DeepRED – Rule Extraction from Deep Neural Networks

Zilke, Jan Ruben; Mencía, Eneldo Loza; Janssen, Frederik

doi:10.1007/978-3-319-46307-0_29

Cited by 176 publications

(109 citation statements)

References 13 publications

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“…Rule-based learner: [82,83,147,148,251,252,253,254,255,256] Decision Tree: [21,56,79,81,97,135,257,258,259] Others: [80] Feature relevance explanation Importance/Contribution: [60,61,110,260,261] Sensitivity / Saliency: [260] [262] Local explanation Decision Tree / Sensitivity: [233] [263] Explanation by Example Activation clusters: [264,144] Text explanation Caption generation: [111] [150] Visual explanation Saliency / Weights: [265] Architecture modification Others: [264] [266] [267] Convolutional Neural Networks Explanation by simplification Decision Tree: [78] Feature relevance explanation Activations: [72,268] [46] Feature Extraction: [72,268] Visual explanation Filter / Activation: [63,136,137,...…”

Section: Explanation By Simplificationmentioning

confidence: 99%

“…Several model simplification techniques have been proposed for neural networks with one single hidden layer, however very few works have been presented for neural networks with multiple hidden layers. One of these few works is DeepRED algorithm [257], which extends the decompositional approach to rule extraction (splitting at neuron level) presented in [259] for multi-layer neural network by adding more decision trees and rules.…”

Section: Multi-layer Neural Networkmentioning

confidence: 99%

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

et al. 2020

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In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

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Section: Explanation By Simplificationmentioning

confidence: 99%

Section: Multi-layer Neural Networkmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

et al. 2020

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“…Efforts to decompose neural networks into decision trees have recently extended work from the 1990s, which focused on shallow networks, to generalizing the process for deep neural networks. One such method is DeepRED [21], which demonstrates a way of extending the CRED [22] algorithm (designed for shallow networks) to arbitrarily many hidden layers. DeepRED utilizes several strategies to simplify its decision trees: it uses RxREN [23] to prune unnecessary input, and it applies algorithm C4.5 [24], a statistical method for creating a parsimonious decision tree.…”

Section: A Explanations Of Deep Network Processingmentioning

confidence: 99%

Explaining Explanations: An Overview of Interpretability of Machine Learning

Gilpin

Bau

Yuan

et al. 2018

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

1,597

932

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There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we describe foundational concepts of explainability and show how they can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.

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“…Methods that combine elements of both approaches are called eclectic. Recent work can be found for both pedagogical (Augasta & Kathirvalavakumar, 2012) and decompositional (Zilke et al, 2016) approaches, with this last one extending a decompositional approach to deep networks.…”

Section: Interpreting Embedding Models: Related Workmentioning

confidence: 99%

Interpreting embedding models of knowledge bases.

Gusmão¹

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Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.

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DeepRED – Rule Extraction from Deep Neural Networks

Cited by 176 publications

References 13 publications

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Explaining Explanations: An Overview of Interpretability of Machine Learning

Interpreting embedding models of knowledge bases.

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