not probe intermediate activations like our experiments do.
For a detailed table of related studies, see Appendix Sec.
A.3.
A commonly employed task paradigm is the “forward simulation / prediction” task, ﬁrst introduced by Doshi-Velez & Kim (2017):
Participants guess the model’s computation based on an input and an explanation.
As there is no absolute metric for the goodness of explanation methods (yet), comparisons are always performed within studies, typically against baselines.
The same holds for additional data collected for conﬁdence or trust ratings.
According to the current literature, studies reporting positive effects of explanations (e.g. Kumarakulasinghe et al.
(2020)) slightly outweigh those reporting inconclusive (e.g. Alufaisan et al.
Chu et al.
(2020)) or even negative effects (e.g. Shen & Huang (2020)).
Minimally Activating Maximally Activating      more confident To our knowledge, no study has yet evaluated the popular explanation method of feature visualizations and how it improves human understanding of intermediate network activations.
study therefore closes an important gap: presenting data for forward prediction task of a CNN we of maximally activating et al
Furthermore our experiments are as they
M ETHODS
We perform two human psychophysical studies 2 with different foci (Experiment I ( N = 10 ) and
Experiment II ( N = 23 )).
In both studies, the task is to choose the one image out of two natural query images (two-alternative forced choice paradigm) that the participant considers to also elicit a strong activation given some reference images (see Fig. 2).
Apart from the image choice, we record the participant’s conﬁdence level and reaction time.
Speciﬁcally, responses are given by clicking on the conﬁdence levels belonging to either query image.
In order to gain insights into how intuitive participants ﬁnd feature visualizations, their subjective judgments are collected in a separate task and a dynamic conversation after the experiment (for details, see Appendix Sec.
A.1.
and Appendix Sec.
A.2.6).
All design choices are made with two main goals: (1) allowing participants to achieve the best performance possible to approximate an upper bound on the helpfulness of the explanation method, and (2) gaining a general impression of the helpfulness of the examined method.
As an example, we choose the natural query images from among those of lowest and highest activations ( → best possible performance) and test many different feature maps across the network ( → generality).
For more details on the human experiment besides the ones below, see Appendix Sec.
A.1.
In Experiment I, we focus on comparing the performance of synthetic images to two baseline conditions: natural reference images and no reference images.
In Experiment II, we compare lay vs. expert participants as well as different presentation schemes of reference images.
Expert participants qualify by being familiar or having practical experience with feature visualization techniques or at least CNNs.
Regarding presentation schemes, we vary whether only maximally or both maximally and minimally activating images are shown; as well as how many example images of each of these are presented ( 1 or 9 ).
Following the existing work on feature visualization (Olah et al., 2017; 2018; 2020b;a), we use an
Inception V1 network 3 (Szegedy et al., 2015) trained on ImageNet (Deng et al., 2009; Russakovsky
[Figure]
None Reference Images 0.
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C o rr e c t p = 0.
p = 0. p = [-0.
-]0.
[Figure]
Synthetic Natural
None Reference Images 0.
R e a c t i o n T i m e [ s e c ] p = 0.
p < 0.
p = 0.003 C B
Synthetic
P r o p o r t i o n o f C o rr e c t T r i a l s
Natural
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[Figure]
et al., 2015).
The synthetic images throughout this study are the optimization results of the feature visualization method by Olah et al. (2017) with the spatial average of a whole feature map (“channel objective”). The natural stimuli are selected from the validation set of the ImageNet ILSVRCdataset (Russakovsky et al., 2015) according to their activations for the feature map of interest. Speciﬁcally, the images of the most extreme activations are sampled, while ensuring that each lay or expert participant sees different query and reference images.
A more detailed description of the speciﬁc sampling process for natural stimuli and the generation process of synthetic stimuli is given in Sec.
A.1.2.
R ESULTS
In this section, all ﬁgures show data from Experiment I except for Fig.
5A+C, which show data from Experiment II.
All ﬁgures for Experiment II, which replicate the ﬁndings of Experiment I, as well as additional ﬁgures for Experiment
I (such as a by-feature-map analysis), can be found in the Appendix Sec.
A.2.
Note that (unless explicitly noted otherwise), error bars denote two standard errors of the mean of the participant average metric.
4. P ARTICIPANTS ARE BETTER , MORE CONFIDENT AND FASTER WITH NATURAL IMAGES
Synthetic images can be helpful:
Given synthetic reference images generated via feature visualization (Olah et al., 2017), participants are able to predict whether a certain network feature map prefers one over the other query image with an accuracy of 82 ± 4% , which is well above chance level ( 50% ) (see Fig.
3A).
However, performance is even higher in what we intended to be the baseline condition: natural reference images ( 92 ± 2% ).
Additionally, for correct answers, participants much more frequently report being highly certain on natural relative to synthetic trials (see Fig.
3B), and their average reaction time is approximately 3 .
7 seconds faster when seeing natural than synthetic reference images (see Fig.
3C).
Taken together, these ﬁndings indicate that in our setup, participants are not just better overall, but also more conﬁdent and substantially faster on natural images.
4. N ATURAL IMAGES ARE MORE HELPFUL ACROSS A BROAD RANGE OF LAYERS
Next, we take a more ﬁne-grained look at performance across different layers and branches of the
Inception modules (see Fig. 4).
