To see the other types of publications on this topic, follow the link: Post-hoc interpretability.
Journal articles on the topic 'Post-hoc interpretability'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Post-hoc interpretability.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Feng, Jiangfan, Yukun Liang, and Lin Li. "Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability." Computational Intelligence and Neuroscience 2021 (July 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/7367870.
Full textAbstract:
The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, fast and reliable detection of abnormal events is still a challenging work. Here, we introduce a two-stream approach that offers an autoencoder-based structure for fast and efficient detection to facilitate anomaly detection from surveillance video without labeled abnormal events. Furthermore, we present post hoc interpretability of feature map visualization to show the process of feature learning, revealing uncertain and ambiguous decision boundaries in the video sequence. Experimental results on Avenue, UCSD Ped2, and Subway datasets show that our method can detect abnormal events well and explain the internal logic of the model at the object level.
2
Zhang, Zaixi, Qi Liu, Hao Wang, Chengqiang Lu, and Cheekong Lee. "ProtGNN: Towards Self-Explaining Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 9127–35. http://dx.doi.org/10.1609/aaai.v36i8.20898.
Full textAbstract:
Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions made by GNNs. Existing explanation methods mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations for a trained GNN. The fact that post-hoc methods fail to reveal the original reasoning process of GNNs raises the need of building GNNs with built-in interpretability. In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs. In ProtGNN, the explanations are naturally derived from the case-based reasoning process and are actually used during classification. The prediction of ProtGNN is obtained by comparing the inputs to a few learned prototypes in the latent space. Furthermore, for better interpretability and higher efficiency, a novel conditional subgraph sampling module is incorporated to indicate which part of the input graph is most similar to each prototype in ProtGNN+. Finally, we evaluate our method on a wide range of datasets and perform concrete case studies. Extensive results show that ProtGNN and ProtGNN+ can provide inherent interpretability while achieving accuracy on par with the non-interpretable counterparts.
3
Xu, Qian, Wenzhao Xie, Bolin Liao, Chao Hu, Lu Qin, Zhengzijin Yang, Huan Xiong, Yi Lyu, Yue Zhou, and Aijing Luo. "Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review." Journal of Healthcare Engineering 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/9919269.
Full textAbstract:
Background. Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective. This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives. Methods. A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of Science, PubMed, ScienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization. Results. Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS. Conclusions. The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results contribute to the understanding of the interpretability of CDSS based on AI in health care. Future studies should focus on establishing formalism for defining interpretability, identifying the properties of interpretability, and developing an appropriate and objective metric for interpretability; in addition, the user's demand for interpretability and how to express and provide explanations are also the directions for future research.
4
Gill, Navdeep, Patrick Hall, Kim Montgomery, and Nicholas Schmidt. "A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing." Information 11, no. 3 (February 29, 2020): 137. http://dx.doi.org/10.3390/info11030137.
Full textAbstract:
This manuscript outlines a viable approach for training and evaluating machine learning systems for high-stakes, human-centered, or regulated applications using common Python programming tools. The accuracy and intrinsic interpretability of two types of constrained models, monotonic gradient boosting machines and explainable neural networks, a deep learning architecture well-suited for structured data, are assessed on simulated data and publicly available mortgage data. For maximum transparency and the potential generation of personalized adverse action notices, the constrained models are analyzed using post-hoc explanation techniques including plots of partial dependence and individual conditional expectation and with global and local Shapley feature importance. The constrained model predictions are also tested for disparate impact and other types of discrimination using measures with long-standing legal precedents, adverse impact ratio, marginal effect, and standardized mean difference, along with straightforward group fairness measures. By combining interpretable models, post-hoc explanations, and discrimination testing with accessible software tools, this text aims to provide a template workflow for machine learning applications that require high accuracy and interpretability and that mitigate risks of discrimination.
5
Marconato, Emanuele, Andrea Passerini, and Stefano Teso. "Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning." Entropy 25, no. 12 (November 22, 2023): 1574. http://dx.doi.org/10.3390/e25121574.
Full textAbstract:
Research on Explainable Artificial Intelligence has recently started exploring the idea of producing explanations that, rather than being expressed in terms of low-level features, are encoded in terms of interpretable concepts learned from data. How to reliably acquire such concepts is, however, still fundamentally unclear. An agreed-upon notion of concept interpretability is missing, with the result that concepts used by both post hoc explainers and concept-based neural networks are acquired through a variety of mutually incompatible strategies. Critically, most of these neglect the human side of the problem: a representation is understandable only insofar as it can be understood by the human at the receiving end. The key challenge in human-interpretable representation learning (hrl) is how to model and operationalize this human element. In this work, we propose a mathematical framework for acquiring interpretable representations suitable for both post hoc explainers and concept-based neural networks. Our formalization of hrl builds on recent advances in causal representation learning and explicitly models a human stakeholder as an external observer. This allows us derive a principled notion of alignment between the machine’s representation and the vocabulary of concepts understood by the human. In doing so, we link alignment and interpretability through a simple and intuitive name transfer game, and clarify the relationship between alignment and a well-known property of representations, namely disentanglement. We also show that alignment is linked to the issue of undesirable correlations among concepts, also known as concept leakage, and to content-style separation, all through a general information-theoretic reformulation of these properties. Our conceptualization aims to bridge the gap between the human and algorithmic sides of interpretability and establish a stepping stone for new research on human-interpretable representations.
6
Degtiarova, Ganna, Fran Mikulicic, Jan Vontobel, Chrysoula Garefa, Lukas S. Keller, Reto Boehm, Domenico Ciancone, et al. "Post-hoc motion correction for coronary computed tomography angiography without additional radiation dose - Improved image quality and interpretability for “free”." Imaging 14, no. 2 (December 23, 2022): 82–88. http://dx.doi.org/10.1556/1647.2022.00060.
