Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/hbaniecki/adversarial-explainable-ai

💡 Adversarial attacks on explanations and how to defend them
https://github.com/hbaniecki/adversarial-explainable-ai

adversarial adversarial-attacks adversarial-examples adversarial-machine-learning attacks counterfactual deep defense evaluation explainability explainable-ai iml interpretability interpretable interpretable-machine-learning model responsible-ai robustness security xai

Last synced: 4 months ago
JSON representation

💡 Adversarial attacks on explanations and how to defend them

Awesome Lists containing this project

README 

          
# Adversarial Attacks and Defenses in Explainable AI



A curated list of papers concerning adversarial explainable AI (AdvXAI).



### Survey



**February, 2024**: The survey is now published in Information Fusion at https://doi.org/10.1016/j.inffus.2024.102303



**September, 2023**: An extended version of the paper is now available [on arXiv](https://arxiv.org/abs/2306.06123)



**June, 2023**: We summarized the current state of the AdvXAI field in the following survey paper (work in progress)



> H. Baniecki, P. Biecek.  **Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey**. *IJCAI Workshop on XAI*, 2023.



### Abstract





  

    

  

 



> Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions. However, recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods, putting their security and trustworthiness into question. The possibility of manipulating, fooling or fairwashing evidence of the model's reasoning has detrimental consequences when applied in high-stakes decision-making and knowledge discovery. This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models, as well as fairness metrics. We introduce a unified notation and taxonomy of methods facilitating a common ground for researchers and practitioners from the intersecting research fields of AdvML and XAI. We discuss how to defend against attacks and design robust interpretation methods. We contribute a list of existing insecurities in XAI and outline the emerging research directions in adversarial XAI (AdvXAI). Future work should address improving explanation methods and evaluation protocols to take into account the reported safety issues.



### Citation



```

@article{baniecki2024adversarial,

  author  = {Hubert Baniecki and Przemyslaw Biecek},

  title   = {Adversarial attacks and defenses in 

             explainable artificial intelligence: A survey},

  journal = {Information Fusion},

  volume  = {107},

  pages   = {102303},

  year    = {2024}

}

```



## Related surveys



* [Explainable AI Methods - A Brief Overview](https://doi.org/10.1007/978-3-031-04083-2_2)

  

   A. Holzinger et al.  

      xxAI - Beyond Explainable AI, 2020 

    Explainable Artificial Intelligence (xAI) is an established field with a vibrant community that has developed a variety of very successful approaches to explain and interpret predictions of complex machine learning models such as deep neural networks. In this article, we briefly introduce a few selected methods and discuss them in a short, clear and concise way. The goal of this article is to give beginners, especially application engineers and data scientists, a quick overview of the state of the art in this current topic. The following 17 methods are covered in this chapter: LIME, Anchors, GraphLIME, LRP, DTD, PDA, TCAV, XGNN, SHAP, ASV, Break-Down, Shapley Flow, Textual Explanations of Visual Models, Integrated Gradients, Causal Models, Meaningful Perturbations, and X-NeSyL.

  

* [Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain](https://doi.org/10.1145/3453158)

  

   I. Rosenberg et al.  

      ACM Computing Surveys, 2021 

    In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This article comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker’ s goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. To the best of our knowledge, this work is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.

  

* [Adversarial Attacks and Defenses: An Interpretation Perspective](https://dl.acm.org/doi/10.1145/3468507.3468519)

  

   N. Liu et al. 

      ACM SIGKDD Explorations Newsletter, 2021 

    Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. Techniques to protect models against adversarial input are called adversarial defense methods. Although many approaches have been proposed to study adversarial attacks and defenses in different scenarios, an intriguing and crucial challenge remains that how to really understand model vulnerability? Inspired by the saying that "if you know yourself and your enemy, you need not fear the battles", we may tackle the challenge above after interpreting machine learning models to open the black-boxes. The goal of model interpretation, or interpretable machine learning, is to extract human-understandable terms for the working mechanism of models. Recently, some approaches start incorporating interpretation into the exploration of adversarial attacks and defenses. Meanwhile, we also observe that many existing methods of adversarial attacks and defenses, although not explicitly claimed, can be understood from the perspective of interpretation. In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation. We categorize interpretation into two types, feature-level interpretation, and model-level interpretation. For each type of interpretation, we elaborate on how it could be used for adversarial attacks and defenses. We then briefly illustrate additional correlations between interpretation and adversaries. Finally, we discuss the challenges and future directions for tackling adversary issues with interpretation.

  

* [A Survey on the Robustness of Feature Importance and Counterfactual Explanations](https://arxiv.org/abs/2111.00358)

  

   S. Mishra et al.  

      Workshop on Explainable AI in Finance (ICAIF XAI), 2021 

    There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models. Arguably, the most popular among them are local explanations that focus on investigating model behaviour for individual instances. Several methods have been proposed for local analysis, but relatively lesser effort has gone into understanding if the explanations are robust and accurately reflect the behaviour of underlying models. In this work, we present a survey of the works that analysed the robustness of two classes of local explanations (feature importance and counterfactual explanations) that are popularly used in analysing AI/ML models in finance. The survey aims to unify existing definitions of robustness, introduces a taxonomy to classify different robustness approaches, and discusses some interesting results. Finally, the survey introduces some pointers about extending current robustness analysis approaches so as to identify reliable explainability methods.

  

* [Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective](https://doi.org/10.1145/3485133)

  

   G. R. Machado et al.  

      ACM Computing Surveys, 2022 

    Deep Learning algorithms have achieved state-of-the-art performance for Image Classification. For this reason, they have been used even in security-critical applications, such as biometric recognition systems and self-driving cars. However, recent works have shown those algorithms, which can even surpass human capabilities, are vulnerable to adversarial examples. In Computer Vision, adversarial examples are images containing subtle perturbations generated by malicious optimization algorithms to fool classifiers. As an attempt to mitigate these vulnerabilities, numerous countermeasures have been proposed recently in the literature. However, devising an efficient defense mechanism has proven to be a difficult task, since many approaches demonstrated to be ineffective against adaptive attackers. Thus, this article aims to provide all readerships with a review of the latest research progress on Adversarial Machine Learning in Image Classification, nevertheless, with a defender’s perspective. This article introduces novel taxonomies for categorizing adversarial attacks and defenses, as well as discuss possible reasons regarding the existence of adversarial examples. In addition, relevant guidance is also provided to assist researchers when devising and evaluating defenses. Finally, based on the reviewed literature, this article suggests some promising paths for future research.

  

* [A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts](https://doi.org/10.1007/s10618-022-00867-8)

  

   G. Schwalbe & B. Finzel.

      Data Mining and Knowledge Discovery, 2023 

    In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intelligence (XAI). With the amount of XAI methods vastly growing, a taxonomy of methods is needed by researchers as well as practitioners: To grasp the breadth of the topic, compare methods, and to select the right XAI method based on traits required by a specific use-case context. Many taxonomies for XAI methods of varying level of detail and depth can be found in the literature. While they often have a different focus, they also exhibit many points of overlap. This paper unifies these efforts and provides a complete taxonomy of XAI methods with respect to notions present in the current state of research. In a structured literature analysis and meta-study, we identified and reviewed more than 50 of the most cited and current surveys on XAI methods, metrics, and method traits. After summarizing them in a survey of surveys, we merge terminologies and concepts of the articles into a unified structured taxonomy. Single concepts therein are illustrated by more than 50 diverse selected example methods in total, which we categorize accordingly. The taxonomy may serve both beginners, researchers, and practitioners as a reference and wide-ranging overview of XAI method traits and aspects. Hence, it provides foundations for targeted, use-case-oriented, and context-sensitive future research.

  

* [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://doi.org/10.1145/3583558)

  

   M. Nauta et al.  

      ACM Computing Surveys, 2023 

    The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method. We find that 1 in 3 papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate with users. This survey also contributes to the call for objective, quantifiable evaluation methods by presenting an extensive overview of quantitative XAI evaluation methods. Our systematic collection of evaluation methods provides researchers and practitioners with concrete tools to thoroughly validate, benchmark and compare new and existing XAI methods. The Co-12 categorization scheme and our identified evaluation methods open up opportunities to include quantitative metrics as optimization criteria during model training in order to optimize for accuracy and interpretability simultaneously.