Generally, feature map visualizations from lower layers show lowlevel features such as striped patterns, color or texture, whereas feature map visualizations from [Figure] P r o p o r t i o n C o rr e c t [Figure]
higher layers tend to show more high-level concepts like (parts of) objects (LeCun et al., 2015;
G¨uc¸l¨u & van Gerven, 2015; Goodfellow et al., 2016).
We ﬁnd performance to be reasonably high across most layers and branches: participants are able to match both low-level and high-level patterns (despite not being explicitly instructed what layer a feature map belonged to).
Again, natural images are mostly more helpful than synthetic images.
4. F OR EXPERT AND LAY PARTICIPANTS ALIKE : NATURAL IMAGES ARE MORE HELPFUL
Explanation methods seek to explain aspects of algorithmic decision-making.
Importantly, an explanation should not just be amenable to experts but to anyone affected by an algorithm’s decision.
We here test whether the explanation method of feature visualization is equally applicable to expert and lay participants (see Fig.
5A).
Contrary to our prior expectation, we ﬁnd no signiﬁcant differences in expert vs. lay performance (RM ANOVA, p = .
44 , for details see Appendix Sec.
A.2.2).
Hence, extensive experience with CNNs is not necessary to perform well in this forward simulation task.
In line with the previous main ﬁnding, both experts and lay participants are both better in the natural than in the synthetic condition.
4. E VEN FOR HAND - PICKED FEATURE VISUALIZATIONS , PERFORMANCE IS HIGHER ON NATURAL IMAGES
Often, explanation methods are presented using carefully selected network units, raising the question whether author-chosen units are representative for the interpretability method as a whole.
Olah et al.
(2017) identify a number of particularly interpretable feature maps in Inception V1 in their appendix overview.
When presenting either these hand-picked visualizations 4 or randomly selected ones, performance for hand-picked feature maps improves slightly (Fig. 5B); however this performance difference is small and not signiﬁcant for both natural
(Wilcoxon test, p = . 59 ) and synthetic (Wilcoxon test, p = .
18 ) reference images (see Appendix Sec.
A.2.4 for further analysis).
Consistent with the ﬁndings reported above, performance is higher for natural than for synthetic reference images even on carefully selected hand-picked feature maps .
4. A DDITIONAL INFORMATION BOOSTS PERFORMANCE , ESPECIALLY FOR NATURAL IMAGES
Publications on feature visualizations vary in terms of how optimized images are presented: Often, a single maximally activating image is shown (e.g. Erhan et al.
; Carter et al.
; Olah et al.
(2018)); sometimes a few images are shown simultaneously (e.g. Yosinski et al.
; Nguyen et al.
(2016b)), and
on occasion both maximally and minimally activating images are shown in unison (Olah et al.
Naturally, the question arises as to what inﬂuence (if any) these choices have, and whether there is an optimal way of presenting extremely activating images.
For this reason, we systematically compare approaches along two dimensions: the number of reference images (vs. 9 ) and the availability of minimally activating images (only Max vs. Min+Max).
The results can ourselves.
[Figure]
Synthetic
Natural Selection Mode
B
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C
Presentation Scheme Synthetic
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Natural
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[Figure]
be found in Fig.
5C. When just a single maximally activating image is presented (condition Max 1), natural images already outperform synthetic images ( 73 ± 4% vs. 64 ± 5% ).
With additional information along either dimension, performance improves both for natural as well as for synthetic images.
The stronger boost in performance, however, is observed for natural reference images.
In fact, performance is higher for natural than for synthetic reference images in all four conditions.
In the Min+Max 9 condition, a replication of the result from Experiment I shown in Fig.
3A, natural images now outperform synthetic images by an even larger margin ( 91 ± 3 vs. 72 ± 4 %).
4. S UBJECTIVELY , INTERPRETABILITY OF FEATURE VISUALIZATIONS VARIES GREATLY
While our data suggests that feature visualizations are indeed helpful for humans to predict CNN activations, we want to emphasize again that our design choices aim at an upper bound on their informativeness.
Another important aspect of evaluating an explanation method is the subjective impression.
Besides recording conﬁdence ratings and reaction times, we collect judgments on intuitiveness trials (see Appendix Fig. 14) and oral impressions after the experiments.
The former ask for ratings of how intuitive feature visualizations appear for natural images.
As Fig.
6A+B show, participants perceive the intuitiveness of synthetic feature visualizations for strongly activating natural dataset images very differently.
Further, the comparison of intuitiveness judgments before and after the main experiments reveals only a small signiﬁcant average improvement for one out of three feature maps (see Fig.
6B+C, Wilcoxon test, p < .
001 for mixed4b). The interactive conversations paint a similar picture: Some synthetic feature visualizations are perceived as intuitive while others do not correspond to understandable concepts.
Nonetheless, four participants report that their ﬁrst “gut feeling” for interpreting these reference images (as one participant phrased it) is more reliable.
Further, a few participants point out that the synthetic visualizations are exhausting to understand.
Finally, three participants additionally emphasize that the minimally activating reference images played an important role in their decision-making.
In a by-feature-map analysis (see Appendix A.2.7 for details and images, as well as Supplementary
Material 1 for more images), we compare differences and commonalities for feature maps of different performance levels.
According to our observations, easy feature maps seem to contain clear object parts or shapes.
In contrast, difﬁcult feature maps seem to have diverse reference images, features that do not correspond to human concepts, or contain conﬂicting information as to which commonalities between query and reference images matter more.