Full textAbstract:
AbstractObjectiveTo evaluate the impact of a motion-correction (MC) algorithm, applicable post-hoc and not dependent on extended padding, on the image quality and interpretability of coronary computed tomography angiography (CCTA).MethodsNinety consecutive patients undergoing CCTA on a latest-generation 256-slice CT device were prospectively included. CCTA was performed with prospective electrocardiogram-triggering and the shortest possible acquisition window (without padding) at 75% of the R-R-interval. All datasets were reconstructed without and with MC of the coronaries. The latter exploits the minimal padding inherent in cardiac CT scans with this device due to data acquisition also during the short time interval needed for the tube to reach target currents and voltage (“free” multiphase). Two blinded readers independently assessed image quality on a 4-point Likert scale for all segments.ResultsA total of 1,030 coronary segments were evaluated. Application of MC both with automatic and manual coronary centerline tracking resulted in a significant improvement in image quality as compared to the standard reconstruction without MC (mean Likert score 3.67 [3.50;3.81] vs 3.58 [3.40;3.73], P = 0.005, and 3.7 [3.55;3.82] vs 3.58 [3.40;3.73], P < 0.001, respectively). Furthermore, MC significantly reduced the proportion of non-evaluable segments and patients with at least one non-evaluable coronary segment from 2% to as low as 0.3%, and from 14% to as low as 3%. Reduction of motion artifacts was predominantly observed in the right coronary artery.ConclusionsA post-hoc device-specific MC algorithm improves image quality and interpretability of prospectively electrocardiogram-triggered CCTA and reduces the proportion of non-evaluable scans without any additional radiation dose exposure.
7
Lao, Danning, Qi Liu, Jiazi Bu, Junchi Yan, and Wei Shen. "ViTree: Single-Path Neural Tree for Step-Wise Interpretable Fine-Grained Visual Categorization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (March 24, 2024): 2866–73. http://dx.doi.org/10.1609/aaai.v38i3.28067.
Full textAbstract:
As computer vision continues to advance and finds widespread applications across various domains, the need for interpretability in deep learning models becomes paramount. Existing methods often resort to post-hoc techniques or prototypes to explain the decision-making process, which can be indirect and lack intrinsic illustration. In this research, we introduce ViTree, a novel approach for fine-grained visual categorization that combines the popular vision transformer as a feature extraction backbone with neural decision trees. By traversing the tree paths, ViTree effectively selects patches from transformer-processed features to highlight informative local regions, thereby refining representations in a step-wise manner. Unlike previous tree-based models that rely on soft distributions or ensembles of paths, ViTree selects a single tree path, offering a clearer and simpler decision-making process. This patch and path selectivity enhances model interpretability of ViTree, enabling better insights into the model's inner workings. Remarkably, extensive experimentation validates that this streamlined approach surpasses various strong competitors and achieves state-of-the-art performance while maintaining exceptional interpretability which is proved by multi-perspective methods. Code can be found at https://github.com/SJTU-DeepVisionLab/ViTree.
8
Jalali, Anahid, Alexander Schindler, Bernhard Haslhofer, and Andreas Rauber. "Machine Learning Interpretability Techniques for Outage Prediction: A Comparative Study." PHM Society European Conference 5, no. 1 (July 22, 2020): 10. http://dx.doi.org/10.36001/phme.2020.v5i1.1244.
Full textAbstract:
Interpretable machine learning has recently attracted a lot of interest in the community. Currently, it mainly focuses on models trained on non-time series data. LIME and SHAP are well-known examples and provide visual-explanations of feature contributions to model decisions on an instance basis. Other post-hoc approaches, such as attribute-wise interpretations, also focus on tabular data only. Little research has been done so far on the interpretability of predictive models trained on time series data. Therefore, this work focuses on explaining decisions made by black-box models such as Deep Neural Networks trained on sensor data. In this paper, we present the results of a qualitative study, in which we systematically compare the types of explanations and the properties (e.g., method, computational complexity) of existing interpretability approaches for models trained on the PHM08-CMAPSS dataset. We compare shallow models such as regression trees (with limited depth) and black-box models such as Long-Short Term Memories (LSTMs) and Support Vector Regression (SVR). We train models on processed sensor data and explain their output using LIME, SHAP, and attribute-wise methods. Throughout our experiments, we point out the advantages and disadvantages of using these approaches for interpreting models trained on time series data. Our investigation results can serve as a guideline for selecting a suitable explainability method for black-box predictive models trained on time-series data.
9
García-Vicente, Clara, David Chushig-Muzo, Inmaculada Mora-Jiménez, Himar Fabelo, Inger Torhild Gram, Maja-Lisa Løchen, Conceição Granja, and Cristina Soguero-Ruiz. "Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors." Applied Sciences 13, no. 7 (March 23, 2023): 4119. http://dx.doi.org/10.3390/app13074119.
Full textAbstract:
Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Synthetic Minority Oversampling Technique Nominal (SMOTEN), Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Networks (CTGANs). Then, we assessed the impact of combining oversampling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are very close to those provided by real data, maintaining relevant insights, and contributing to increasing the predictive performance. The GAN-based model and a linear classifier outperform other oversampling techniques, improving the area under the curve by 2%. These results demonstrate the capability of synthetic data to help with both determining risk factors and building models for CVD prediction.
10
Wang, Zhengguang. "Validation, Robustness, and Accuracy of Perturbation-Based Sensitivity Analysis Methods for Time-Series Deep Learning Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23768–70. http://dx.doi.org/10.1609/aaai.v38i21.30559.
Full textAbstract:
This work undertakes studies to evaluate Interpretability Methods for Time Series Deep Learning. Sensitivity analysis assesses how input changes affect the output, constituting a key component of interpretation. Among the post-hoc interpretation methods such as back-propagation, perturbation, and approximation, my work will investigate perturbation-based sensitivity Analysis methods on modern Transformer models to benchmark their performances. Specifically, my work intends to answer three research questions: 1) Do different sensitivity analysis methods yield comparable outputs and attribute importance rankings? 2) Using the same sensitivity analysis method, do different Deep Learning models impact the output of the sensitivity analysis? 3) How well do the results from sensitivity analysis methods align with the ground truth?
11
Chatterjee, Soumick, Arnab Das, Chirag Mandal, Budhaditya Mukhopadhyay, Manish Vipinraj, Aniruddh Shukla, Rajatha Nagaraja Rao, Chompunuch Sarasaen, Oliver Speck, and Andreas Nürnberger. "TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models." Applied Sciences 12, no. 4 (February 10, 2022): 1834. http://dx.doi.org/10.3390/app12041834.
Full textAbstract:
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.
12
Murdoch, W. James, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. "Definitions, methods, and applications in interpretable machine learning." Proceedings of the National Academy of Sciences 116, no. 44 (October 16, 2019): 22071–80. http://dx.doi.org/10.1073/pnas.1900654116.