  

* [SoK: Explainable Machine Learning in Adversarial Environments](https://www.computer.org/csdl/proceedings-article/sp/2024/313000a021/1RjE9XVNjnW)

  

   M. Noppel & C. Wressnegger.

      IEEE Symposium on Security and Privacy (S&P), 2024 

    Modern deep learning methods have long been considered black boxes due to the lack of insights into their decision-making process. However, recent advances in explainable machine learning have turned the tables. Post-hoc explanation methods enable precise relevance attribution of input features for otherwise opaque models such as deep neural networks. This progression has raised expectations that these techniques can uncover attacks against learning-based systems such as adversarial examples or neural backdoors. Unfortunately, current methods are not robust against manipulations themselves. In this paper, we set out to systematize attacks against post-hoc explanation methods to lay the groundwork for developing more robust explainable machine learning. If explanation methods cannot be misled by an adversary, they can serve as an effective tool against attacks, marking a turning point in adversarial machine learning. We present a hierarchy of explanation-aware robustness notions and relate existing defenses to it. In doing so, we uncover synergies, research gaps, and future directions toward more reliable explanations robust against manipulations.

  



## Background (2018)



* [Towards better understanding of gradient-based attribution methods for Deep Neural Networks](https://openreview.net/forum?id=Sy21R9JAW)

  

   M. Ancona et al.

      International Conference on Learning Representations (ICLR), 2018 

    Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them. By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation. Finally, we propose a novel evaluation metric, called Sensitivity-n and test the gradient-based attribution methods alongside with a simple perturbation-based attribution method on several datasets in the domains of image and text classification, using various network architectures.

  

* [Towards Robust Interpretability with Self-Explaining Neural Networks](https://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining-neural-networks#:~:text=paper)

  

   D. Alvarez-Melis & T. Jaakkola. 

      Neural Information Processing Systems (NeurIPS), 2018 

    Most recent work on interpretability of complex machine learning models has focused on estimating a-posteriori explanations for previously trained models around specific predictions. Self-explaining models where interpretability plays a key role already during learning have received much less attention. We propose three desiderata for explanations in general -- explicitness, faithfulness, and stability -- and show that existing methods do not satisfy them. In response, we design self-explaining models in stages, progressively generalizing linear classifiers to complex yet architecturally explicit models. Faithfulness and stability are enforced via regularization specifically tailored to such models. Experimental results across various benchmark datasets show that our framework offers a promising direction for reconciling model complexity and interpretability.

  

* [Sanity Checks for Saliency Maps](https://papers.nips.cc/paper/8160-sanity-checks-for-saliency-maps#:~:text=pdf#:~:text=paper)

  

   J. Adebayo et al. 

      Neural Information Processing Systems (NeurIPS), 2018 

    Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We find that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, finding outliers in the data, explaining the relationship between inputs and outputs that the model learned, and debugging the model. We interpret our findings through an analogy with edge detection in images, a technique that requires neither training data nor model. Theory in the case of a linear model and a single-layer convolutional neural network supports our experimental findings.

  



## Adversarial attacks on model explanations



* [Interpretation of Neural Networks Is Fragile](https://doi.org/10.1609/aaai.v33i01.33013681)

  

   A. Ghorbani et al. 

      AAAI Conference on Artificial Intelligence (AAAI), 2019 

    In order for machine learning to be trusted in many applications, it is critical to be able to reliably explain why the machine learning algorithm makes certain predictions. For this reason, a variety of methods have been developed recently to interpret neural network predictions by providing, for example, feature importance maps. For both scientific robustness and security reasons, it is important to know to what extent can the interpretations be altered by small systematic perturbations to the input data, which might be generated by adversaries or by measurement biases. In this paper, we demonstrate how to generate adversarial perturbations that produce perceptively indistinguishable inputs that are assigned the same predicted label, yet have very different interpretations. We systematically characterize the robustness of interpretations generated by several widely-used feature importance interpretation methods (feature importance maps, integrated gradients, and DeepLIFT) on ImageNet and CIFAR-10. In all cases, our experiments show that systematic perturbations can lead to dramatically different interpretations without changing the label. We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly susceptible to adversarial attack. Our analysis of the geometry of the Hessian matrix gives insight on why robustness is a general challenge to current interpretation approaches.

  

* [The (Un)reliability of Saliency Methods](https://www.researchgate.net/publication/335707891_The_Unreliability_of_Saliency_Methods#:~:text=Public)

  

   P. J. Kindermans et al. 

      Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019 

    Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction. We use a simple and common pre-processing step which can be compensated for easily—adding a constant shift to the input data—to show that a transformation with no effect on how the model makes the decision can cause numerous methods to attribute incorrectly. In order to guarantee reliability, we believe that the explanation should not change when we can guarantee that two networks process the images in identical manners. We show, through several examples, that saliency methods that do not satisfy this requirement result in misleading attribution. The approach can be seen as a type of unit test; we construct a narrow ground truth to measure one stated desirable property. As such, we hope the community will embrace the development of additional tests.

  

* [How to Manipulate CNNs to Make Them Lie: the GradCAM Case](https://doi.org/10.48550/arXiv.1907.10901)

  

   T. Viering et al. 

      BMVC Workshop on Interpretable and Explainable Machine Vision (BMVC Workshop), 2019 

    Recently many methods have been introduced to explain CNN decisions. However, it has been shown that some methods can be sensitive to manipulation of the input. We continue this line of work and investigate the explanation method GradCAM. Instead of manipulating the input, we consider an adversary that manipulates the model itself to attack the explanation. By changing weights and architecture, we demonstrate that it is possible to generate any desired explanation, while leaving the model's accuracy essentially unchanged. This illustrates that GradCAM cannot explain the decision of every CNN and provides a proof of concept showing that it is possible to obfuscate the inner workings of a CNN. Finally, we combine input and model manipulation. To this end we put a backdoor in the network: the explanation is correct unless there is a specific pattern present in the input, which triggers a malicious explanation. Our work raises new security concerns, especially in settings where explanations of models may be used to make decisions, such as in the medical domain.

  

* [Fooling Network Interpretation in Image Classification](https://doi.org/10.48550/arXiv.1812.02843)

  

   A. Subramanya et al. 

      IEEE/CVF International Conference on Computer Vision (ICCV), 2019 

    Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely effective in causing misclassification. However, these patches are highlighted using standard network interpretation algorithms, thus revealing the identity of the adversary. We show that it is possible to create adversarial patches which not only fool the prediction, but also change what we interpret regarding the cause of the prediction. Moreover, we introduce our attack as a controlled setting to measure the accuracy of interpretation algorithms. We show this using extensive experiments for Grad-CAM interpretation that transfers to occluding patch interpretation as well. We believe our algorithms can facilitate developing more robust network interpretation tools that truly explain the network's underlying decision making process.

  

* [Fooling Neural Network Interpretations via Adversarial Model Manipulation](https://papers.nips.cc/paper/8558-fooling-neural-network-interpretations-via-adversarial-model-manipulation#:~:text=paper)

  

   J. Heo et al. 

      Neural Information Processing Systems (NeurIPS), 2019 

    We ask whether the neural network interpretation methods can be fooled via adversarial model manipulation, which is defined as a model fine-tuning step that aims to radically alter the explanations without hurting the accuracy of the original models, e.g., VGG19, ResNet50, and DenseNet121. By incorporating the interpretation results directly in the penalty term of the objective function for fine-tuning, we show that the state-of-the-art saliency map based interpreters, e.g., LRP, Grad-CAM, and SimpleGrad, can be easily fooled with our model manipulation. We propose two types of fooling, Passive and Active, and demonstrate such foolings generalize well to the entire validation set as well as transfer to other interpretation methods. Our results are validated by both visually showing the fooled explanations and reporting quantitative metrics that measure the deviations from the original explanations. We claim that the stability of neural network interpretation method with respect to our adversarial model manipulation is an important criterion to check for developing robust and reliable neural network interpretation method.

  

* [Explanations can be manipulated and geometry is to blame](https://papers.nips.cc/paper/9511-explanations-can-be-manipulated-and-geometry-is-to-blame)

  

   A. K. Dombrowski et al. 

      Neural Information Processing Systems (NeurIPS), 2019 

    Explanation methods aim to make neural networks more trustworthy and interpretable. In this paper, we demonstrate a property of explanation methods which is disconcerting for both of these purposes. Namely, we show that explanations can be manipulated arbitrarily by applying visually hardly perceptible perturbations to the input that keep the network's output approximately constant. We establish theoretically that this phenomenon can be related to certain geometrical properties of neural networks. This allows us to derive an upper bound on the susceptibility of explanations to manipulations. Based on this result, we propose effective mechanisms to enhance the robustness of explanations.