Bluntly speaking, we are also often surprised that participants identiﬁed the correct image — the reasons for this are unclear to us.
D ISCUSSION & C ONCLUSION
Feature visualizations such as synthetic maximally activating images are a widely used explanation method, but it is unclear whether they indeed help humans to understand CNNs.
Using wellcontrolled psychophysical experiments with both expert and lay participants, we here conduct the
[Figure]
I n t u i t i v e n e ss 3a
[Figure]
I n t u i t i v e n e ss
D i ff e r e n c
e
i n
I n t u i t i v e n e ss very ﬁrst investigation of intermediate synthetic feature visualizations by Olah et al.
(2017): Can participants predict which of two query images leads to a strong activation in a feature map, given extremely activating visualizations?
Speciﬁcally, we shed light on the following questions: (1.)
How informative are synthetic feature visualizations — and how do they compare to a natural image baseline?
We ﬁnd above-chance performance given synthetic feature visualizations, but to our own surprise, synthetic feature visualizations are systematically less informative than the simple baseline of strongly activating natural images.
Interestingly, many synthetic feature visualizations contain regularization mechanisms to introduce more “natural structure” (Olah et al., 2017), sometimes even called a “natural image prior” (Mahendran & Vedaldi, 2015; Offert & Bell, 2020).
This raises the question: Are natural images maybe all you need?
One might posit that extremely activating natural (reference) images would have an unfair advantage because we also test on extremely activating natural (query) images.
However, our task design ultimately reﬂects that XAI is mainly concerned with explaining how units behave on natural inputs.
Furthermore, the fact that feature visualization are not bound to the natural image manifold is often claimed as an advantage because it supposedly allows them to capture more precisely which features a unit is sensitive to (Olah et al., 2017).
Our results, though, demonstrate that this is not the case if we want to understand the behavior of units on natural inputs.
Do you need to be a CNN expert in order to understand feature visualizations?
To the best of our knowledge, our study is the ﬁrst to compare the performances of expert and lay people when evaluating explanation methods.
Previously, publications either focused on only expert groups (Hase Bansal,
2020; Kumarakulasinghe
et
al. or only laypeople (Schmidt Biessmann
; Al- ufaisan et al Our experiment shows no signiﬁcant difference between expert and
lay
participants in our task — both perform
similarly well, and even better on natural images: a replication of our main ﬁnding.
While a few caveats remain when moving an experiment from the well-controlled lab to a crowdsourcing platform (Haghiri et al., 2019), this suggests that future studies may not have to rely on selected expert participants, but may leverage larger lay participant pools.
Are hand-picked synthetic feature visualizations representative?
An open question was whether the visualizations shown in publications represent the general interpretability of
feature
visualizations (
a concern voiced by e g
Kriegeskorte, 2015),
even though they are hand-picked (Olah et al., 2017).
Our ﬁnding
that
there is
no
large
difference
in performance between hand- and randomly - picked feature visualizations
suggests that this aspect
is
minor.
What is the best way of
presenting images?
Existing work suggests that more than one example (Offert, 2017) and particularly negative examples (Kim et al., 2016) enhance human understanding of data distributions.
Our systematic exploration of presentation schemes provides evidence that increasing the number of reference images as well as presenting both minimally and maximally activating reference images (as opposed to only maximally activating ones) improve human performance.
This ﬁnding might be of interest to future studies aiming at peak performance or for developing software for understanding CNNs.
How do humans subjectively perceive feature visualizations?
Apart from the high informativeness of explanations, another relevant question is how much trust humans have in them.
In our experiment, we ﬁnd that subjective impressions of how reasonable synthetic feature visualizations are for explaining responses to natural images vary greatly.
This ﬁnding is in line with Hase &
Bansal (2020) who evaluated explanation methods on text and tabular data.
Caveats.
Despite our best intentions, a few caveats remain: The forward simulation paradigm is only one speciﬁc way to measure the informativeness of explanation methods, but does not allow us to make judgments about their helpfulness in other applications such as comparing different CNNs.
Further, we emphasize that all experimental design choices were made with the goal to measure the best possible performance.
As a consequence, our ﬁnding that synthetic reference images help humans predict a network’s strongly activating image may not necessarily be representative of a less optimal experimental set-up with e.g. query images corresponding to less extreme feature map activations.
Knobs to further de- or increase participant performance remain (e.g. hyper-parameter choices could be tuned to layers).
Finally, while we explored one particular method in depth (Olah et al., 2017); it remains an open question whether the results can be replicated for other feature visualizations methods.
Future directions.
We see many promising future directions.
For one, the current study uses query images from extreme opposite ends of a feature map’s activation spectrum.
For a more ﬁne-grained measure of informativeness, we will study query images that elicit more similar activations.
Additionally, future participants could be provided with even more information—such as, for example, where a feature map is located in the network.
Furthermore, it has been suggested that the combination of synthetic and natural reference images might provide synergistic information to participants (Olah et al., 2017), which could again be studied in our experimental paradigm.
Finally, further studies could explore single neuron-centered feature visualizations, combinations of units as well as different network architectures.
Taken together, our results highlight the need for thorough human quantitative evaluations of feature visualizations and suggest that example natural images provide a surprisingly challenging baseline for understanding CNN activations.