Full textAbstract:
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.
13
Aslam, Nida, Irfan Ullah Khan, Samiha Mirza, Alanoud AlOwayed, Fatima M. Anis, Reef M. Aljuaid, and Reham Baageel. "Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)." Sustainability 14, no. 12 (June 16, 2022): 7375. http://dx.doi.org/10.3390/su14127375.
Full textAbstract:
With the expansion of the internet, a major threat has emerged involving the spread of malicious domains intended by attackers to perform illegal activities aiming to target governments, violating privacy of organizations, and even manipulating everyday users. Therefore, detecting these harmful domains is necessary to combat the growing network attacks. Machine Learning (ML) models have shown significant outcomes towards the detection of malicious domains. However, the “black box” nature of the complex ML models obstructs their wide-ranging acceptance in some of the fields. The emergence of Explainable Artificial Intelligence (XAI) has successfully incorporated the interpretability and explicability in the complex models. Furthermore, the post hoc XAI model has enabled the interpretability without affecting the performance of the models. This study aimed to propose an Explainable Artificial Intelligence (XAI) model to detect malicious domains on a recent dataset containing 45,000 samples of malicious and non-malicious domains. In the current study, initially several interpretable ML models, such as Decision Tree (DT) and Naïve Bayes (NB), and black box ensemble models, such as Random Forest (RF), Extreme Gradient Boosting (XGB), AdaBoost (AB), and Cat Boost (CB) algorithms, were implemented and found that XGB outperformed the other classifiers. Furthermore, the post hoc XAI global surrogate model (Shapley additive explanations) and local surrogate LIME were used to generate the explanation of the XGB prediction. Two sets of experiments were performed; initially the model was executed using a preprocessed dataset and later with selected features using the Sequential Forward Feature selection algorithm. The results demonstrate that ML algorithms were able to distinguish benign and malicious domains with overall accuracy ranging from 0.8479 to 0.9856. The ensemble classifier XGB achieved the highest result, with an AUC and accuracy of 0.9991 and 0.9856, respectively, before the feature selection algorithm, while there was an AUC of 0.999 and accuracy of 0.9818 after the feature selection algorithm. The proposed model outperformed the benchmark study.
14
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.
Full textAbstract:
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.
15
Guo, Jiaxing, Zhiyi Tang, Changxing Zhang, Wei Xu, and Yonghong Wu. "An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference." Applied Sciences 13, no. 9 (May 4, 2023): 5659. http://dx.doi.org/10.3390/app13095659.
Full textAbstract:
Structural health monitoring systems continuously monitor the operational state of structures, generating a large amount of monitoring data during the process. The structural responses of extreme events, such as earthquakes, ship collisions, or typhoons, could be captured and further analyzed. However, it is challenging to identify these extreme events due to the interference of faulty data. Real-world monitoring systems suffer from frequent misidentification and false alarms. Unfortunately, it is difficult to improve the system’s built-in algorithms, especially the deep neural networks, partly because the current neural networks only output results and do not provide an interpretable decision-making basis. In this study, a deep learning-based method with visual interpretability is proposed to identify seismic data under sensor faults interference. The transfer learning technique is employed to learn the features of seismic data and faulty data with efficiency. A post hoc interpretation algorithm, termed Gradient-weighted Class Activation Mapping (Grad-CAM), is embedded into the neural networks to uncover the interest regions that support the output decision. The in situ seismic responses of a cable-stayed long-span bridge are used for method verification. The results show that the proposed method can effectively identify seismic data mixed with various types of faulty data while providing good interpretability.
16
Okajima, Yuzuru, and Kunihiko Sadamasa. "Deep Neural Networks Constrained by Decision Rules." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2496–505. http://dx.doi.org/10.1609/aaai.v33i01.33012496.
Full textAbstract:
Deep neural networks achieve high predictive accuracy by learning latent representations of complex data. However, the reasoning behind their decisions is difficult for humans to understand. On the other hand, rule-based approaches are able to justify the decisions by showing the decision rules leading to them, but they have relatively low accuracy. To improve the interpretability of neural networks, several techniques provide post-hoc explanations of decisions made by neural networks, but they cannot guarantee that the decisions are always explained in a simple form like decision rules because their explanations are generated after the decisions are made by neural networks.In this paper, to balance the accuracy of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting decision rules from a given ruleset. Because the networks are forced to make decisions based on decision rules, it is guaranteed that every decision is supported by a decision rule. Furthermore, we propose a technique to jointly optimize the neural network and the ruleset from which the network select rules. The log likelihood of correct classifications is maximized under a model with hyper parameters about the ruleset size and the prior probabilities of rules being selected. This feature makes it possible to limit the ruleset size or prioritize human-made rules over automatically acquired rules for promoting the interpretability of the output. Experiments on datasets of time-series and sentiment classification showed rule-constrained networks achieved accuracy as high as that achieved by original neural networks and significantly higher than that achieved by existing rule-based models, while presenting decision rules supporting the decisions.
17
Qian, Wei, Chenxu Zhao, Yangyi Li, Fenglong Ma, Chao Zhang, and Mengdi Huai. "Towards Modeling Uncertainties of Self-Explaining Neural Networks via Conformal Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 14651–59. http://dx.doi.org/10.1609/aaai.v38i13.29382.
Full textAbstract:
Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations. The fact that post-hoc methods can fail to reveal the actual original reasoning process of DNNs raises the need to build DNNs with built-in interpretability. Motivated by this, many self-explaining neural networks have been proposed to generate not only accurate predictions but also clear and intuitive insights into why a particular decision was made. However, existing self-explaining networks are limited in providing distribution-free uncertainty quantification for the two simultaneously generated prediction outcomes (i.e., a sample's final prediction and its corresponding explanations for interpreting that prediction). Importantly, they also fail to establish a connection between the confidence values assigned to the generated explanations in the interpretation layer and those allocated to the final predictions in the ultimate prediction layer. To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations. We perform the theoretical analysis for the proposed framework. Extensive experimental evaluation demonstrates the effectiveness of the proposed uncertainty framework.
18
Huai, Mengdi, Jinduo Liu, Chenglin Miao, Liuyi Yao, and Aidong Zhang. "Towards Automating Model Explanations with Certified Robustness Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6935–43. http://dx.doi.org/10.1609/aaai.v36i6.20651.
