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Academic literature on the topic 'Post-hoc Explainability'
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Journal articles on the topic "Post-hoc Explainability"
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Fauvel, Kevin, Tao Lin, Véronique Masson, Élisa Fromont, and Alexandre Termier. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification." Mathematics 9, no. 23 (2021): 3137. http://dx.doi.org/10.3390/math9233137.
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Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both large and small datasets, while allowing the full exploitation of a faithful post hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.
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Mochaourab, Rami, Arun Venkitaraman, Isak Samsten, Panagiotis Papapetrou, and Cristian R. Rojas. "Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective." IEEE Signal Processing Magazine 39, no. 4 (2022): 119–29. http://dx.doi.org/10.1109/msp.2022.3155955.
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Lee, Gin Chong, and Chu Kiong Loo. "On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition." Sensors 22, no. 5 (2022): 1905. http://dx.doi.org/10.3390/s22051905.
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This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo State Network (ESN) input and reservoir weights, in the context of human action recognition (HAR). To ensure stability and echo state property in the reservoir, Recurrent Plots (RPs) and Recurrence Quantification Analysis (RQA) techniques are exploited for explainability and characterization of the reservoir dynamics and hence tuning ESN hyperparameters. The optimized self-organizing reservoirs are cascaded with a Convolutional Neural Network (CNN) to ensure that the activation of internal echo state representations (ESRs) echoes similar topological qualities and temporal features of the input time-series, and the CNN efficiently learns the dynamics and multiscale temporal features from the ESRs for action recognition. The hyperparameter optimization (HPO) algorithms are additionally adopted to optimize the CNN stage in SO-ConvESN. Experimental results on the HAR problem using several publicly available 3D-skeleton-based action datasets demonstrate the showcasing of the RPs and RQA technique in examining the explainability of reservoir dynamics for designing stable self-organizing reservoirs and the usefulness of implementing HPOs in SO-ConvESN for the HAR task. The proposed SO-ConvESN exhibits competitive recognition accuracy.
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Maree, Charl, and Christian Omlin. "Reinforcement Learning Your Way: Agent Characterization through Policy Regularization." AI 3, no. 2 (2022): 250–59. http://dx.doi.org/10.3390/ai3020015.
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The increased complexity of state-of-the-art reinforcement learning (RL) algorithms has resulted in an opacity that inhibits explainability and understanding. This has led to the development of several post hoc explainability methods that aim to extract information from learned policies, thus aiding explainability. These methods rely on empirical observations of the policy, and thus aim to generalize a characterization of agents’ behaviour. In this study, we have instead developed a method to imbue agents’ policies with a characteristic behaviour through regularization of their objective functions. Our method guides the agents’ behaviour during learning, which results in an intrinsic characterization; it connects the learning process with model explanation. We provide a formal argument and empirical evidence for the viability of our method. In future work, we intend to employ it to develop agents that optimize individual financial customers’ investment portfolios based on their spending personalities.
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Yan, Fei, Yunqing Chen, Yiwen Xia, Zhiliang Wang, and Ruoxiu Xiao. "An Explainable Brain Tumor Detection Framework for MRI Analysis." Applied Sciences 13, no. 6 (2023): 3438. http://dx.doi.org/10.3390/app13063438.
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Explainability in medical images analysis plays an important role in the accurate diagnosis and treatment of tumors, which can help medical professionals better understand the images analysis results based on deep models. This paper proposes an explainable brain tumor detection framework that can complete the tasks of segmentation, classification, and explainability. The re-parameterization method is applied to our classification network, and the effect of explainable heatmaps is improved by modifying the network architecture. Our classification model also has the advantage of post-hoc explainability. We used the BraTS-2018 dataset for training and verification. Experimental results show that our simplified framework has excellent performance and high calculation speed. The comparison of results by segmentation and explainable neural networks helps researchers better understand the process of the black box method, increase the trust of the deep model output, and make more accurate judgments in disease identification and diagnosis.
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Maarten Schraagen, Jan, Sabin Kerwien Lopez, Carolin Schneider, Vivien Schneider, Stephanie Tönjes, and Emma Wiechmann. "The Role of Transparency and Explainability in Automated Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 27–31. http://dx.doi.org/10.1177/1071181321651063.