A UTHOR C ONTRIBUTIONS
The initiative of investigating human predictability of CNN activations came from WB.
JB, WB, MB and
TSAW jointly combined it with the idea of investigating human interpretability of feature visualizations.
JB led the project.
JB, RSZ and JS jointly designed and implemented the experiments (with advice and feedback from TSAW, RG, MB and WB). The data analysis was performed by JB and RSZ (with advice and feedback from RG, TSAW, MB and WB). JB designed, and JB and JS implemented the pilot study. JB conducted the experiments (with help from JS).
RSZ performed the statistical signiﬁcance tests (with advice from TSAW and feedback from JB and RG).
MB helped shape the bigger picture and initiated intuitiveness trials.
WB provided day-to-day supervision.
JB, RSZ and RG wrote the initial version of the manuscript.
All authors contributed to the ﬁnal version of the manuscript.
A CKNOWLEDGMENTS
We thank Felix A. Wichmann and Isabel Valera for helpful discussions.
We further thank Alexander B¨ottcher and Stefan Sietzen for support as well as helfpul discussions on technical details.
Additionally, we thank Chris
Olah for clariﬁcations via slack .
distill .
pub .
Moreover, we thank Leon Sixt for valuable feedback on the introduction and related work.
From our lab, we thank Matthias K¨ummerer, Matthias Tangemann, Evgenia
Rusak and Ori Press for helping in piloting our experiments, as well as feedback from Evgenia Rusak, Claudio
Michaelis, Dylan Paiton and Matthias K¨ummerer.
And ﬁnally, we thank all our participants for taking part in our experiments.
We thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting JB,
RZ and RG.
We acknowledge support from the German Federal Ministry of Education and Research (BMBF) through the Competence Center for Machine Learning (TUE.AI, FKZ 01IS18039A) and the Bernstein Computational Neuroscience Program T¨ubingen
(FKZ: 01GQ1002), the Cluster of Excellence Machine Learning:
A
PPENDIX
A.
D ETAILS ON METHODS A.1. H UMAN E XPERIMENTS
In our two human psychophysical studies, we ask humans to predict a feature map’s strongly activating image (“forward simulation task”, Doshi-Velez & Kim 2017).
Answers to the two-alternative forced choice paradigm are recorded together with the participants’ conﬁdence level ( 1 : not conﬁdent, 2 : somewhat conﬁdent, 3 : very conﬁdent, see Fig. 7).
Time per trial is unlimited and we record reaction time.
After each trial, feedback is given (see Fig. 7).
A progress bar at the bottom of the screen indicates how many trials of a block are already completed.
As reference images, either synthetic, natural or no reference images are given.
The synthetic images are the feature visualizations from the method of Olah et al.
Trials of different reference images are arranged in blocks.
Synthetic and natural reference images are alternated, and, in the case of Experiment I, framed by trials without reference images (see Fig.
8A, B).
The order of the reference image types is counter-balanced across subjects.
The main trials in the experiments are complemented by practice, catch and intuitiveness trials.
To avoid learning effects, we use different feature maps for each trial type per participant.
Speciﬁcally, practice trials give participants the opportunity to familiarize themselves with the task.
In order to monitor the attention of participants, catch trials appear randomly throughout blocks of main trials.
Here, the query images are a copy of one of the reference images, i.e., there is an obvious correct answer (see Fig. 15).
This control mechanism allows us to decide whether trial blocks should be excluded from the analysis due to e.g. fatigue.
To obtain the participant’s subjective impression of the helpfulness of maximally activating images, the experiments are preceded (and also succeeded in the case of Experiment II) by three intuitiveness trials (see Fig. 14).
Here, participants judge in a slightly different task design how intuitive they consider the synthetic stimuli for the natural stimuli.
For more details on the intuitiveness trials, see below.
At the end of the experiment, all expert participants in Experiment I and all lay (but not expert) participants in Experiment II are asked about their strategy and whether it changed over time.
The information gained through the ﬁrst group allows us to understand the variety of cues used and paves the way to identify interesting directions for follow-up experiments.
The information gained through the second group allowed comparisons to experts’ impressions reported in Experiment I.
Experiment I
The ﬁrst experiment focuses on comparing performance of synthetic images to two baselines: natural reference images and no reference images (see Fig.
8A).
Screenshots of trials are shown in Fig.
In total, 45 feature maps are tested: 36 of these are uniformly sampled from the feature maps of each of the four branches for each of the nine Inception modules.
The other nine feature maps are uniformly hand-picked for interpretability from the Inception modules’ pooling branch based on the appendix overview selection provided by Olah et al.
(2017) or based on our own choices.
In the spirit of a general statement about the explainability method, different participants see different natural reference and query images, and each participant sees different natural query images for the same feature maps in different reference conditions.
To check the consistency of participants’ responses, we repeat six randomly chosen main trials for each of the three tested reference image types at the end of the experiment.
Experiment II
The second experiment (see Fig.
8B) is about testing expert vs. lay participants as well as comparing different presentation schemes 5 (Max 1, Min+Max 1, Max 9 and Min+Max 9, see Fig.
8E).
Screenshots of trials are shown in Fig.
In total, 80 feature maps are tested: They are uniformly sampled from every second layer with an Inception module of the network (hence a total of 5 instead of 9 layers), and from all four branches of the Inception modules.
Given the focus on four different presentation schemes in this experiment, we repeat the sampling method four times without overlap.