Full textAbstract:
Providing model explanations has gained significant popularity recently. In contrast with the traditional feature-level model explanations, concept-based explanations can provide explanations in the form of high-level human concepts. However, existing concept-based explanation methods implicitly follow a two-step procedure that involves human intervention. Specifically, they first need the human to be involved to define (or extract) the high-level concepts, and then manually compute the importance scores of these identified concepts in a post-hoc way. This laborious process requires significant human effort and resource expenditure due to manual work, which hinders their large-scale deployability. In practice, it is challenging to automatically generate the concept-based explanations without human intervention due to the subjectivity of defining the units of concept-based interpretability. In addition, due to its data-driven nature, the interpretability itself is also potentially susceptible to malicious manipulations. Hence, our goal in this paper is to free human from this tedious process, while ensuring that the generated explanations are provably robust to adversarial perturbations. We propose a novel concept-based interpretation method, which can not only automatically provide the prototype-based concept explanations but also provide certified robustness guarantees for the generated prototype-based explanations. We also conduct extensive experiments on real-world datasets to verify the desirable properties of the proposed method.
19
Xue, Mufan, Xinyu Wu, Jinlong Li, Xuesong Li, and Guoyuan Yang. "A Convolutional Neural Network Interpretable Framework for Human Ventral Visual Pathway Representation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (March 24, 2024): 6413–21. http://dx.doi.org/10.1609/aaai.v38i6.28461.
Full textAbstract:
Recently, convolutional neural networks (CNNs) have become the best quantitative encoding models for capturing neural activity and hierarchical structure in the ventral visual pathway. However, the weak interpretability of these black-box models hinders their ability to reveal visual representational encoding mechanisms. Here, we propose a convolutional neural network interpretable framework (CNN-IF) aimed at providing a transparent interpretable encoding model for the ventral visual pathway. First, we adapt the feature-weighted receptive field framework to train two high-performing ventral visual pathway encoding models using large-scale functional Magnetic Resonance Imaging (fMRI) in both goal-driven and data-driven approaches. We find that network layer-wise predictions align with the functional hierarchy of the ventral visual pathway. Then, we correspond feature units to voxel units in the brain and successfully quantify the alignment between voxel responses and visual concepts. Finally, we conduct Network Dissection along the ventral visual pathway including the fusiform face area (FFA), and discover variations related to the visual concept of `person'. Our results demonstrate the CNN-IF provides a new perspective for understanding encoding mechanisms in the human ventral visual pathway, and the combination of ante-hoc interpretable structure and post-hoc interpretable approaches can achieve fine-grained voxel-wise correspondence between model and brain. The source code is available at: https://github.com/BIT-YangLab/CNN-IF.
20
Kumar, Akshi, Shubham Dikshit, and Victor Hugo C. Albuquerque. "Explainable Artificial Intelligence for Sarcasm Detection in Dialogues." Wireless Communications and Mobile Computing 2021 (July 2, 2021): 1–13. http://dx.doi.org/10.1155/2021/2939334.
Full textAbstract:
Sarcasm detection in dialogues has been gaining popularity among natural language processing (NLP) researchers with the increased use of conversational threads on social media. Capturing the knowledge of the domain of discourse, context propagation during the course of dialogue, and situational context and tone of the speaker are some important features to train the machine learning models for detecting sarcasm in real time. As situational comedies vibrantly represent human mannerism and behaviour in everyday real-life situations, this research demonstrates the use of an ensemble supervised learning algorithm to detect sarcasm in the benchmark dialogue dataset, MUStARD. The punch-line utterance and its associated context are taken as features to train the eXtreme Gradient Boosting (XGBoost) method. The primary goal is to predict sarcasm in each utterance of the speaker using the chronological nature of a scene. Further, it is vital to prevent model bias and help decision makers understand how to use the models in the right way. Therefore, as a twin goal of this research, we make the learning model used for conversational sarcasm detection interpretable. This is done using two post hoc interpretability approaches, Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), to generate explanations for the output of a trained classifier. The classification results clearly depict the importance of capturing the intersentence context to detect sarcasm in conversational threads. The interpretability methods show the words (features) that influence the decision of the model the most and help the user understand how the model is making the decision for detecting sarcasm in dialogues.
21
Fan, Yongxian, Meng Liu, and Guicong Sun. "An interpretable machine learning framework for diagnosis and prognosis of COVID-19." PLOS ONE 18, no. 9 (September 21, 2023): e0291961. http://dx.doi.org/10.1371/journal.pone.0291961.
Full textAbstract:
Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.
22
Shen, Yifan, Li Liu, Zhihao Tang, Zongyi Chen, Guixiang Ma, Jiyan Dong, Xi Zhang, Lin Yang, and Qingfeng Zheng. "Explainable Survival Analysis with Convolution-Involved Vision Transformer." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2207–15. http://dx.doi.org/10.1609/aaai.v36i2.20118.
Full textAbstract:
Image-based survival prediction models can facilitate doctors in diagnosing and treating cancer patients. With the advance of digital pathology technologies, the big whole slide images (WSIs) provide increasing resolution and more details for diagnosis. However, the gigabyte-size WSIs would make most models computationally infeasible. To this end, instead of using the complete WSIs, most of existing models only use a pre-selected subset of key patches or patch clusters as input, which might fail to completely capture the patient's tumor morphology. In this work, we aim to develop a novel survival analysis model to fully utilize the complete WSI information. We show that the use of a Vision Transformer (ViT) backbone, together with convolution operations involved in it, is an effective framework to improve the prediction performance. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features, making the model more faithful and the results easier to comprehend by human users. Evaluations on two large cancer datasets show that our proposed model is more effective and has better interpretability for survival prediction.
23
Alangari, Nourah, Mohamed El Bachir Menai, Hassan Mathkour, and Ibrahim Almosallam. "Intrinsically Interpretable Gaussian Mixture Model." Information 14, no. 3 (March 3, 2023): 164. http://dx.doi.org/10.3390/info14030164.
