A plethora of methods attempting to explain predictions of black-box models have been proposed by the Explainable Artificial Intelligence (XAI) community. Yet, measuring the quality of the generated explanations is largely unexplored, making quantitative comparisons non-trivial. In this work, we propose a suite of multifaceted metrics that enables us to objectively compare explainers based on the correctness, consistency, as well as the confidence of the generated explanations. These metrics are computationally inexpensive, do not require model-retraining and can be used across different data modalities. We evaluate them on common explainers such as Grad-CAM, SmoothGrad, LIME and Integrated Gradients. Our experiments show that the proposed metrics reflect qualitative observations reported in earlier works. Over the past few years, deep learning has made significant progress, outperforming the state-ofthe-art in many tasks like image classification (Mahajan et al., 2018) , semantic segmentation (Zhu et al., 2018) , machine translation (Kalchbrenner et al., 2016) and even surpassing humans in the games of Chess and Go (Silver et al., 2016) . As these models are deployed in more mission-critical systems, we notice that despite their incredible performance on standard metrics, they are fragile (Szegedy et al., 2013; Goodfellow et al., 2014) and can be easily fooled by small perturbations to the inputs (Engstrom et al., 2017) . Further research has also exposed that these models are biased in undesirable ways exacerbating gender and racial biases (Howard et al., 2017; Escudé Font & Costa-Jussà, 2019) . These issues have amplified the need for making these black-box models interpretable. Consequently, the XAI community has proposed a variety of algorithms that aim to explain predictions of these models (Ribeiro et al., 2016; Lundberg & Lee, 2017; Shrikumar et al., 2017; Smilkov et al., 2017; Selvaraju et al., 2016; Sundararajan et al., 2017) . With such an explosion of interpretability methods (hereon referred to as explainers), evaluating them has become non-trivial. This is due to the lack of a widely accepted metric to quantitatively compare them. There have been several attempts to propose such metrics. Unfortunately, they tend to suffer from major drawbacks like computational cost (Hooker et al., 2018) , inability to be extended to non-image domains (Kindermans et al., 2017a) , or simply focusing only one desirable attribute of a good explainer. (Yeh et al., 2019) . In this paper, we propose a suite of metrics that attempt to alleviate these drawbacks and can be applied across multiple data modalities. Unlike the vast majority of prior work, we not only consider the correctness of an explainer, but also the consistency and confidence of the generated explanations. We use these metrics to evaluate and compare widely used explainers such as LIME (Ribeiro et al., 2016) , Grad-CAM (Selvaraju et al., 2016) , SmoothGrad (Smilkov et al., 2017) and Integrated Gradients (Sundararajan et al., 2017) on an Inception-V3 (Szegedy et al., 2015) model pretrained on the ImageNet dataset (ILSVRC2012) (Deng et al., 2009) , in an objective manner (i.e., without the need of a human-in-the-loop). Moreover, our proposed metrics are general and computationally inexpensive. Our main contributions are: 1. Identifying and formulating the properties of a good explainer. 2. Proposing a generic, computationally inexpensive suite of metrics to evaluate explainers. 3. Comparing common explainers and discussing pros and cons of each. We find that while Grad-CAM seems to perform best overall, it does suffer from drawbacks not reported in prior works. On the other hand, LIME consistently underperforms in comparison to the other models.
We propose a suite of metrics that capture desired properties of explainability algorithms and use it to objectively compare and evaluate such methods
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