In terms of reference image types, only synthetic and natural images are tested.
Like in Experiment I, different participants see different natural reference and query images.
[Figure]
[Figure] [Figure] [Figure]
[Figure]
A
C
[Figure]
B
Experiment II (Expert & Lay Participants)
Experiment I
(Expert Participants)
[Figure]
E
B: Experiment II.
Here, the focus is on testing expert and lay participants as well as comparing different presentation schemes (Max 1, Min+Max 1, Max 9 and Min+Max 9, see E for illustrations) Both the order of natural and synthetic blocks as well as the four presentation conditions are counterbalanced across participants.
To maintain a reasonable experiment length for each participant, only 20 relevant trials are used reference type presentation plus additional
However, expert and lay participants see the same images.
For details on the counter-balancing of all conditions, please refer to
Intuitiveness Trials
In order to obtain the participants’ subjective impression of the helpfulness of maximally activating images, we add trials at the beginning of the experiments, and also at the end of Experiment II.
The task set-up is slightly different (see Fig.
14): Only maximally activating (i.e. no minimally activating) images are shown.
We ask participants to rate how intuitive they ﬁnd the explanation of the entirety of the synthetic images for the entirety of the natural images.
Again, all images presented in one trial are speciﬁc to one feature map.
By moving a slider to the right (left), participants judge the explanation method as intuitive (not intuitive).
The ratings are recorded on a continuous scale from − 100 (not intuitive) to +100 (intuitive).
All participants see the same three trials in a randomized order.
The trials are again taken from the hand-picked (i.e. interpretable) feature maps of the appendix overview in Olah et al.
In theory, this again allows for the highest intuitiveness ratings possible.
The speciﬁc feature maps are from a low, intermediate and high layer: feature map 43 of mixed3a, feature map 504 of mixed4b and feature map 17 of mixed 5b.
Participants Our two experiments are within-subject studies, meaning that every participant answers trials for all conditions.
This design choice allows us to test fewer participants.
In Experiment I, 10 expert participants take part ( 7 male, 3 female, age: 27 .
2 years, SD = 1 . 75 ).
In Experiment II, 23 participants take part (of which 10 are experts; 14 male, 9 female, age: 28 . 1 years, SD = 6 . 76 ).
Expert participants qualify by being familiar or having worked with convolutional neural networks and most of them even with feature visualization techniques.
All participants are naive with respect to the aim of the study.
Expert (lay) participants are paid 15 e (10 e ), per hour for participation.
Before the experiment, all participants give written informed consent for participating.
All participants have normal or corrected to normal vision.
All procedures conform to Standard
8 of the American Psychological 405 Association’s “Ethical Principles of Psychologists and Code of Conduct” (2016).
Before the experiment, the ﬁrst author explains the task to each participant and ensures complete understanding.
For lay participants, the explanation is simpliﬁed:
Maximally (minimally) activating images are called “favorite images” (“non-favorite images”) of a “computer program” and the question is explained as which of the two query images would also be a “favorite” image to the computer program.
Apparatus Stimuli are displayed on a VIEWPixx 3D LCD (VPIXX Technologies; spatial resolution 1920 × 1080 px, temporal resolution 120 Hz ).
Outside the stimulus image, the monitor is set to mean gray.
Participants view the display from 60 cm (maintained via a chinrest) in a darkened chamber.
At this distance, pixels subtend approximately 0 .
024 ° degrees on average ( 41 ps per degree of visual angle).
Stimulus presentation and data collection is controlled via a desktop computer (Intel Core i5-4460 CPU, AMD Radeon R9 380 GPU) running Ubuntu Linux (16.04 LTS), using
PsychoPy (Peirce et al., 2019, version 3.0) under Python 3.6.
A.1. S TIMULI S ELECTION
Model Following the existing work on feature visualization by Olah et al. (2017; 2018; 2020b;a), we use an Inception V1 network 6 (Szegedy et al., 2015) trained on ImageNet (Deng et al., 2009; Russakovsky et al., 2015).
Note that the Inception V1 network used in previously mentioned work slightly deviates from the original network architecture:
The 3 × 3 branch of Inception module mixed4a only holds 204 instead of 208 feature maps.
To stay as close as possible to the aforementioned work, we also use their implementation and trained weights of the network 7 .
We investigate feature visualizations for all branches (i.e. kernel sizes) of the Inception modules and sample from layers mixed3a to mixed5b before the ReLU non-linearity.
Synthethic Images from Feature Visualization
The synthetic images throughout this study are the optimization results of the feature visualization method from Olah et al.
We use the channel objective to ﬁnd synthetic stimuli that maximally (minimally) activate the spatial mean of a given feature map of the network.
We perform the optimization using lucid 0.3.8 and TensorFlow 1.15.0 (Abadi et al., 2015) and use the hyperparameter as speciﬁed in Olah et al.
For the experimental conditions with more than one minimally/maximally activating reference image, we add a diversity regulariztion across the samples.
In hindsight, we realized that we generatedsynthetic images in Experiment I, even though we only needed and used 9 per feature map.
Selection of Natural Images The natural stimuli are selected from the validation set of the ImageNet ILSVRC 2012 (Russakovsky et al., 2015) dataset.