Full textAbstract:
Understanding the reasoning behind a predictive model’s decision is an important and longstanding problem driven by ethical and legal considerations. Most recent research has focused on the interpretability of supervised models, whereas unsupervised learning has received less attention. However, the majority of the focus was on interpreting the whole model in a manner that undermined accuracy or model assumptions, while local interpretation received much less attention. Therefore, we propose an intrinsic interpretation for the Gaussian mixture model that provides both global insight and local interpretations. We employed the Bhattacharyya coefficient to measure the overlap and divergence across clusters to provide a global interpretation in terms of the differences and similarities between the clusters. By analyzing the GMM exponent with the Garthwaite–Kock corr-max transformation, the local interpretation is provided in terms of the relative contribution of each feature to the overall distance. Experimental results obtained on three datasets show that the proposed interpretation method outperforms the post hoc model-agnostic LIME in determining the feature contribution to the cluster assignment.
24
Kong, Weihao, Jianping Chen, and Pengfei Zhu. "Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research." Minerals 14, no. 2 (January 24, 2024): 128. http://dx.doi.org/10.3390/min14020128.
Full textAbstract:
Sandstone-hosted uranium deposits are indeed significant sources of uranium resources globally. They are typically found in sedimentary basins and have been extensively explored and exploited in various countries. They play a significant role in meeting global uranium demand and are considered important resources for nuclear energy production. Erlian Basin, as one of the sedimentary basins in northern China, is known for its uranium mineralization hosted within sandstone formations. In this research, machine learning (ML) methodology was applied to mineral prospectivity mapping (MPM) of the metallogenic zone in the Manite depression of the Erlian Basin. An ML model of 92% accuracy was implemented with the random forest algorithm. Additionally, the confusion matrix and receiver operating characteristic curve were used as model evaluation indicators. Furthermore, the model explainability research with post hoc interpretability algorithms bridged the gap between complex opaque (black-box) models and geological cognition, enabling the effective and responsible use of AI technologies. The MPM results shown in QGIS provided vivid geological insights for ML-based metallogenic prediction. With the favorable prospective targets delineated, geologists can make decisions for further uranium exploration.
25
Chen, Qian, Taolin Zhang, Dongyang Li, and Xiaofeng He. "CIDR: A Cooperative Integrated Dynamic Refining Method for Minimal Feature Removal Problem." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (March 24, 2024): 17763–71. http://dx.doi.org/10.1609/aaai.v38i16.29729.
Full textAbstract:
The minimal feature removal problem in the post-hoc explanation area aims to identify the minimal feature set (MFS). Prior studies using the greedy algorithm to calculate the minimal feature set lack the exploration of feature interactions under a monotonic assumption which cannot be satisfied in general scenarios. In order to address the above limitations, we propose a Cooperative Integrated Dynamic Refining method (CIDR) to efficiently discover minimal feature sets. Specifically, we design Cooperative Integrated Gradients (CIG) to detect interactions between features. By incorporating CIG and characteristics of the minimal feature set, we transform the minimal feature removal problem into a knapsack problem. Additionally, we devise an auxiliary Minimal Feature Refinement algorithm to determine the minimal feature set from numerous candidate sets. To the best of our knowledge, our work is the first to address the minimal feature removal problem in the field of natural language processing. Extensive experiments demonstrate that CIDR is capable of tracing representative minimal feature sets with improved interpretability across various models and datasets.
26
Collazos-Huertas, Diego Fabian, Andrés Marino Álvarez-Meza, David Augusto Cárdenas-Peña, Germán Albeiro Castaño-Duque, and César Germán Castellanos-Domínguez. "Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity." Sensors 23, no. 5 (March 2, 2023): 2750. http://dx.doi.org/10.3390/s23052750.
Full textAbstract:
Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of “poor skill” subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.
27
Olatunji, Iyiola E., Mandeep Rathee, Thorben Funke, and Megha Khosla. "Private Graph Extraction via Feature Explanations." Proceedings on Privacy Enhancing Technologies 2023, no. 2 (April 2023): 59–78. http://dx.doi.org/10.56553/popets-2023-0041.
Full textAbstract:
Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access to model explanations. Based on the different kinds of auxiliary information available to the adversary, we propose several graph reconstruction attacks. We show that additional knowledge of post-hoc feature explanations substantially increases the success rate of these attacks. Further, we investigate in detail the differences between attack performance with respect to three different classes of explanation methods for graph neural networks: gradient-based, perturbation-based, and surrogate model-based methods. While gradient-based explanations reveal the most in terms of the graph structure, we find that these explanations do not always score high in utility. For the other two classes of explanations, privacy leakage increases with an increase in explanation utility. Finally, we propose a defense based on a randomized response mechanism for releasing the explanations, which substantially reduces the attack success rate. Our code is available at https://github.com/iyempissy/graph-stealing-attacks-with-explanation.
28
Vieira, Carla Piazzon Ramos, and Luciano Antonio Digiampietri. "A study about Explainable Articial Intelligence: using decision tree to explain SVM." Revista Brasileira de Computação Aplicada 12, no. 1 (January 8, 2020): 113–21. http://dx.doi.org/10.5335/rbca.v12i1.10247.
Full textAbstract:
The technologies supporting Artificial Intelligence (AI) have advanced rapidly over the past few years and AI is becoming a commonplace in every aspect of life like the future of self-driving cars or earlier health diagnosis. For this to occur shortly, the entire community stands in front of the barrier of explainability, an inherent problem of latest models (e.g. Deep Neural Networks) that were not present in the previous hype of AI (linear and rule-based models). Most of these recent models are used as black boxes without understanding partially or even completely how different features influence the model prediction avoiding algorithmic transparency. In this paper, we focus on how much we can understand the decisions made by an SVM Classifier in a post-hoc model agnostic approach. Furthermore, we train a tree-based model (inherently interpretable) using labels from the SVM, called secondary training data to provide explanations and compare permutation importance method to the more commonly used measures such as accuracy and show that our methods are both more reliable and meaningful techniques to use. We also outline the main challenges for such methods and conclude that model-agnostic interpretability is a key component in making machine learning more trustworthy.
29
Maree, Charl, and Christian W. Omlin. "Can Interpretable Reinforcement Learning Manage Prosperity Your Way?" AI 3, no. 2 (June 13, 2022): 526–37. http://dx.doi.org/10.3390/ai3020030.
Full textAbstract:
Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and understanding of customers. Post-hoc approaches are typically used for explaining pretrained reinforcement learning models. Based on our previous modeling of customer spending behaviour, we adapt our recent reinforcement learning algorithm that intrinsically characterizes desirable behaviours and we transition to the problem of prosperity management. We train inherently interpretable reinforcement learning agents to give investment advice that is aligned with prototype financial personality traits which are combined to make a final recommendation. We observe that the trained agents’ advice adheres to their intended characteristics, they learn the value of compound growth, and, without any explicit reference, the notion of risk as well as improved policy convergence.