  

  


* [You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods](https://ecai2020.eu/papers/72_paper.pdf)

  

   B. Dimanov et al. 

      European Conference on Artificial Intelligence (ECAI), 2020 

    Transparency of algorithmic systems has been discussed as a way for end-users and regulators to develop appropriate trust in machine learning models. One popular approach, LIME [26], even suggests that model explanations can answer the question “Why should I trust you?” Here we show a straightforward method for modifying a pre-trained model to manipulate the output of many popular feature importance explanation methods with little change in accuracy, thus demonstrating the danger of trusting such explanation methods. We show how this explanation attack can mask a model’s discriminatory use of a sensitive feature, raising strong concerns about using such explanation methods to check model fairness.

  

* [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://dl.acm.org/doi/10.1145/3375627.3375830#:~:text=pdf)

  

   D. Slack et al. 

      AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020 

    As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.

  

    


* ["How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations](https://dl.acm.org/doi/10.1145/3375627.3375833#:~:text=pdf)

  

   H. Lakkaraju & O. Bastani. 

      AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020 

    As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a human interpretable manner. There has been recent concern that a high-fidelity explanation of a black box ML model may not accurately reflect the biases in the black box. As a consequence, explanations have the potential to mislead human users into trusting a problematic black box. In this work, we rigorously explore the notion of misleading explanations and how they influence user trust in black box models. Specifically, we propose a novel theoretical framework for understanding and generating misleading explanations, and carry out a user study with domain experts to demonstrate how these explanations can be used to mislead users. Our work is the first to empirically establish how user trust in black box models can be manipulated via misleading explanations.

  

* [Fairwashing Explanations with Off-Manifold Detergent](https://proceedings.mlr.press/v119/anders20a#:~:text=download%20pdf)

  

   C. J. Anders et al. 

      International Conference on Machine Learning (ICML), 2020 

    Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making process of the algorithm and thereby increase its acceptance by the end-users. In this paper, we show both theoretically and experimentally that these hopes are presently unfounded. Specifically, we show that, for any classifier g, one can always construct another classifier g' which has the same behavior on the data (same train, validation, and test error) but has arbitrarily manipulated explanation maps. We derive this statement theoretically using differential geometry and demonstrate it experimentally for various explanation methods, architectures, and datasets. Motivated by our theoretical insights, we then propose a modification of existing explanation methods which makes them significantly more robust.

  

* [Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security](https://ieeexplore.ieee.org/abstract/document/9206780)

  

   A. Kuppa & N. A. Le-Khac. 

      International Joint Conference on Neural Networks (IJCNN), 2020 

    Cybersecurity community is slowly leveraging Machine Learning (ML) to combat ever evolving threats. One of the biggest drivers for successful adoption of these models is how well domain experts and users are able to understand and trust their functionality. As these black-box models are being employed to make important predictions, the demand for transparency and explainability is increasing from the stakeholders. Explanations supporting the output of ML models are crucial in cyber security, where experts require far more information from the model than a simple binary output for their analysis. Recent approaches in the literature have focused on three different areas: (a) creating and improving explainability methods which help users better understand the internal workings of ML models and their outputs; (b) attacks on interpreters in white box setting; (c) defining the exact properties and metrics of the explanations generated by models. However, they have not covered, the security properties and threat models relevant to cybersecurity domain, and attacks on explainable models in black box settings. In this paper, we bridge this gap by proposing a taxonomy for Explainable Artificial Intelligence (XAI) methods, covering various security properties and threat models relevant to cyber security domain. We design a novel black box attack for analyzing the consistency, correctness and confidence security properties of gradient based XAI methods. We validate our proposed system on 3 security-relevant data-sets and models, and demonstrate that the method achieves attacker's goal of misleading both the classifier and explanation report and, only explainability method without affecting the classifier output. Our evaluation of the proposed approach shows promising results and can help in designing secure and robust XAI methods.

    

* [Interpretable Deep Learning under Fire](https://www.usenix.org/conference/usenixsecurity20/presentation/zhang-xinyang#:~:text=pdf)

  

   X. Zhang et al. 

      USENIX Security Symposium, 2020 

    Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. A plethora of interpretation models have been proposed to help users understand the inner workings of DNNs: how does a DNN arrive at a specific decision for a given input? The improved interpretability is believed to offer a sense of security by involving human in the decision-making process. Yet, due to its data-driven nature, the interpretability itself is potentially susceptible to malicious manipulations, about which little is known thus far. Here we bridge this gap by conducting the first systematic study on the security of interpretable deep learning systems (IDLSes). We show that existing IDLSes are highly vulnerable to adversarial manipulations. Specifically, we present ADV2, a new class of attacks that generate adversarial inputs not only misleading target DNNs but also deceiving their coupled interpretation models. Through empirical evaluation against four major types of IDLSes on benchmark datasets and in security-critical applications (e.g., skin cancer diagnosis), we demonstrate that with ADV2 the adversary is able to arbitrarily designate an input's prediction and interpretation. Further, with both analytical and empirical evidence, we identify the prediction-interpretation gap as one root cause of this vulnerability -- a DNN and its interpretation model are often misaligned, resulting in the possibility of exploiting both models simultaneously. Finally, we explore potential countermeasures against ADV2, including leveraging its low transferability and incorporating it in an adversarial training framework. Our findings shed light on designing and operating IDLSes in a more secure and informative fashion, leading to several promising research directions.

  

* [Remote explainability faces the bouncer problem](https://www.nature.com/articles/s42256-020-0216-z)

  

   E. Le Merrer & G. Tredan. 

      Nature Machine Intelligence, 2020 

    The concept of explainability is envisioned to satisfy society’s demands for transparency about machine learning decisions. The concept is simple: like humans, algorithms should explain the rationale behind their decisions so that their fairness can be assessed. Although this approach is promising in a local context (for example, the model creator explains it during debugging at the time of training), we argue that this reasoning cannot simply be transposed to a remote context, where a model trained by a service provider is only accessible to a user through a network and its application programming interface. This is problematic, as it constitutes precisely the target use case requiring transparency from a societal perspective. Through an analogy with a club bouncer (who may provide untruthful explanations upon customer rejection), we show that providing explanations cannot prevent a remote service from lying about the true reasons leading to its decisions. More precisely, we observe the impossibility of remote explainability for single explanations by constructing an attack on explanations that hides discriminatory features from the querying user. We provide an example implementation of this attack. We then show that the probability that an observer spots the attack, using several explanations for attempting to find incoherences, is low in practical settings. This undermines the very concept of remote explainability in general.

  

* [On the Privacy Risks of Model Explanations](https://www.comp.nus.edu.sg/~reza/files/Shokri-AIES2021.pdf)

  

   R. Shokri et al. 

      AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2021 

    Privacy and transparency are two key foundations of trustworthy machine learning. Model explanations offer insights into a model’s decisions on input data, whereas privacy is primarily concerned with protecting information about the training data. We analyze connections between model explanations and the leakage of sensitive information about the model’s training set. We investigate the privacy risks of feature-based model explanations using membership inference attacks: quantifying how much model predictions plus their explanations leak information about the presence of a datapoint in the training set of a model. We extensively evaluate membership inference attacks based on feature-based model explanations, over a variety of datasets. We show that backpropagation-based explanations can leak a significant amount of information about individual training datapoints. This is because they reveal statistical information about the decision boundaries of the model about an input, which can reveal its membership. We also empirically investigate the trade-off between privacy and explanation quality, by studying the perturbation-based model explanations.