To choose the maximally (minimally) activating natural stimuli for a given feature map, we perform three steps, which are illustrated in
Fig. 9 and explained in the following: First, we calculate the activation of said feature map for all pre-processed images (resizing to 256 × 256 pixels, cropping centrally to 224 × 224 pixels and normalizing) and take the spatial average to get a scalar representing the excitability of the given feature map caused by the image.
Second, we order the images according to the collected activation values and select the ( N stimuli +1) · N batches maximally (respectively minimally) activating images.
Here, N stimuli corresponds to the number of reference images used (either 1 or 9 , see Fig.
8, E ), the + comes from the query image, and N batches =
20 determines the maximum number of participants we can test with our setup.
Third, we distribute the selected images into N stimuli +1 blocks.
Within each block, we randomly shufﬂe the order of the images.
Lastly, we create N batches batches of data by selecting one image from each of the blocks for every batch.
[Figure]
Subject
Order of presentation schemes
(0-3) and batch-blocks (A-D)
Batches
Order of synthetic and natural Practice
Main 0 (A) 1 (B) 2 (C) 3 (D) natural:synthetic:natural - synthetic0 (B)
(D) 1 (C)
(A) 3 (B
[Table]
The reasons for creating several batches of extremely activating natural images are two-fold: (1) We want to get a general impression of the interpretability method and would like to reduce the dependence on single images, and (2) in Experiment I, a participant has to see different query images in the three different reference conditions.
A downside of this design choice is an increase in variability.
The precise allocation was done as follows:
In Experiment I, the natural query images of the none condition were always allocated the batch with batch nr = subject id , the query and reference images of the natural condition were allocated the batch with batch nr = subject id +1 , and the natural query images of the synthetic condition were allocated the batch with batch nr = subject id +2 .
The allocation scheme in Experiment II can be found in Table 1.
Selection of Feature Maps The selection of feature maps used in Experiment I is shown in Table 2; the selection of feature maps used in Experiment II is shown in Table 3.
[Table]
A.1. D IFFERENT A CTIVATION M AGNITUDES
We note that the elicited activations of synthetic images are almost always about one magnitude larger than the activations of natural images (see Fig. 10a).
This constitutes an inherent difference in the synthetic and natural reference image condition.
A simple approach to make the two conditions more comparable is to limit the optimization process such that the resulting feature visualizations elicit activations similar to that of natural images.
This can be achieved by halting the optimization process once the activations approximately match.
By following that procedure one ﬁnds limited synthetic images which are indistinguishable from natural images in terms of their activations (see
Fig. 10b).
Importantly though, these images are visually not more similar to natural images, have a much lower color contrast than normal feature visualizations, and above all hardly resemble meaningful features (see Fig. 11).
A.1. D ATA A NALYSIS
Signiﬁcance Tests All signiﬁcance tests are performed with JASP (JASP Team, 2020, version 0.13.1).
For the analysis of the distribution of conﬁdence ratings (see Fig.
3B), we use contingency tables with χ 2 -tests.
For testing pairwise effects in accuracy, conﬁdence, reaction time and intuitiveness data, we report Wilcoxon signed-rank tests with uncorrected p-values (Bonferroni-corrected critical alpha values with family-wise alpha level of 0 .
05 reported in all ﬁgures where relevant).
These non-parametric tests are preferred for these data because they do not make distributional assumptions like normally-distributed errors, as in e.g. paired t -tests.
For testing marginal effects (main effects of one factor marginalizing over another) we report results from repeated measures
ANOVA (RM ANOVA), which does assume normality.
m i x e da 1 xm
i x
e da 3 xm i x
e da 5 xm i x
e da P oo l m i x e da P oo l * m i x e db 1 xm i x
e db 3 xm
i x e db 5 xm i x
e db P oo l m i x e db P oo l * m i x e da 1 xm i x
e da 3 xm i x
e da 5 xm i x
e da P oo l m i x e da P oo l * m i x e db 1 xm i x
e db 3 xm
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e db P oo l m i x e db P oo l * m i x e dc 1 xm i
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e dd 3 xm i
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e dd 5 xm
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e dd P oo l m i x e dd P oo l * m i x e de 1 xm i x
e de 3 xm i x
e de 5 xm i x e de P oo l m i x e de P oo l * m i x
e da 1 xm i x
e da 3 xm i x
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e da P oo l m i x e da P oo l * m i x
[Table]
−
[Table]
d _ 4 d P oo l * m i x e d _ 4 e 1 xm i x
e d _ 4 e 3 xm i x
e d _ 4 e 5 xm
i x e d _ 4 e P oo l m i x e d _ 4 e P oo l * m i x e d _ 5 a 1 xm i x e d _ 5 a 3 xm i x e d _ 5 a 5 xm i x e d _ 5 a P oo l m i x e d _ 5 a P oo l * m i x e d _ 5 b 1 xm i x e d _ 5 b 3 xm i x e d _ 5 b 5 xm i x e d _ 5 b P oo l m i x e d _ 5 b P oo l *
[Figure] [Figure]
Layer
Branch
Feature Map for
[Table]
A.
D ETAILS ON RESULTS A.2. C OMPLEMENTING FIGURES FOR MAIN RESULTS
Figures 16 - 21 complement the results and ﬁgures presented in Section 4.
Here, all experimental conditions are shown.
A.2. D ETAILS ON PERFORMANCE OF EXPERT AND LAY PARTICIPANTS
As reported in the main body of the paper, a mixed-effects ANOVA revealed no signiﬁcant main effect of expert level ( F (1 , 21) = 0 .
