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Join the community, add a new evaluation result row, explainable artificial intelligence.
250 papers with code • 0 benchmarks • 8 datasets
XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
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Most implemented papers
Axiomatic attribution for deep networks.
We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works.
GNNExplainer: Generating Explanations for Graph Neural Networks
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
jphall663/kdd_2019 • 8 Jun 2019
Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models.
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood.
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
soerenab/AudioMNIST • 9 Jul 2018
Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions.
Entropy-based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains.
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective
josesousaribeiro/exirt • 18 Oct 2022
In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method to use to explain these models, with this choice being almost directly linked to the popularity of a specific method.
MEGAN: Multi-Explanation Graph Attention Network
Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances.
Do Not Trust Additive Explanations
ModelOriented/iBreakDown • 27 Mar 2019
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently.
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Explainable Artificial Intelligence can provide explanations for its decisions or predictions to human users. •. This paper offers a systematic literature review with different applications. •. This article is considered a roadmap for further research in the field.
In this paper, we provide a comprehensive review of XAI techniques, emphasizing their methodologies, strengths, and potential limitations. Furthermore, we present a case study employing six model-agnostic XAI techniques, offering a comparative analysis of their effectiveness in explaining a black-box model related to a healthcare scenario.
Abstract: Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions.
In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts.
Explainable artificial intelligence. 242 papers with code • 0 benchmarks • 8 datasets. XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans.
In our survey, we included 73 papers that covered a range of general and specific aspects of XAI with a focus on challenges and research directions in XAI reported in these included papers. There are various taxonomies proposed for XAI which mainly depend on the aspects discussed in the papers.