30
Gu, Jindong. "Interpretable Graph Capsule Networks for Object Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1469–77. http://dx.doi.org/10.1609/aaai.v35i2.16237.
Full textAbstract:
Capsule Networks, as alternatives to Convolutional Neural Networks, have been proposed to recognize objects from images. The current literature demonstrates many advantages of CapsNets over CNNs. However, how to create explanations for individual classifications of CapsNets has not been well explored. The widely used saliency methods are mainly proposed for explaining CNN-based classifications; they create saliency map explanations by combining activation values and the corresponding gradients, e.g., Grad-CAM. These saliency methods require a specific architecture of the underlying classifiers and cannot be trivially applied to CapsNets due to the iterative routing mechanism therein. To overcome the lack of interpretability, we can either propose new post-hoc interpretation methods for CapsNets or modifying the model to have build-in explanations. In this work, we explore the latter. Specifically, we propose interpretable Graph Capsule Networks (GraCapsNets), where we replace the routing part with a multi-head attention-based Graph Pooling approach. In the proposed model, individual classification explanations can be created effectively and efficiently. Our model also demonstrates some unexpected benefits, even though it replaces the fundamental part of CapsNets. Our GraCapsNets achieve better classification performance with fewer parameters and better adversarial robustness, when compared to CapsNets. Besides, GraCapsNets also keep other advantages of CapsNets, namely, disentangled representations and affine transformation robustness.
31
Nguyen, Hung Viet, and Haewon Byeon. "Predicting Depression during the COVID-19 Pandemic Using Interpretable TabNet: A Case Study in South Korea." Mathematics 11, no. 14 (July 17, 2023): 3145. http://dx.doi.org/10.3390/math11143145.
Full textAbstract:
COVID-19 has further aggravated problems by compelling people to stay indoors and limit social interactions, leading to a worsening of the depression situation. This study aimed to construct a TabNet model combined with SHapley Additive exPlanations (SHAP) to predict depression in South Korean society during the COVID-19 pandemic. We used a tabular dataset extracted from the Seoul Welfare Survey with a total of 3027 samples. The TabNet model was trained on this dataset, and its performance was compared to that of several other machine learning models, including Random Forest, eXtreme Gradient Boosting, Light Gradient Boosting, and CatBoost. According to the results, the TabNet model achieved an Area under the receiver operating characteristic curve value (AUC) of 0.9957 on the training set and an AUC of 0.9937 on the test set. Additionally, the study investigated the TabNet model’s local interpretability using SHapley Additive exPlanations (SHAP) to provide post hoc global and local explanations for the proposed model. By combining the TabNet model with SHAP, our proposed model might offer a valuable tool for professionals in social fields, and psychologists without expert knowledge in the field of data analysis can easily comprehend the decision-making process of this AI model.
32
Tulsani, Vijya, Prashant Sahatiya, Jignasha Parmar, and Jayshree Parmar. "XAI Applications in Medical Imaging: A Survey of Methods and Challenges." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (October 27, 2023): 181–86. http://dx.doi.org/10.17762/ijritcc.v11i9.8332.
Full textAbstract:
Medical imaging plays a pivotal role in modern healthcare, aiding in the diagnosis, monitoring, and treatment of various medical conditions. With the advent of Artificial Intelligence (AI), medical imaging has witnessed remarkable advancements, promising more accurate and efficient analysis. However, the black-box nature of many AI models used in medical imaging has raised concerns regarding their interpretability and trustworthiness. In response to these challenges, Explainable AI (XAI) has emerged as a critical field, aiming to provide transparent and interpretable solutions for medical image analysis. This survey paper comprehensively explores the methods and challenges associated with XAI applications in medical imaging. The survey begins with an introduction to the significance of XAI in medical imaging, emphasizing the need for transparent and interpretable AI solutions in healthcare. We delve into the background of medical imaging in healthcare and discuss the increasing role of AI in this domain. The paper then presents a detailed survey of various XAI techniques, ranging from interpretable machine learning models to deep learning approaches with built-in interpretability and post hoc interpretation methods. Furthermore, the survey outlines a wide range of applications where XAI is making a substantial impact, including disease diagnosis and detection, medical image segmentation, radiology reports, surgical planning, and telemedicine. Real-world case studies illustrate successful applications of XAI in medical imaging. The challenges associated with implementing XAI in medical imaging are thoroughly examined, addressing issues related to data quality, ethics, regulation, clinical integration, model robustness, and human-AI interaction. The survey concludes by discussing emerging trends and future directions in the field, highlighting the ongoing efforts to enhance XAI methods for medical imaging and the critical role XAI will play in the future of healthcare. This survey paper serves as a comprehensive resource for researchers, clinicians, and policymakers interested in the integration of Explainable AI into medical imaging, providing insights into the latest methods, successful applications, and the challenges that lie ahead.
33
Zhong, Xian, Zohaib Salahuddin, Yi Chen, Henry C. Woodruff, Haiyi Long, Jianyun Peng, Xiaoyan Xie, Manxia Lin, and Philippe Lambin. "An Interpretable Radiomics Model Based on Two-Dimensional Shear Wave Elastography for Predicting Symptomatic Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma." Cancers 15, no. 21 (November 6, 2023): 5303. http://dx.doi.org/10.3390/cancers15215303.