6 , p = 0 .
44 , between-subjects effect).
Further, there is no signiﬁcant interaction with the reference image type ( F (1 , 21) = 0 .
4 , p = 0 .
53 ), and both expert and lay participants show a signiﬁcant main effect of the reference image type ( F (1 , 21) = 230 .
2 , p < 0 .
[Figure]
r t i
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i
[Figure] [Figure]
[Figure] [Figure]
[Figure] [Figure]
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r t i o n
C
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c
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layers
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A.2. D ETAILS ON PERFORMANCE OF EXPERTS SPLIT BY DIFFERENT LEVELS OF EXPERTISE
Even though Experiment II does not show a signiﬁcant performance difference for lay and expert participants, it is an open question whether the level of expertise or the background of experts matters.
For the data from experts, we hence further divide participants into subgroups according to their expertise (see Fig.
20a-f) and background level (see Fig.
20g-h).
Expertise level 1 means that participants are familiar with CNNs, but not feature visualizations; expertise level 2 means that participants have heard of or read about feature visualizations; and expertise level 3 means that participants have used feature visualizations themselves.
We note that we also accepted feature visualizations methods other than the one by Olah et al.
(2017), e.g. DeepDream (Mordvintsev et al., 2015) for level 2 and 3.
Regarding background, we distinguished computational neuroscientists from researchers working on computer vision and / or machine learning.
We note that some subgroups only hold one participant and hence may not be representative.
Our data shows varying trends for the three expert levels (see Fig.
20a-f): For synthetic images, performance decreases with increasing expertise in Experiment I, but increases for Experiment II.
For natural images, performance ﬁrst increases for participants of expertise level 2, and then slightly decreases for participants with expertise level 3 - a trend that holds for both Experiment I and II.
In the none condition of Experiment I, performance is highest for the participant of expertise level 1, but decreases for participants of expertise level 2, and again slightly increases for expertise level 3.
Regarding expert’s different backgrounds, our hypothesis is that many of the computational neuroscientists are very familiar with maximally exciting images for monkeys or rodents, and hence might perform better than pure computer vision / machine learning experts.
Fig.
20g-h suggest that this is not the case: The bars for all three reference image types are very similar.
Not ﬁnding clear trends in our data between different expertise levels or experts is not surprising as there is even no signiﬁcant difference between participants whose professional backgrounds are much further apart: lay people vs. people familiar with CNNs.
A.2. D ETAILS ON PERFORMANCE OF HAND - AND RANDOMLY - PICKED FEATURE MAPS
As described in the main body of the paper, pairwise Wilcoxon sign-rank tests reveal no signiﬁcant differences between hand-picked and randomly-selected feature maps within each reference image type ( Z (9) = 27 .
5 , p = 0 .
59 for natural reference images and Z (9) = 41 p = 0 .
18 for synthetic references).
However, marginalizing over reference image type using a repeated measures ANOVA reveals a signiﬁcant main effect of the feature map selection mode: F (1 , 9) = 6 .
14 , p = 0 .
Therefore, while there may be a small effect of hand-picking feature maps, our data indicates that this effect, if present, is small.
A.2. R EPEATED TRIALS
To check the consistency of participants’ responses, we repeat six main trials for each of the three tested reference image types at the end of the experiment.
Speciﬁcally, the six trials correspond to the three highest and three lowest absolute conﬁdence ratings.
Results are shown in Fig.
We observe consistency to be high for both the synthetic and natural reference image types, and moderate for no reference images (see Fig. 21A).
In absolute terms, the largest increase in performance occurs for the none condition; for natural reference images there was also a small increase; for synthetic reference images, there was a slight decrease (see Fig.
21B and C).
In the question session after the experiments, many participants reported remembering the repeated trials from the ﬁrst time.
A.2. Q UALITATIVE F INDINGS
In a qualitative interview conducted after completion of the experiment, participants reported to use a large variety of strategies.
Colors, edges, repeated patterns, orientations, small local structures and (small) objects were commonly mentioned.
Most but not all participants reported to have adapted their decision strategy throughout the experiment.
Especially lay participants from Experiment II emphasized that the trial-by-trial feedback was helpful and that it helped to learn new strategies.
As already described in the main text, participants reported that the task difﬁculty varied greatly; while some trials were simple, others were challenging.
A few participants highlighted that the comparison between minimally and maximally activating images was a crucial clue and allowed employing the
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[Figure] [Figure]
exclusion criterion: If the minimally activating query image was easily identiﬁable, the choice of the maximally activating query image was trivial.
This aspect motivated us to conduct an additional experiment where the presentation scheme was varied (Experiment II).
A.2. B Y -F EATURE -M AP A NALYSIS
For Experiment I, we look at each feature map separately and analyze which feature maps participants ﬁnd easy and which they ﬁnd difﬁcult.
Further, we investigate commonalities and differences between feature maps.
We note that the data for this analysis relies on only 10 responses for each feature map and hence may be noisy.
In Fig.
22, we show the number of correct answers split up by reference image type.
The patterns look similar to the trend in Fig.
Across most layers, there is no clearly identiﬁable trend that feature maps of a certain network depth would be easier or more difﬁcult; only the lowest (3a) and the highest layer (5b) seem slightly more difﬁcult for both the synthetic and the natural reference images.