  

* [Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing](https://arxiv.org/abs/2108.04990)

  

   S. Sinha et al. 

      Workshop on Analyzing and Interpreting Neural Networks for NLP (EMNLP BlackboxNLP), 2021 

    Interpretability methods like Integrated Gradient and LIME are popular choices for explaining natural language model predictions with relative word importance scores. These interpretations need to be robust for trustworthy NLP applications in high-stake areas like medicine or finance. Our paper demonstrates how interpretations can be manipulated by making simple word perturbations on an input text. Via a small portion of word-level swaps, these adversarial perturbations aim to make the resulting text semantically and spatially similar to its seed input (therefore sharing similar interpretations). Simultaneously, the generated examples achieve the same prediction label as the seed yet are given a substantially different explanation by the interpretation methods. Our experiments generate fragile interpretations to attack two SOTA interpretation methods, across three popular Transformer models and on two different NLP datasets. We observe that the rank order correlation drops by over 20% when less than 10% of words are perturbed on average. Further, rank-order correlation keeps decreasing as more words get perturbed. Furthermore, we demonstrate that candidates generated from our method have good quality metrics. Our code is available at: https://github.com/QData/TextAttack-Fragile-Interpretations.

  

* [Data Poisoning Attacks Against Outcome Interpretations of Predictive Models](https://doi.org/10.1145/3447548.3467405)

  

   H. Zhang et al. 

      ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021 

    The past decades have witnessed significant progress towards improving the accuracy of predictions powered by complex machine learning models. Despite much success, the lack of model interpretability prevents the usage of these techniques in life-critical systems such as medical diagnosis and self-driving systems. Recently, the interpretability issue has received much attention, and one critical task is to explain why a predictive model makes a specific decision. We refer to this task as outcome interpretation. Many outcome interpretation methods have been developed to produce human-understandable interpretations by utilizing intermediate results of the machine learning models, such as gradients and model parameters. Although the effectiveness of outcome interpretation approaches has been shown in a benign environment, their robustness against data poisoning attacks (i.e., attacks at the training phase) has not been studied. As the first work towards this direction, we aim to answer an important question: Can training-phase adversarial samples manipulate the outcome interpretation of target samples? To answer this question, we propose a data poisoning attack framework named IMF (Interpretation Manipulation Framework), which can manipulate the interpretations of target samples produced by representative outcome interpretation methods. Extensive evaluations verify the effectiveness and efficiency of the proposed attack strategies on two real-world datasets.

  

* [Counterfactual Explanations Can Be Manipulated](https://openreview.net/forum?id=iaO_IH7CnGJ)

  

   D. Slack et al. 

      Neural Information Processing Systems (NeurIPS), 2021 

    Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical applications (e.g law enforcement, financial lending), it becomes important to ensure that we clearly understand the vulnerabilties of these methods and find ways to address them. However, there is little understanding of the vulnerabilities and shortcomings of counterfactual explanations. In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated. More specifically, we show counterfactual explanations may converge to drastically different counterfactuals under a small perturbation indicating they are not robust. Leveraging this insight, we introduce a novel objective to train seemingly fair models where counterfactual explanations find much lower cost recourse under a slight perturbation. We describe how these models can unfairly provide low-cost recourse for specific subgroups in the data while appearing fair to auditors. We perform experiments on loan and violent crime prediction data sets where certain subgroups achieve up to 20x lower cost recourse under the perturbation. These results raise concerns regarding the dependability of current counterfactual explanation techniques, which we hope will inspire investigations in robust counterfactual explanations.

  

* [Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)](https://doi.org/10.1609/aaai.v36i11.21590)

  

   H. Baniecki & P. Biecek. 

      AAAI Conference on Artificial Intelligence (AAAI), 2022 

    We introduce a model-agnostic algorithm for manipulating SHapley Additive exPlanations (SHAP) with perturbation of tabular data. It is evaluated on predictive tasks from healthcare and financial domains to illustrate how crucial is the context of data distribution in interpreting machine learning models. Our method supports checking the stability of the explanations used by various stakeholders apparent in the domain of responsible AI; moreover, the result highlights the explanations’ vulnerability that can be exploited by an adversary.

  

* [Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability](https://arxiv.org/abs/2110.07120v2)

  

   D. Brown & H. Kvinge 

      Workshop on New Frontiers in Adversarial Machine Learning (ICML AdvML Frontiers), 2022 

    Methods for model explainability have become increasingly critical for testing the fairness and soundness of deep learning. Concept-based interpretability techniques, which use a small set of human-interpretable concept exemplars in order to measure the influence of a concept on a model's internal representation of input, are an important thread in this line of research. In this work we show that these explainability methods can suffer the same vulnerability to adversarial attacks as the models they are meant to analyze. We demonstrate this phenomenon on two well-known concept-based interpretability methods: TCAV and faceted feature visualization. We show that by carefully perturbing the examples of the concept that is being investigated, we can radically change the output of the interpretability method. The attacks that we propose can either induce positive interpretations (polka dots are an important concept for a model when classifying zebras) or negative interpretations (stripes are not an important factor in identifying images of a zebra). Our work highlights the fact that in safety-critical applications, there is need for security around not only the machine learning pipeline but also the model interpretation process.

  

* [Fooling Partial Dependence via Data Poisoning](https://doi.org/10.1007/978-3-031-26409-2_8)

  

   H. Baniecki et al. 

      European Conference on Machine Learning and PKDD (ECML PKDD), 2022 

    Many methods have been developed to understand complex predictive models and high expectations are placed on post-hoc model explainability. It turns out that such explanations are not robust nor trustworthy, and they can be fooled. This paper presents techniques for attacking Partial Dependence (plots, profiles, PDP), which are among the most popular methods of explaining any predictive model trained on tabular data. We showcase that PD can be manipulated in an adversarial manner, which is alarming, especially in financial or medical applications where auditability became a must-have trait supporting black-box models. The fooling is performed via poisoning the data to bend and shift explanations in the desired direction using genetic and gradient algorithms. To the best of our knowledge, this is the first work using a genetic algorithm for attacking explanations, which is highly transferable as it generalizes both ways: in a model-agnostic and an explanation-agnostic manner.

  

* [On the Privacy Risks of Algorithmic Recourse](https://proceedings.mlr.press/v206/pawelczyk23a)

  

   M. Pawelczyk et al.

      International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 

    As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected individuals, potential adversaries could also exploit these recourses to compromise privacy. In this work, we make the first attempt at investigating if and how an adversary can leverage recourses to infer private information about the underlying model’s training data. To this end, we propose a series of novel membership inference attacks which leverage algorithmic recourse. More specifically, we extend the prior literature on membership inference attacks to the recourse setting by leveraging the distances between data instances and their corresponding counterfactuals output by state-of-the-art recourse methods. Extensive experimentation with real world and synthetic datasets demonstrates significant privacy leakage through recourses. Our work establishes unintended privacy leakage as an important risk in the widespread adoption of recourse methods.

   

* [Fooling SHAP with Stealthily Biased Sampling](https://openreview.net/forum?id=J4mJjotSauh)

  

   G. Laberge et al.

      International Conference on Learning Representations (ICLR), 2023 

    SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations. However, existing attacks focus solely on altering the black-box model itself. In this paper, we propose a complementary family of attacks that leave the model intact and manipulate SHAP explanations using stealthily biased sampling of the data points used to approximate expectations w.r.t the background distribution. In the context of fairness audit, we show that our attack can reduce the importance of a sensitive feature when explaining the difference in outcomes between groups while remaining undetected. These results highlight the manipulability of SHAP explanations and encourage auditors to treat them with skepticism.

   

* [Disguising Attacks with Explanation-Aware Backdoors](https://www.computer.org/csdl/proceedings-article/sp/2023/933600a996/1Js0DygrC36)

  

   M. Noppel et al. 

      IEEE Symposium on Security and Privacy (S&P), 2023 

    Explainable machine learning holds great potential for analyzing and understanding learning-based systems. These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealthy adversaries. In this paper, we demonstrate how to fully disguise the adversarial operation of a machine learning model. Similar to neural backdoors, we modify the model’s prediction upon trigger presence but simultaneously fool an explanation method that is applied post-hoc for analysis. This enables an adversary to hide the presence of the trigger or point the explanation to entirely different portions of the input, throwing a red herring. We analyze different manifestations of these explanation-aware backdoors for gradient- and propagation-based explanation methods in the image domain, before we resume to conduct a red-herring attack against malware classification.