Full textAbstract:
Objective: The aim of this study was to develop and validate an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for symptomatic post-hepatectomy liver failure (PHLF) prediction in patients undergoing liver resection for hepatocellular carcinoma (HCC). Methods: A total of 345 consecutive patients were enrolled. A five-fold cross-validation was performed during training, and the models were evaluated in the independent test cohort. A multi-patch radiomics model was established based on the 2D-SWE images for predicting symptomatic PHLF. Clinical features were incorporated into the models to train the clinical–radiomics model. The radiomics model and the clinical–radiomics model were compared with the clinical model comprising clinical variables and other clinical predictive indices, including the model for end-stage liver disease (MELD) score and albumin–bilirubin (ALBI) score. Shapley Additive exPlanations (SHAP) was used for post hoc interpretability of the radiomics model. Results: The clinical–radiomics model achieved an AUC of 0.867 (95% CI 0.787–0.947) in the five-fold cross-validation, and this score was higher than that of the clinical model (AUC: 0.809; 95% CI: 0.715–0.902) and the radiomics model (AUC: 0.746; 95% CI: 0.681–0.811). The clinical–radiomics model showed an AUC of 0.822 in the test cohort, higher than that of the clinical model (AUC: 0.684, p = 0.007), radiomics model (AUC: 0.784, p = 0.415), MELD score (AUC: 0.529, p < 0.001), and ALBI score (AUC: 0.644, p = 0.016). The SHAP analysis showed that the first-order radiomics features, including first-order maximum 64 × 64, first-order 90th percentile 64 × 64, and first-order 10th percentile 32 × 32, were the most important features for PHLF prediction. Conclusion: An interpretable clinical–radiomics model based on 2D-SWE and clinical variables can help in predicting symptomatic PHLF in HCC.
34
Singh, Rajeev Kumar, Rohan Gorantla, Sai Giridhar Rao Allada, and Pratap Narra. "SkiNet: A deep learning framework for skin lesion diagnosis with uncertainty estimation and explainability." PLOS ONE 17, no. 10 (October 31, 2022): e0276836. http://dx.doi.org/10.1371/journal.pone.0276836.
Full textAbstract:
Skin cancer is considered to be the most common human malignancy. Around 5 million new cases of skin cancer are recorded in the United States annually. Early identification and evaluation of skin lesions are of great clinical significance, but the disproportionate dermatologist-patient ratio poses a significant problem in most developing nations. Therefore a novel deep architecture, named as SkiNet, is proposed to provide faster screening solution and assistance to newly trained physicians in the process of clinical diagnosis of skin cancer. The main motive behind SkiNet’s design and development is to provide a white box solution, addressing a critical problem of trust and interpretability which is crucial for the wider adoption of Computer-aided diagnosis systems by medical practitioners. The proposed SkiNet is a two-stage pipeline wherein the lesion segmentation is followed by the lesion classification. Monte Carlo dropout and test time augmentation techniques have been employed in the proposed method to estimate epistemic and aleatoric uncertainty. A novel segmentation model named Bayesian MultiResUNet is used to estimate the uncertainty on the predicted segmentation map. Saliency-based methods like XRAI, Grad-CAM and Guided Backprop are explored to provide post-hoc explanations of the deep learning models. The ISIC-2018 dataset is used to perform the experimentation and ablation studies. The results establish the robustness of the proposed model on the traditional benchmarks while addressing the black-box nature of such models to alleviate the skepticism of medical practitioners by incorporating transparency and confidence to the model’s prediction.
35
Arnold, Thomas, Daniel Kasenberg, and Matthias Scheutz. "Explaining in Time." ACM Transactions on Human-Robot Interaction 10, no. 3 (July 2021): 1–23. http://dx.doi.org/10.1145/3457183.
Full textAbstract:
Explainability has emerged as a critical AI research objective, but the breadth of proposed methods and application domains suggest that criteria for explanation vary greatly. In particular, what counts as a good explanation, and what kinds of explanation are computationally feasible, has become trickier in light of oqaque “black box” systems such as deep neural networks. Explanation in such cases has drifted from what many philosophers stipulated as having to involve deductive and causal principles to mere “interpretation,” which approximates what happened in the target system to varying degrees. However, such post hoc constructed rationalizations are highly problematic for social robots that operate interactively in spaces shared with humans. For in such social contexts, explanations of behavior, and, in particular, justifications for violations of expected behavior, should make reference to socially accepted principles and norms. In this article, we show how a social robot’s actions can face explanatory demands for how it came to act on its decision, what goals, tasks, or purposes its design had those actions pursue and what norms or social constraints the system recognizes in the course of its action. As a result, we argue that explanations for social robots will need to be accurate representations of the system’s operation along causal, purposive, and justificatory lines. These explanations will need to generate appropriate references to principles and norms—explanations based on mere “interpretability” will ultimately fail to connect the robot’s behaviors to its appropriate determinants. We then lay out the foundations for a cognitive robotic architecture for HRI, together with particular component algorithms, for generating explanations and engaging in justificatory dialogues with human interactants. Such explanations track the robot’s actual decision-making and behavior, which themselves are determined by normative principles the robot can describe and use for justifications.
36
Tursunalieva, Ainura, David L. J. Alexander, Rob Dunne, Jiaming Li, Luis Riera, and Yanchang Zhao. "Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications." Applied Sciences 14, no. 2 (January 5, 2024): 496. http://dx.doi.org/10.3390/app14020496.
Full textAbstract:
Transparency in AI models is essential for promoting human–AI collaboration and ensuring regulatory compliance. However, interpreting these models is a complex process influenced by various methods and datasets. This study presents a comprehensive overview of foundational interpretation techniques, meticulously referencing the original authors and emphasizing their pivotal contributions. Recognizing the seminal work of these pioneers is imperative for contextualizing the evolutionary trajectory of interpretation in the field of AI. Furthermore, this research offers a retrospective analysis of interpretation techniques, critically evaluating their inherent strengths and limitations. We categorize these techniques into model-based, representation-based, post hoc, and hybrid methods, delving into their diverse applications. Furthermore, we analyze publication trends over time to see how the adoption of advanced computational methods within various categories of interpretation techniques has shaped the development of AI interpretability over time. This analysis highlights a notable preference shift towards data-driven approaches in the field. Moreover, we consider crucial factors such as the suitability of these techniques for generating local or global insights and their compatibility with different data types, including images, text, and tabular data. This structured categorization serves as a guide for practitioners navigating the landscape of interpretation techniques in AI. In summary, this review not only synthesizes various interpretation techniques but also acknowledges the contributions of their original authors. By emphasizing the origins of these techniques, we aim to enhance AI model explainability and underscore the importance of recognizing biases, uncertainties, and limitations inherent in the methods and datasets. This approach promotes the ethical and practical use of interpretation insights, empowering AI practitioners, researchers, and professionals to make informed decisions when selecting techniques for responsible AI implementation in real-world scenarios.