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This study investigates the differences and effects of transparency and explainability on trust, situation awareness, and satisfaction in the context of an automated car. Three groups were compared in a between-subjects design (n = 73). Participants in every group saw six graphically manipulated videos of an automated car from the driver’s perspective with either transparency, post-hoc explanations or both combined. Transparency resulted in higher trust, higher satisfaction and higher level 2 situational awareness (SA) than explainability. Transparency also resulted in higher level 2 SA than the combined condition, but did not differ in terms of trust or satisfaction. Moreover, explainability led to significantly worse satisfaction compared to combined feedback. Although our findings should be replicated in more ecologically valid driving situations, we tentatively conclude that transparency alone should be implemented in semi self-driving cars, and possibly automated systems in general, whenever possible to make them most satisfactory, trustworthy, and resulting in higher SA.
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Srinivasu, Parvathaneni Naga, N. Sandhya, Rutvij H. Jhaveri, and Roshani Raut. "From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies." Mobile Information Systems 2022 (June 13, 2022): 1–20. http://dx.doi.org/10.1155/2022/8167821.
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Introduction. Artificial intelligence (AI) models have been employed to automate decision-making, from commerce to more critical fields directly affecting human lives, including healthcare. Although the vast majority of these proposed AI systems are considered black box models that lack explainability, there is an increasing trend of attempting to create medical explainable Artificial Intelligence (XAI) systems using approaches such as attention mechanisms and surrogate models. An AI system is said to be explainable if humans can tell how the system reached its decision. Various XAI-driven healthcare approaches and their performances in the current study are discussed. The toolkits used in local and global post hoc explainability and the multiple techniques for explainability pertaining the Rational, Data, and Performance explainability are discussed in the current study. Methods. The explainability of the artificial intelligence model in the healthcare domain is implemented through the Local Interpretable Model-Agnostic Explanations and Shapley Additive Explanations for better comprehensibility of the internal working mechanism of the original AI models and the correlation among the feature set that influences decision of the model. Results. The current state-of-the-art XAI-based and future technologies through XAI are reported on research findings in various implementation aspects, including research challenges and limitations of existing models. The role of XAI in the healthcare domain ranging from the earlier prediction of future illness to the disease’s smart diagnosis is discussed. The metrics considered in evaluating the model’s explainability are presented, along with various explainability tools. Three case studies about the role of XAI in the healthcare domain with their performances are incorporated for better comprehensibility. Conclusion. The future perspective of XAI in healthcare will assist in obtaining research insight in the healthcare domain.
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Cho, Hyeoncheol, Youngrock Oh, and Eunjoo Jeon. "SEEN: Seen: Sharpening Explanations for Graph Neural Networks Using Explanations From Neighborhoods." Advances in Artificial Intelligence and Machine Learning 03, no. 02 (2023): 1165–79. http://dx.doi.org/10.54364/aaiml.2023.1168.
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Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth of GNN applications, recent progress in explaining predictions from GNNs, such as sensitivity analysis, perturbation methods, and attribution methods, showed great opportunities and possibilities for explaining GNN predictions. In this study, we propose a method to improve the explanation quality of node classification tasks that can be applied in a post hoc manner through aggregation of auxiliary explanations from important neighboring nodes, named SEEN. Applying SEEN does not require modification of a graph and can be used with diverse explainability techniques due to its independent mechanism. Experiments on matching motifparticipating nodes from a given graph show great improvement in explanation accuracy of up to 12.71% and demonstrate the correlation between the auxiliary explanations and the enhanced explanation accuracy through leveraging their contributions. SEEN provides a simple but effective method to enhance the explanation quality of GNN model outputs, and this method is applicable in combination with most explainability techniques.
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Chatterjee, Soumick, Arnab Das, Chirag Mandal, et al. "TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models." Applied Sciences 12, no. 4 (2022): 1834. http://dx.doi.org/10.3390/app12041834.
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Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning-based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas that influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generates visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post hoc interpretations and explanations of their models, develop more explainable tools, and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-Flight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.
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Roscher, R., B. Bohn, M. F. Duarte, and J. Garcke. "EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 817–24. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-817-2020.
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Abstract. For some time now, machine learning methods have been indispensable in many application areas. Especially with the recent development of efficient neural networks, these methods are increasingly used in the sciences to obtain scientific outcomes from observational or simulated data. Besides a high accuracy, a desired goal is to learn explainable models. In order to reach this goal and obtain explanation, knowledge from the respective domain is necessary, which can be integrated into the model or applied post-hoc. We discuss explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results. We show that recent advances in machine learning to enhance transparency, interpretability, and explainability are helpful in overcoming these challenges.