  

* [Foiling Explanations in Deep Neural Networks](https://openreview.net/forum?id=wvLQMHtyLk)

  

   S. V. Tamam et al. 

      Transactions on Machine Learning Research, 2023 

    Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade. Yet despite exhibiting superb performance over many problems, their black-box nature still poses a significant challenge with respect to explainability. Indeed, explainable artificial intelligence (XAI) is crucial in several fields, wherein the answer alone---sans a reasoning of how said answer was derived---is of little value. This paper uncovers a troubling property of explanation methods for image-based DNNs: by making small visual changes to the input image---hardly influencing the network's output---we demonstrate how explanations may be arbitrarily manipulated through the use of evolution strategies. Our novel algorithm, AttaXAI, a model-and-data XAI-agnostic, adversarial attack on XAI algorithms, only requires access to the output logits of a classifier and to the explanation map; these weak assumptions render our approach highly useful where real-world models and data are concerned. We compare our method's performance on two benchmark datasets---CIFAR100 and ImageNet---using four different pretrained deep-learning models: VGG16-CIFAR100, VGG16-ImageNet, MobileNet-CIFAR100, and Inception-v3-ImageNet. We find that the XAI methods can be manipulated without the use of gradients or other model internals. Our novel algorithm is successfully able to manipulate an image in a manner imperceptible to the human eye, such that the XAI method outputs a specific explanation map. To our knowledge, this is the first such method in a black-box setting, and we believe it has significant value where explainability is desired, required, or legally mandatory.

  

* [Focus-Shifting Attack: An Adversarial Attack That Retains Saliency Map Information and Manipulates Model Explanations](https://doi.org/10.1109/TR.2023.3303923)

  

   Q. Huang et al. 

      IEEE Transactions on Reliability, 2023 

    With the increased use of deep learning in many fields, a question has been raised: "How much should we trust the results generated by deep learning models?" Thus, there has been much research into the interpretations of model results, in order to open the black box of deep learning. The focus is more on interpretation than prediction in some fields such as medicine. Adversarial attacks are the most direct threats to deep learning models. They can add undetectable perturbations to the data to make the models give incorrect results, and model explanations are also susceptible to attacks. This leads to a loss of trust in explanations provided by the models, limiting the application and commercial value of deep learning. This research proposes a targeted adversarial attack algorithm that manipulates the interpretation of the model. Unlike other adversarial attacks on model interpretation, focus-shifting attack (FS Attack) can preserve the numerical depth of the original saliency map without specifying a perturbation budget. Experiments have shown that the FS Attack has a higher degree of image similarity and misleading interpretation than other adversarial attacks, and the property of preserving the numerical depth of the original saliency map makes it more difficult to detect. This study uses several common explanation methods as experimental subjects to investigate how these explanations can be manipulated and evaluate the effectiveness of the attack under different conditions. Under a particular interpretation, the FS Attack has a highly successful attack rate of 94.6, which is a critical adversarial attack.

  

* [Interpretation Attacks and Defenses on Predictive Models Using Electronic Health Records](https://doi.org/10.1007/978-3-031-43418-1_27)

  

   F. Razmi et al. 

      European Conference on Machine Learning and PKDD (ECML PKDD), 2023 

    The emergence of complex deep neural networks made it crucial to employ interpretation methods for gaining insight into the rationale behind model predictions. However, recent studies have revealed attacks on these interpretations, which aim to deceive users and subvert the trustworthiness of the models. It is especially critical in medical systems, where interpretations are essential in explaining outcomes. This paper presents the first interpretation attack on predictive models using sequential electronic health records (EHRs). Prior attempts in image interpretation mainly utilized gradient-based methods, yet our research shows that our attack can attain significant success on EHR interpretations that do not rely on model gradients. We introduce metrics compatible with EHR data to evaluate the attack’s success. Moreover, our findings demonstrate that detection methods that have successfully identified conventional adversarial examples are ineffective against our attack. We then propose a defense method utilizing auto-encoders to de-noise the data and improve the interpretations’ robustness. Our results indicate that this de-noising method outperforms the widely used defense method, SmoothGrad, which is based on adding noise to the data. 

  

* [SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability](https://doi.org/10.48550/arXiv.2208.09418)

  

   W. Huang et al. 

      IEEE/CVF International Conference on Computer Vision (ICCV), 2023 

    Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI method. In this paper, we identify several challenges that the state-of-the-art is unable to cope with collectively: i) existing metrics are not comprehensive; ii) XAI techniques are highly heterogeneous; iii) misinterpretations are normally rare events. To tackle these challenges, we introduce two black-box evaluation methods, concerning the worst-case interpretation discrepancy and a probabilistic notion of how robust in general, respectively. Genetic Algorithm (GA) with bespoke fitness function is used to solve constrained optimisation for efficient worst-case evaluation. Subset Simulation (SS), dedicated to estimate rare event probabilities, is used for evaluating overall robustness. Experiments show that the accuracy, sensitivity, and efficiency of our methods outperform the state-of-the-arts. Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.

  

* [Attribution-based Explanations that Provide Recourse Cannot be Robust](https://jmlr.org/papers/v24/23-0042.html)

  

   H. Fokkema et al. 

      Journal of Machine Learning Research, 2023 

    Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get actionable options for recourse, which allow an affected user to change the decision f(x) of a machine learning system by making limited changes to its input x. We formalize this by providing a general definition of recourse sensitivity, which needs to be instantiated with a utility function that describes which changes to the decisions are relevant to the user. This definition applies to local attribution methods, which attribute an importance weight to each input feature. It is often argued that such local attributions should be robust, in the sense that a small change in the input x that is being explained, should not cause a large change in the feature weights. However, we prove formally that it is in general impossible for any single attribution method to be both recourse sensitive and robust at the same time. It follows that there must always exist counterexamples to at least one of these properties. We provide such counterexamples for several popular attribution methods, including LIME, SHAP, Integrated Gradients and SmoothGrad. Our results also cover counterfactual explanations, which may be viewed as attributions that describe a perturbation of x. We further discuss possible ways to work around our impossibility result, for instance by allowing the output to consist of sets with multiple attributions, and we provide sufficient conditions for specific classes of continuous functions to be recourse sensitive. Finally, we strengthen our impossibility result for the restricted case where users are only able to change a single attribute of x, by providing an exact characterization of the functions f to which impossibility applies.

  

* [Don't trust your eyes: on the (un)reliability of feature visualizations](https://openreview.net/forum?id=OZWHYyfPwY)

  

   R. Geirho et al. 

      International Conference on Machine Learning (ICML), 2024 

    How do neural networks extract patterns from pixels? Feature visualizations attempt to answer this important question by visualizing highly activating patterns through optimization. Today, visualization methods form the foundation of our knowledge about the internal workings of neural networks, as a type of mechanistic interpretability. Here we ask: How reliable are feature visualizations? We start our investigation by developing network circuits that trick feature visualizations into showing arbitrary patterns that are completely disconnected from normal network behavior on natural input. We then provide evidence for a similar phenomenon occurring in standard, unmanipulated networks: feature visualizations are processed very differently from standard input, casting doubt on their ability to "explain" how neural networks process natural images. This can be used as a sanity check for feature visualizations. We underpin our empirical findings by theory proving that the set of functions that can be reliably understood by feature visualization is extremely small and does not include general black-box neural networks. Therefore, a promising way forward could be the development of networks that enforce certain structures in order to ensure more reliable feature visualizations.

  

* [From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation](https://excv-workshop.github.io/publication/from-flexibility-to-manipulation-the-slippery-slope-of-xai-evaluation/paper.pdf)

  

   K. WickstrĂžm et al. 

      Workshop on Explainable Computer Vision (ECCV eXCV), 2024 

    The lack of ground truth explanation labels is a fundamental challenge for quantitative evaluation in explainable artificial intelligence (XAI). This challenge becomes especially problematic when evaluation methods have numerous hyperparameters that must be specified by the user, as there is no ground truth to determine an optimal hyperparameter selection. It is typically not feasible to do an exhaustive search of hyperparameters so researchers typically make a normative choice based on similar studies in the literature, which provides great flexibility for the user. In this work, we illustrate how this flexibility can be exploited to manipulate the evaluation outcome. We frame this manipulation as an adversarial attack on the evaluation where seemingly innocent changes in hyperparameter setting significantly influence the evaluation outcome. We demonstrate the effectiveness of our manipulation across several datasets with large changes in evaluation outcomes across several explanation methods and models. Lastly, we propose a mitigation strategy based on ranking across hyperparameters that aims to provide robustness towards such manipulation. This work highlights the difficulty of conducting reliable XAI evaluation and emphasizes the importance of a holistic and transparent approach to evaluation in XAI. Code is available at https://github.com/Wickstrom/quantitative-xai-manipulation.