Easy Feature Maps When feature maps are easy (synthetic: 10 / 10 , natural: 10 / 10 correct responses), their features seem to correspond to clear object parts (e.g. dogs vs. humans, food vs. cats), or shapes (e.g. round vs. edgy (see Supplementary Material Fig.
In Fig.
23, we show the query as well as natural and synthetic reference images for one such easy feature map for one participant.
For the images shown to two more participants, see Supplementary Material Fig.
Other relatively easy feature maps (where eight to ten participants choose the correct query image for both reference image types) additionally contained other low level cues such as color or texture (see Supplementary Material Fig.
Difﬁcult Feature Maps The most difﬁcult feature maps for synthetic and natural reference images are displayed in Fig.
Only four participants predicted the correct query image.
Interestingly, the other reference image type was much more easily predictable for both feature maps: Nine out of ten participants correctly simulated the network’s decision.
Our impression is that the reason for these feature maps being so difﬁcult in one reference condition is the diversity in the images.
In the case of synthetic reference images, we also consider identifying a concept difﬁcult and consequently are unsure what to compare.
From studying several feature maps, our impression is that one or more of the following aspects make feature maps difﬁcult to interpret:
• Reference images are diverse (see Fig. 24a for synthetic reference images and d for natural reference images)
[Table]
o p t i m i z e d # c o rr e c t a n s w
[Table]
r s ( o u t o f 10 )
[Table]
n a t u r a l # c o rr e c t a n s w e r
[Table]
( o u t o f 10 )
[Table] [Table]
n
[Table]
n e # c
[Table]
rr e c t a n s w e r
s
o
[Table]
o f 10 ) 5 6 5 7 2 2 2       4 4 3 3 4 45 7 4   3
[Figure]
• The common feature(s) seem to not correspond to common human concepts (see Fig. 24a and c)
• Conﬂicting information,
i
e. commonalities can be found between one query image and both the minimal and maximal reference images (see Fig.
25a: eyes and extremity-like structure in synthetic min reference images vs. eyes and earth-colors in synthetic max reference images - both could be considered similar to the max query image of a frog) • Very small object parts such as eyes or round, earth-colored shapes seem to be the decisive features (see Fig.
25a and b) • Low level cues such as the orientation of lines appear random in the synthetic reference images 9 (see Fig. 26a)
Finally, when we speak bluntly, we are often surprised that participants identiﬁed the correct image — the reasons for this are unclear to us (see for example Supplementary Material Fig.
A.2. H IGH QUALITY DATA AS SHOWN BY HIGH PERFORMANCE ON CATCH TRIALS
We integrate a mechanism to probe the quality of our data: In catch trials , the correct answer is trivial and hence incorrect answers might suggest the exclusion of speciﬁc trial blocks (for details, see Sec.
A.1.1).
Fortunately, very few trials are missed:
In Experiment I, only two (out of ten) participants miss one trial each (i.e. a total of 2 out of 180 catch trials were missed); in Experiment II, ﬁve participants miss one trial and four participants miss two trials (i.e. a total of 13 out of 736 catch
[Figure]
[Figure] [Figure]
trials were missed).
As this indicates that our data is of high quality, we do not perform the analysis with excluded trials as we expect to ﬁnd the same results.
8 Baseline condition.
9 Metrics of explanation quality computed without human judgment are inconclusive and do not correspond to human rankings.
10 Task has an additional “I don’t know”-option for conﬁdence rating.
A. D ETAILS ON R ELATED W ORK
Paper
Analyzes
IntermediateFeatures? Explanation Methods Analyzed Results Explanation Conﬁdence/Trust helpful?
Ours yes • Feature Visualization • natural images• no explanationyes • high variance in conﬁdence ratings • natural images are more helpful Biessmann&Reﬁano (2019) no • LRP
[Table]
Guided Backprop • simple gradientyes • highest
[Table]
for guided backprop Chu et al. no
[Table]
prediction• no informationno
[Table]
faulty explanations do not decrease trust Shen & Huan (2020)
no
[Table]
no explanation [Table]
no
[Table]
Jeyakumaretal.
no
[Table]
SHAP [Table] unclear
[Table]
Alqaraawietal.
no
[Table]
scores
[Table]
no
[Table]
yes
[Table]
across conditions Chandrasekaranetal. (2017) no • prediction
[Table]
maps
[Table]
no explanation
[Table]
no
[Table]
Schmidt & Biessmann (2019) no [Table] method
[Table]
/no explanation
[Table]
yes
[Table]
humans trust own judgement regardless explanations, except in one condition
Hase & Bansal (2020) no • LIME
[Table]
Prototype • Anchor • Decision Boundary • combination of allpartly • high variance in helpfulness • helpfulness cannot predict user performance Kumarakulasingheetal. (2020) no • LIME yes • fairly high trust and reliance
Ribeiro et al. no • LIME • Anchor • no explanationyes • high conﬁdence for Anchor • low for LIME & no explanation Alufaisanetal. (2020) no • prediction + Anchor • prediction• no informationpartly • explanations
[Table]
Ramamurthyetal.
no
[Table]
Dieber & [Table] [Table] Dinuetal.
[Table]
partly [Table] [Table] [Table]
Schmidt &
[Table]
[Table]
[Table]
[Table]
Ribeiroetal.
[Table]
[Table]