This is the second post in a 3-post series on Explainable AI (XAI). The first post highlighted examples and offered practical advice on how and when to use XAI techniques for computer vision tasks. In this post, we will expand the discussion by offering advice on some of the XAI limitations and challenges that you might find along the path of XAI adoption.At this point, you might have heard about Explainable AI and want to explore XAI techniques within your work. But along the way you might also have wondered : Just how well do XAI techniques work? And what should you do if you find the results of XAI aren’t as compelling as you’d like? Below, we explore some of the questions surrounding XAI with a healthy dose of skepticism.
Are expectations too high?XAI – like many subfields of AI – can be subject to some of the hype, fallacies, and misplaced hopes associated with AI at large, including the potential for unrealistic expectations. Terms such as explainable and explanation risk falling under the fallacy of matching AI developments with human abilities (the 2021 paper Why AI is Harder Thank We Think brilliantly describes four similar fallacies in AI assumptions). This fallacy leads to the unrealistic expectation that we are approaching a point in time where AI solutions will not only achieve great feats and eventually surpass human intelligence, but – on top of that – they will be able to explain how and why AI did what it did; therefore, increasing the level of trust in AI decisions. Once we become aware of this fallacy, it is legitimate to ask ourselves: How much can we realistically expect from XAI? In this post we will discuss whether our expectations for XAI (and the value XAI techniques might add to our work) are too high and focus on answering three main questions:
- Can XAI become a proxy for trust(worthiness)?
- What limitations of XAI techniques should we be aware of?
- Can consistency across XAI techniques improve user experience?
Figure 1: Example of misclassification in a “husky vs. wolf” image classifier due to a spurious correlation between images of wolves and the presence of snow. The image on the right, which shows the result of the LIME post-hoc XAI technique , captures the classifier blunder. Source: https://arxiv.org/pdf/1602.04938.pdf The potential usefulness of post-hoc XAI results has led to a growing adoption of techniques, such as imageLIME, occlusion sensitivity, gradCAM for computer vision tasks. However, these XAI techniques sometimes fall short of delivering the desired explanation due to some well-known limitations. Below are 3 examples:
A. XAI techniques can sometimes use similar explanations for correct and incorrect decisions.For example, in the previous post in this series, we showed that the heatmaps produced by the gradCAM function provided similarly convincing explanations (in this case, focus on the head area of the dog) both when their prediction is correct (Figure 2, top) as well as incorrect (Figure 2, bottom: a Labrador retriever was mistakenly identified as a beagle).
Figure 2: Example of results using gradCAM for the dog breed image classification task.
B. Even in cases where XAI techniques show that a model is not looking in the right place, that doesn’t necessarily mean that it is easy to know how to fix the underlying problem.In some easier cases, such as in the husky vs. wolf classification mentioned earlier, a quick visual inspection of model errors could have helped identify the spurious correlation between “presence of snow” and “images of wolves.” There is no guarantee that the same process would work for other (larger or more complex) tasks.
C. Results are model- and task-dependent.In our previous post in this series, we also showed that the heatmaps produced by the gradCAM function in MATLAB provided different visual explanations associated with the same image (Figure 3) depending on the pretrained model and task. Figure 4 shows those two examples and adds a third example, in which the same network (GoogLeNet) was used without modification. A quick visual inspection of Figure 4 is enough to spot significant differences among the three heatmaps. Figure 3: Test image for different image classification tasks and models (shown in Figure 4). Figure 4: Using gradCAM on the same test image (Figure 3), but for different image classification tasks and models. can be problematic, since the most commonly used post-hoc XAI methods use significantly different explanation interfaces (that is, visualization schemes) in their implementation (Figure 6):
- CAM (Class Activation Maps), including Grad-CAM, and occlusion sensitivity use a heatmap to correlate hot colors with salient/relevant portions of the image.
- LIME (Local Interpretable Model-Agnostic Explanations) generates superpixels, which are typicallly shown as highlighted pixels outlined in different pseudo-colors.
- SHAP (SHapley Additive exPlanations) values are used to divide pixels among those that increase or decrease the probability of a class being predicted.
TakeawayIn this blog post, we offered words of caution and discussed some limitations of existing XAI methods. Despite the downsides, there are still many reasons to be optimistic about the potential of XAI, as we will share the next (and final) post in this series.
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