  



## Defense against the attacks on explanations



* [Adversarial explanations for understanding image classification decisions and improved NN robustness](https://arxiv.org/abs/1906.02896#:~:text=pdf)

  

   W. Woods et al. 

      Nature Machine Intelligence, 2019 

    For sensitive problems, such as medical imaging or fraud detection, Neural Network (NN) adoption has been slow due to concerns about their reliability, leading to a number of algorithms for explaining their decisions. NNs have also been found vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. Here we demonstrate both that these attacks can invalidate prior attempts to explain the decisions of NNs, and that with very robust networks, the attacks themselves may be leveraged as explanations with greater fidelity to the model. We show that the introduction of a novel regularization technique inspired by the Lipschitz constraint, alongside other proposed improvements, greatly improves an NN's resistance to adversarial examples. On the ImageNet classification task, we demonstrate a network with an Accuracy-Robustness Area (ARA) of 0.0053, an ARA 2.4x greater than the previous state of the art. Improving the mechanisms by which NN decisions are understood is an important direction for both establishing trust in sensitive domains and learning more about the stimuli to which NNs respond.

  

* [Robust Attribution Regularization](https://papers.nips.cc/paper/2019/hash/172ef5a94b4dd0aa120c6878fc29f70c-Abstract.html#:~:text=paper)

  

   J. Chen et al. 

      Neural Information Processing Systems (NeurIPS), 2019 

    An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions. We take a step towards solving this problem through the lens of axiomatic attribution of neural networks. Our theory is grounded in the recent work, Integrated Gradients (IG) [STY17], in axiomatically attributing a neural network’s output change to its input change. We propose training objectives in classic robust optimization models to achieve robust IG attributions. Our objectives give principled generalizations of previous objectives designed for robust predictions, and they naturally degenerate to classic soft-margin training for one-layer neural networks. We also generalize previous theory and prove that the objectives for different robust optimization models are closely related. Experiments demonstrate the effectiveness of our method, and also point to intriguing problems which hint at the need for better optimization techniques or better neural network architectures for robust attribution training.

  

* [A simple defense against adversarial attacks on heatmap explanations](https://arxiv.org/abs/2007.06381#:~:text=pdf)

  

   L. Rieger & L. K. Hansen. 

      Workshop on Human Interpretability in Machine Learning (ICML WHI), 2020 

    With machine learning models being used for more sensitive applications, we rely on interpretability methods to prove that no discriminating attributes were used for classification. A potential concern is the so-called "fair-washing" - manipulating a model such that the features used in reality are hidden and more innocuous features are shown to be important instead. In our work we present an effective defence against such adversarial attacks on neural networks. By a simple aggregation of multiple explanation methods, the network becomes robust against manipulation. This holds even when the attacker has exact knowledge of the model weights and the explanation methods used.

  

  


* [Proper Network Interpretability Helps Adversarial Robustness in Classification](https://proceedings.mlr.press/v119/boopathy20a#:~:text=download%20pdf)

  

   A. Boopathy et al. 

      International Conference on Machine Learning (ICML), 2020 

    Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks. In this paper, we theoretically show that with a proper measurement of interpretation, it is actually difficult to prevent prediction-evasion adversarial attacks from causing interpretation discrepancy, as confirmed by experiments on MNIST, CIFAR-10 and Restricted ImageNet. Spurred by that, we develop an interpretability-aware defensive scheme built only on promoting robust interpretation (without the need for resorting to adversarial loss minimization). We show that our defense achieves both robust classification and robust interpretation, outperforming state-of-theart adversarial training methods against attacks of large perturbation in particular.

  

* [Robust and Stable Black Box Explanations](https://proceedings.mlr.press/v119/lakkaraju20a#:~:text=download%20pdf)

  

   H. Lakkaraju et al. 

      International Conference on Machine Learning (ICML), 2020 

    As machine learning black boxes are increasingly being deployed in real-world applications, there has been a growing interest in developing post hoc explanations that summarize the behaviors of these black boxes. However, existing algorithms for generating such explanations have been shown to lack stability and robustness to distribution shifts. We propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax objective that aims to construct the highest fidelity explanation with respect to the worst-case over a set of adversarial perturbations. We instantiate this algorithm for explanations in the form of linear models and decision sets by devising the required optimization procedures. To the best of our knowledge, this work makes the first attempt at generating post hoc explanations that are robust to a general class of adversarial perturbations that are of practical interest. Experimental evaluation with real-world and synthetic datasets demonstrates that our approach substantially improves robustness of explanations without sacrificing their fidelity on the original data distribution.

  

* [Smoothed Geometry for Robust Attribution](https://proceedings.neurips.cc/paper/2020/hash/9d94c8981a48d12adfeecfe1ae6e0ec1-Abstract.html#:~:text=paper)

  

   Z. Wang et al. 

      Neural Information Processing Systems (NeurIPS), 2020 

    Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent explanations for nearby inputs. This lack of robustness is especially problematic in high-stakes applications where adversarially-manipulated explanations could impair safety and trustworthiness. Building on a geometric understanding of these attacks presented in recent work, we identify Lipschitz continuity conditions on models' gradient that lead to robust gradient-based attributions, and observe that smoothness may also be related to the ability of an attack to transfer across multiple attribution methods. To mitigate these attacks in practice, we propose an inexpensive regularization method that promotes these conditions in DNNs, as well as a stochastic smoothing technique that does not require re-training. Our experiments on a range of image models demonstrate that both of these mitigations consistently improve attribution robustness, and confirm the role that smooth geometry plays in these attacks on real, large-scale models.

  

* [On Guaranteed Optimal Robust Explanations for NLP Models](https://www.ijcai.org/proceedings/2021/366)

  

   E. La Malfa et al. 

      International Joint Conference on Artificial Intelligence (IJCAI), 2021

    We build on abduction-based explanations for machine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP). Our explanations comprise a subset of the words of the input text that satisfies two key features: optimality w.r.t. a user-defined cost function, such as the length of explanation, and robustness, in that they ensure prediction invariance for any bounded perturbation in the embedding space of the left-out words. We present two solution algorithms, respectively based on implicit hitting sets and maximum universal subsets, introducing a number of algorithmic improvements to speed up convergence of hard instances. We show how our method can be configured with different perturbation sets in the embedded space and used to detect bias in predictions by enforcing include/exclude constraints on biased terms, as well as to enhance existing heuristic-based NLP explanation frameworks such as Anchors. We evaluate our framework on three widely used sentiment analysis tasks and texts of up to 100 words from SST, Twitter and IMDB datasets, demonstrating the effectiveness of the derived explanations.

  

* [On Locality of Local Explanation Models](https://openreview.net/forum?id=6OkPFFMgBt)

  

   S. Ghalebikesabi al. 

      Neural Information Processing Systems (NeurIPS), 2021 

    Shapley values provide model agnostic feature attributions for model outcome at a particular instance by simulating feature absence under a global population distribution. The use of a global population can lead to potentially misleading results when local model behaviour is of interest. Hence we consider the formulation of  neighbourhood reference distributions that improve the local interpretability of Shapley values. By doing so, we find that the Nadaraya-Watson estimator, a well-studied kernel regressor, can be expressed as a self-normalised importance sampling estimator. Empirically, we observe that Neighbourhood Shapley values identify meaningful sparse  feature relevance attributions that provide insight into local model behaviour, complimenting conventional Shapley analysis. They also increase on-manifold explainability and robustness to the construction of adversarial classifiers. 

  

* [Towards robust explanations for deep neural networks](https://doi.org/10.1016/j.patcog.2021.108194)

  

   A. K. Dombrowski et al. 

      Pattern Recognition, 2022 

    Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible to manipulations. With this work, we aim to enhance the resilience of explanations. We develop a unified theoretical framework for deriving bounds on the maximal manipulability of a model. Based on these theoretical insights, we present three different techniques to boost robustness against manipulation: training with weight decay, smoothing activation functions, and minimizing the Hessian of the network. Our experimental results confirm the effectiveness of these approaches.

