LioNets: Local Interpretation of Neural Networks Through Penultimate Layer Decoding

Mollas, Ioannis; Bassiliades, Nikolaos; Tsoumakas, Grigorios

doi:10.1007/978-3-030-43823-4_23

Cited by 12 publications

(9 citation statements)

References 34 publications

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“…An interesting implementation of Hunter’s argumentation model (Besnard & Hunter, 2001, 2009) can be found in Mollas et al . (2020). The authors use a feature importance technique, in order to extract untruthful parts in the explanation of a data-driven model.…”

Section: Argumentation and Machine Learning For Explainabilitymentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

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Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

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Section: Argumentation and Machine Learning For Explainabilitymentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

View full text Add to dashboard Cite

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“…Other model-specific outcome explanation techniques include the one proposed by Mollas et al [42], which uses unsupervised learning techniques and a similarity metric to explain individual outcomes of random forests. Another technique was proposed by Haufe et al [41], which transforms non-linear models in terms of multivariate classifiers into interpretable linear models.…”

Section: A Explaining How the Outcome Was Generatedmentioning

confidence: 99%

“…LionForest [42] Specific Explaining the decisions of random forests via unsupervised learning techniques and similarity metrics. TABLE 1: A summary of the XAI techniques covered in Section II-A, all of which are designed for outcome explanation.…”

Section: Introductionmentioning

confidence: 99%

Explainable Artificial Intelligence for Tabular Data: A Survey

2021

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Machine learning techniques are increasingly gaining attention due to their widespread use in various disciplines across academia and industry. Despite their tremendous success, many such techniques suffer from the "black-box" problem, which refers to situations where the data analyst is unable to explain why such techniques arrive at certain decisions. This problem has fuelled interest in Explainable Artificial Intelligence (XAI), which refers to techniques that can easily be interpreted by humans. Unfortunately, many of these techniques are not suitable for tabular data, which is surprising given the importance and widespread use of tabular data in critical applications such as finance, healthcare, and criminal justice. Also surprising is the fact that, despite the vast literature on XAI, there are still no survey articles to date that focus on tabular data. Consequently, despite the existing survey articles that cover a wide range of XAI techniques, it remains challenging for researchers working on tabular data to go through all of these surveys and extract the techniques that are suitable for their analysis. Our article fills this gap by providing a comprehensive and up-to-date survey of the XAI techniques that are relevant to tabular data. Furthermore, we categorize the references covered in our survey, indicating the type of the model being explained, the approach being used to provide the explanation, and the XAI problem being addressed. Our article is the first to provide researchers with a map that helps them navigate the XAI literature in the context of tabular data. INDEX TERMSBlack-box models, Explainable Artificial Intelligence, Machine Learning, Model interpretability VOLUME 4, 2016This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3116481, IEEE Access M. Sahakyan et al.: Explainable Artificial Intelligence for Tabular Data: A Survey• Decomposability, reflecting the degree to which each input, parameter, and calculation can be explained intuitively;• Algorithmic transparency, reflecting the degree to which the inner workings of the learning algorithm can be understood.For example, rule-based models [16] are considered transparent since they use a series of if-then rules that can easily be understood without the need for any further explanation. Unlike transparent models, black-box models are those that do not explain their predictions in a way that humans can understand [17]. Examples of black-box models include artificial neural networks [18] and gradient boosting [19]. Although black-box models are hard to interpret by humans, they tend to have higher prediction accuracy compared to their transparent counterparts. This trade-off between accuracy and transparency gives raise to the black-box explanation problem, which involves explaining the rationale behind the decisions made by black-box models. By providing such explanations, one can continue ...

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“…Afterwards, it allows the ranking of neurons and dimensions based on their overall saliency. Finally, lionets [29] looks at the penultimate layer of a DNN, which models texts in an alternative representation, randomly permutes the weights of nodes in that layer to generate new vectors, classifies them, observes the classification outcome and returns the explanation using a linear regressor like lime. Differently from these model-specific methods, xspells is not tied to a specific architecture and it can be used to explain any black box sentiment classifier.…”

Section: Related Workmentioning

confidence: 99%

Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars

2020

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We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.

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LioNets: Local Interpretation of Neural Networks Through Penultimate Layer Decoding

Cited by 12 publications

References 34 publications

Argumentation and explainable artificial intelligence: a survey

Argumentation and explainable artificial intelligence: a survey

Explainable Artificial Intelligence for Tabular Data: A Survey

Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars

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