  

* [Deceptive AI Explanations: Creation and Detection](https://arxiv.org/abs/2001.07641v3)

  

   J. Schneider et al.

      International Conference on Agents and Artificial Intelligence (ICAART), 2022 

    Artificial intelligence (AI) comes with great opportunities but can also pose significant risks. Automatically generated explanations for decisions can increase transparency and foster trust, especially for systems based on automated predictions by AI models. However, given, e.g., economic incentives to create dishonest AI, to what extent can we trust explanations? To address this issue, our work investigates how AI models (i.e., deep learning, and existing instruments to increase transparency regarding AI decisions) can be used to create and detect deceptive explanations. As an empirical evaluation, we focus on text classification and alter the explanations generated by GradCAM, a well-established explanation technique in neural networks. Then, we evaluate the effect of deceptive explanations on users in an experiment with 200 participants. Our findings confirm that deceptive explanations can indeed fool humans. However, one can deploy machine learning (ML) methods to detect seemingly minor deception attempts with accuracy exceeding 80% given sufficient domain knowledge. Without domain knowledge, one can still infer inconsistencies in the explanations in an unsupervised manner, given basic knowledge of the predictive model under scrutiny.

  

* [Defense Against Explanation Manipulation](https://doi.org/10.3389/fdata.2022.704203)

  

   R. Tang et al. 

      Frontiers in Big Data, 2022 

    Explainable machine learning attracts increasing attention as it improves the transparency of models, which is helpful for machine learning to be trusted in real applications. However, explanation methods have recently been demonstrated to be vulnerable to manipulation, where we can easily change a model's explanation while keeping its prediction constant. To tackle this problem, some efforts have been paid to use more stable explanation methods or to change model configurations. In this work, we tackle the problem from the training perspective, and propose a new training scheme called Adversarial Training on EXplanations (ATEX) to improve the internal explanation stability of a model regardless of the specific explanation method being applied. Instead of directly specifying explanation values over data instances, ATEX only puts constraints on model predictions which avoids involving second-order derivatives in optimization. As a further discussion, we also find that explanation stability is closely related to another property of the model, i.e., the risk of being exposed to adversarial attack. Through experiments, besides showing that ATEX improves model robustness against manipulation targeting explanation, it also brings additional benefits including smoothing explanations and improving the efficacy of adversarial training if applied to the model.

  

* [Constraint-Driven Explanations for Black-Box ML Models](https://doi.org/10.1609/aaai.v36i8.20805)

  

   A. A. Shrotri et al.

      AAAI Conference on Artificial Intelligence (AAAI), 2022 

    The need to understand the inner workings of opaque Machine Learning models has prompted researchers to devise various types of post-hoc explanations. A large class of such explainers proceed in two phases: first perturb an input instance whose explanation is sought, and then generate an interpretable artifact to explain the prediction of the opaque model on that instance. Recently, Deutch and Frost proposed to use an additional input from the user: a set of constraints over the input space to guide the perturbation phase. While this approach affords the user the ability to tailor the explanation to their needs, striking a balance between flexibility, theoretical rigor and computational cost has remained an open challenge. We propose a novel constraint-driven explanation generation approach which simultaneously addresses these issues in a modular fashion. Our framework supports the use of expressive Boolean constraints giving the user more flexibility to specify the subspace to generate perturbations from. Leveraging advances in Formal Methods, we can theoretically guarantee strict adherence of the samples to the desired distribution. This also allows us to compute fidelity in a rigorous way, while scaling much better in practice. Our empirical study demonstrates concrete uses of our tool CLIME in obtaining more meaningful explanations with high fidelity.

  

* ["Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution](https://doi.org/10.1145/3548606.3559392)

  

   Y. Gan et al.

      ACM SIGSAC Conference on Computer and Communications Security (CCS), 2022 

    Neural networks have become increasingly popular. Nevertheless, understanding their decision process turns out to be complicated. One vital method to explain a models' behavior is feature attribution, i.e., attributing its decision to pivotal features. Although many algorithms are proposed, most of them aim to improve the faithfulness (fidelity) to the model. However, the real environment contains many random noises, which may cause the feature attribution maps to be greatly perturbed for similar images. More seriously, recent works show that explanation algorithms are vulnerable to adversarial attacks, generating the same explanation for a maliciously perturbed input. All of these make the explanation hard to trust in real scenarios, especially in security-critical applications. To bridge this gap, we propose Median Test for Feature Attribution (MeTFA) to quantify the uncertainty and increase the stability of explanation algorithms with theoretical guarantees. MeTFA is method-agnostic, i.e., it can be applied to any feature attribution method. MeTFA has the following two functions: (1) examine whether one feature is significantly important or unimportant and generate a MeTFA-significant map to visualize the results; (2) compute the confidence interval of a feature attribution score and generate a MeTFA-smoothed map to increase the stability of the explanation. Extensive experiments show that MeTFA improves the visual quality of explanations and significantly reduces the instability while maintaining the faithfulness of the original method. To quantitatively evaluate MeTFA's faithfulness and stability, we further propose several robust faithfulness metrics, which can evaluate the faithfulness of an explanation under different noise settings. Experiment results show that the MeTFA-smoothed explanation can significantly increase the robust faithfulness. In addition, we use two typical applications to show MeTFA's potential in the applications. First, when being applied to the SOTA explanation method to locate context bias for semantic segmentation models, MeTFA-significant explanations use far smaller regions to maintain 99%+ faithfulness. Second, when testing with different explanation-oriented attacks, MeTFA can help defend vanilla, as well as adaptive, adversarial attacks against explanations.

  

* [Preventing deception with explanation methods using focused sampling](https://doi.org/10.1007/s10618-022-00900-w)

  

   D. VreĆĄ & M. Robnik-Ć ikonja. 

      Data Mining and Knowledge Discovery, 2022 

    Machine learning models are used in many sensitive areas where, besides predictive accuracy, their comprehensibility is also essential. Interpretability of prediction models is necessary to determine their biases and causes of errors and is a prerequisite for users’ confidence. For complex state-of-the-art black-box models, post-hoc model-independent explanation techniques are an established solution. Popular and effective techniques, such as IME, LIME, and SHAP, use perturbation of instance features to explain individual predictions. Recently, (Slack et al. in Fooling LIME and SHAP: Adversarial attacks on post-hoc explanation methods, 2020) put their robustness into question by showing that their outcomes can be manipulated due to inadequate perturbation sampling employed. This weakness would allow owners of sensitive models to deceive inspection and hide potentially unethical or illegal biases existing in their predictive models. Such possibility could undermine public trust in machine learning models and give rise to legal restrictions on their use. We show that better sampling in these explanation methods prevents malicious manipulations. The proposed sampling uses data generators that learn the training set distribution and generate new perturbation instances much more similar to the training set. We show that the improved sampling increases the LIME and SHAP’s robustness, while the previously untested method IME is the most robust. Further ablation studies show how the enhanced sampling changes the quality of explanations, reveal differences between data generators, and analyze the effect of different level of conservatism in the employment of biased classifiers.

  

* [Certifiably robust interpretation via RĂ©nyi differential privacy](https://doi.org/10.1016/j.artint.2022.103787)

  

   A. Liu et al. 

      Artificial Intelligence, 2022 

    Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of interpretation robustness from a new perspective of RĂ©nyi differential privacy (RDP). The advantages of our RĂ©nyi-Robust-Smooth (RDP-based interpretation method) are three-folds. First, it can offer provable and certifiable top-k robustness. That is, the top-k important attributions of the interpretation map are provably robust under any input perturbation with bounded l_d-norm (for any d >= 1, including  d = inf). Second, our proposed method offers ∌12% better experimental robustness than existing approaches in terms of the top-k attributions. Remarkably, the accuracy of RĂ©nyi-Robust-Smooth also outperforms existing approaches. Third, our method can provide a smooth tradeoff between robustness and computational efficiency. Experimentally, its top-k attributions are twice more robust than existing approaches when the computational resources are highly constrained.

  

* [Unfooling Perturbation-Based Post Hoc Explainers](https://doi.org/10.1609/aaai.v37i6.25847)

  

   Z. Carmichael & W. J. Scheirer. 

      AAAI Conference on Artificial Intelligence (AAAI), 2023 

    Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary about the lack of transparency of its decision-making processes. Perturbation-based post hoc explainers offer a model agnostic means of interpreting these systems while only requiring query-level access. However, recent work demonstrates that these explainers can be fooled adversarially. This discovery has adverse implications for auditors, regulators, and other sentinels. With this in mind, several natural questions arise - how can we audit these black box systems? And how can we ascertain that the auditee is complying with the audit in good faith? In this work, we rigorously formalize this problem and devise a defense against adversarial attacks on perturbation-based explainers. We propose algorithms for the detection (CAD-Detect) and defense (CAD-Defend) of these attacks, which are aided by our novel conditional anomaly detection approach, KNN-CAD. We demonstrate that our approach successfully detects whether a black box system adversarially conceals its decision-making process and mitigates the adversarial attack on real-world data for the prevalent explainers, LIME and SHAP.

  

    


* [Towards More Robust Interpretation via Local Gradient Alignment](https://doi.org/10.1609/aaai.v37i7.25986)

  

   S. Joo et al.

      AAAI Conference on Artificial Intelligence (AAAI), 2023 

    Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining robust feature attributions. However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods. In this paper, we provide new insights by taking such normalization into account. First, we show that for every non-negative homogeneous neural network, a naive ℓ2-robust criterion for gradients is not normalization invariant, which means that two functions with the same normalized gradient can have different values. Second, we formulate a normalization invariant cosine distance-based criterion and derive its upper bound, which gives insight for why simply minimizing the Hessian norm at the input, as has been done in previous work, is not sufficient for attaining robust feature attribution. Finally, we propose to combine both ℓ2 and cosine distance-based criteria as regularization terms to leverage the advantages of both in aligning the local gradient. As a result, we experimentally show that models trained with our method produce much more robust interpretations on CIFAR-10 and ImageNet-100 without significantly hurting the accuracy, compared to the recent baselines. To the best of our knowledge, this is the first work to verify the robustness of interpretation on a larger-scale dataset beyond CIFAR-10, thanks to the computational efficiency of our method.

  

* [On the robustness of sparse counterfactual explanations to adverse perturbations](https://doi.org/10.1016/j.artint.2022.103840)

  

   M. Virgolin & S. Fracaros. 

      Artificial Intelligence, 2023 

    Counterfactual explanations (CEs) are a powerful means for understanding how decisions made by algorithms can be changed. Researchers have proposed a number of desiderata that CEs should meet to be practically useful, such as requiring minimal effort to enact, or complying with causal models. In this paper, we consider the interplay between the desiderata of robustness (i.e., that enacting CEs remains feasible and cost-effective even if adverse events take place) and sparsity (i.e., that CEs require only a subset of the features to be changed). In particular, we study the effect of addressing robustness separately for the features that are recommended to be changed and those that are not. We provide definitions of robustness for sparse CEs that are workable in that they can be incorporated as penalty terms in the loss functions that are used for discovering CEs. To carry out our experiments, we create and release code where five data sets (commonly used in the field of fair and explainable machine learning) have been enriched with feature-specific annotations that can be used to sample meaningful perturbations. Our experiments show that CEs are often not robust and, if adverse perturbations take place (even if not worst-case), the intervention they prescribe may require a much larger cost than anticipated, or even become impossible. However, accounting for robustness in the search process, which can be done rather easily, allows discovering robust CEs systematically. Robust CEs make additional intervention to contrast perturbations much less costly than non-robust CEs. We also find that robustness is easier to achieve for the features to change, posing an important point of consideration for the choice of what counterfactual explanation is best for the user. Our code is available at: https://github.com/marcovirgolin/robust-counterfactuals.

  

* [Robust Explanation Constraints for Neural Networks](https://openreview.net/forum?id=_hHYaKu0jcj)

  

   M. R. Wicker et al.

      International Conference on Learning Representations (ICLR), 2023 

    Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular explanations methods have been found to be fragile to minor perturbations of input features or model parameters. Relying on constraint relaxation techniques from non-convex optimization, we develop a method that upper-bounds the largest change an adversary can make to a gradient-based explanation via bounded manipulation of either the input features or model parameters. By propagating a compact input or parameter set as symbolic intervals through the forwards and backwards computations of the neural network we can formally certify the robustness of gradient-based explanations. Our bounds are differentiable, hence we can incorporate provable explanation robustness into neural network training. Empirically, our method surpasses the robustness provided by previous heuristic approaches. We find that our training method is the only method able to learn neural networks with certificates of explanation robustness across all six datasets tested. 

   

* [Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse](https://openreview.net/forum?id=sC-PmTsiTB)

  

   M. Pawelczyk et al.

      International Conference on Learning Representations (ICLR), 2023 

    As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means for recourse. While several approaches have been proposed to construct recourses for affected individuals, the recourses output by these methods either achieve low costs (i.e., ease-of-implementation) or robustness to small perturbations (i.e., noisy implementations of recourses), but not both due to the inherent trade-offs between the recourse costs and robustness. Furthermore, prior approaches do not provide end users with any agency over navigating the aforementioned trade-offs. In this work, we address the above challenges by proposing the first algorithmic framework which enables users to effectively manage the recourse cost vs. robustness trade-offs. More specifically, our framework Probabilistically ROBust rEcourse (PROBE) lets users choose the probability with which a recourse could get invalidated (recourse invalidation rate) if small changes are made to the recourse i.e., the recourse is implemented somewhat noisily. To this end, we propose a novel objective function which simultaneously minimizes the gap between the achieved (resulting) and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction. We develop novel theoretical results to characterize the recourse invalidation rates corresponding to any given instance w.r.t. different classes of underlying models (e.g., linear models, tree based models etc.), and leverage these results to efficiently optimize the proposed objective. Experimental evaluation with multiple real world datasets demonstrate the efficacy of the proposed framework.

   

* [Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values](https://doi.org/10.1007/978-3-031-44064-9_8)

  

   K. Blesch et al. 

      World Conference on Explainable Artificial Intelligence (xAI), 2023 

    Shapley values have achieved great popularity in explainable artificial intelligence. However, with standard sampling methods, resulting feature attributions are susceptible to adversarial attacks. This originates from target function evaluations at extrapolated data points, which are easily detectable and hence, enable models to behave accordingly. In this paper, we introduce a novel strategy for increased robustness against adversarial attacks of both local and global explanations: Knockoff imputed Shapley values. Our approach builds on the model-X knockoff methodology, which generates synthetic data that preserves statistical properties of the original samples. This enables researchers to flexibly choose an appropriate model to generate on-manifold data for the calculation of Shapley values upfront, instead of having to estimate a large number of conditional densities or make strong parametric assumptions. Through real and simulated data experiments, we demonstrate the effectiveness of knockoff imputation against adversarial attacks.

  



### More towards robust and stable explanations



* [On the (In)fidelity and Sensitivity of Explanations](https://papers.nips.cc/paper/9278-on-the-infidelity-and-sensitivity-of-explanations#:~:text=paper)

  

   C. K. Yeh et al. 

      Neural Information Processing Systems (NeurIPS), 2019 

    We consider objective evaluation measures of saliency explanations for complex black-box machine learning models. We propose simple robust variants of two notions that have been considered in recent literature: (in)fidelity, and sensitivity. We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, the optimal explanation for infidelity is a novel combination of two popular explanation methods. By varying the perturbation distribution that defines infidelity, we obtain novel explanations by optimizing infidelity, which we show to out-perform existing explanations in both quantitative and qualitative measurements. Another salient question given these measures is how to modify any given explanation to have better values with respect to these measures. We propose a simple modification based on lowering sensitivity, and moreover show that when done appropriately, we could simultaneously improve both sensitivity as well as fidelity.

  

* [S-LIME: Stabilized-LIME for Model Explanation](https://doi.org/10.1145/3447548.3467274)

  

   Z. Zhou et al. 

      ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021

    An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing efforts for researchers to develop methods to interpret these black-box models. Post hoc explanations based on perturbations, such as LIME [39], are widely used approaches to interpret a machine learning model after it has been built. This class of methods has been shown to exhibit large instability, posing serious challenges to the effectiveness of the method itself and harming user trust. In this paper, we propose S-LIME, which utilizes a hypothesis testing framework based on central limit theorem for determining the number of perturbation points needed to guarantee stability of the resulting explanation. Experiments on both simulated and real world data sets are provided to demonstrate the effectiveness of our method.

  

* [BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations](https://proceedings.mlr.press/v161/zhao21a.html)

  

   X. Zhao et al. 

      Uncertainty in Artificial Intelligence (UAI), 2021

    Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI – wh