37
Mokhtari, Ayoub, Roberto Casale, Zohaib Salahuddin, Zelda Paquier, Thomas Guiot, Henry C. Woodruff, Philippe Lambin, Jean-Luc Van Laethem, Alain Hendlisz, and Maria Antonietta Bali. "Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study." Diagnostics 14, no. 7 (March 28, 2024): 712. http://dx.doi.org/10.3390/diagnostics14070712.
Full textAbstract:
Purpose. This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients. Methods. Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan–Meier curves. SHAP analysis was applied to allow for post hoc interpretability. Results. A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70). Conclusions. An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.
38
Xie, Yibing, Nichakorn Pongsakornsathien, Alessandro Gardi, and Roberto Sabatini. "Explanation of Machine-Learning Solutions in Air-Traffic Management." Aerospace 8, no. 8 (August 12, 2021): 224. http://dx.doi.org/10.3390/aerospace8080224.
Full textAbstract:
Advances in the trusted autonomy of air-traffic management (ATM) systems are currently being pursued to cope with the predicted growth in air-traffic densities in all classes of airspace. Highly automated ATM systems relying on artificial intelligence (AI) algorithms for anomaly detection, pattern identification, accurate inference, and optimal conflict resolution are technically feasible and demonstrably able to take on a wide variety of tasks currently accomplished by humans. However, the opaqueness and inexplicability of most intelligent algorithms restrict the usability of such technology. Consequently, AI-based ATM decision-support systems (DSS) are foreseen to integrate eXplainable AI (XAI) in order to increase interpretability and transparency of the system reasoning and, consequently, build the human operators’ trust in these systems. This research presents a viable solution to implement XAI in ATM DSS, providing explanations that can be appraised and analysed by the human air-traffic control operator (ATCO). The maturity of XAI approaches and their application in ATM operational risk prediction is investigated in this paper, which can support both existing ATM advisory services in uncontrolled airspace (Classes E and F) and also drive the inflation of avoidance volumes in emerging performance-driven autonomy concepts. In particular, aviation occurrences and meteorological databases are exploited to train a machine learning (ML)-based risk-prediction tool capable of real-time situation analysis and operational risk monitoring. The proposed approach is based on the XGBoost library, which is a gradient-boost decision tree algorithm for which post-hoc explanations are produced by SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Results are presented and discussed, and considerations are made on the most promising strategies for evolving the human–machine interactions (HMI) to strengthen the mutual trust between ATCO and systems. The presented approach is not limited only to conventional applications but also suitable for UAS-traffic management (UTM) and other emerging applications.
39
Xie, Huafang, Lin Liu, and Han Yue. "Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique." International Journal of Environmental Research and Public Health 19, no. 21 (October 24, 2022): 13833. http://dx.doi.org/10.3390/ijerph192113833.
Full textAbstract:
Street crime is a common social problem that threatens the security of people’s lives and property. Understanding the influencing mechanisms of street crime is an essential precondition for formulating crime prevention strategies. Widespread concern has contributed to the development of streetscape environment features as they can significantly affect the occurrence of street crime. Emerging street view images are a low-cost and highly accessible data source. On the other hand, machine-learning models such as XGBoost (eXtreme Gradient Boosting) usually have higher fitting accuracies than those of linear regression models. Therefore, they are popular for modeling the relationships between crime and related impact factors. However, due to the “black box” characteristic, researchers are unable to understand how each variable contributes to the occurrence of crime. Existing research mainly focuses on the independent impacts of streetscape environment features on street crime, but not on the interaction effects between these features and the community socioeconomic conditions and their local variations. In order to address the above limitations, this study first combines street view images, an objective detection network, and a semantic segmentation network to extract a systematic measurement of the streetscape environment. Then, controlling for socioeconomic factors, we adopted the XGBoost model to fit the relationships between streetscape environment features and street crime at the street segment level. Moreover, we used the SHAP (Shapley additive explanation) framework, a post-hoc machine-learning explainer, to explain the results of the XGBoost model. The results demonstrate that, from a global perspective, the number of people on the street, extracted from street view images, has the most significant impact on street property crime among all the street view variables. The local interpretability of the SHAP explainer demonstrates that a particular variable has different effects on street crime at different street segments. The nonlinear associations between streetscape environment features and street crime, as well as the interaction effects of different streetscape environment features are discussed. The positive effect of the number of pedestrians on street crime increases with the length of the street segment and the number of crime generators. The combination of street view images and interpretable machine-learning techniques is helpful in better accurately understanding the complex relationships between the streetscape environment and street crime. Furthermore, the readily comprehensible results can offer a reference for formulating crime prevention strategies.
40
Turbé, Hugues, Mina Bjelogrlic, Christian Lovis, and Gianmarco Mengaldo. "Evaluation of post-hoc interpretability methods in time-series classification." Nature Machine Intelligence, March 13, 2023. http://dx.doi.org/10.1038/s42256-023-00620-w.
Full textAbstract:
AbstractPost-hoc interpretability methods are critical tools to explain neural-network results. Several post-hoc methods have emerged in recent years but they produce different results when applied to a given task, raising the question of which method is the most suitable to provide accurate post-hoc interpretability. To understand the performance of each method, quantitative evaluation of interpretability methods is essential; however, currently available frameworks have several drawbacks that hinder the adoption of post-hoc interpretability methods, especially in high-risk sectors. In this work we propose a framework with quantitative metrics to assess the performance of existing post-hoc interpretability methods, particularly in time-series classification. We show that several drawbacks identified in the literature are addressed, namely, the dependence on human judgement, retraining and the shift in the data distribution when occluding samples. We also design a synthetic dataset with known discriminative features and tunable complexity. The proposed methodology and quantitative metrics can be used to understand the reliability of interpretability methods results obtained in practical applications. In turn, they can be embedded within operational workflows in critical fields that require accurate interpretability results for, example, regulatory policies.
41
Madsen, Andreas, Siva Reddy, and Sarath Chandar. "Post-hoc Interpretability for Neural NLP: A Survey." ACM Computing Surveys, July 9, 2022. http://dx.doi.org/10.1145/3546577.
Full textAbstract:
Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use. Explaining models helps to address the safety and ethical concerns and is essential for accountability. Interpretability serves to provide these explanations in terms that are understandable to humans. Additionally, post-hoc methods provide explanations after a model is learned and are generally model-agnostic. This survey provides a categorization of how recent post-hoc interpretability methods communicate explanations to humans, it discusses each method in-depth, and how they are validated, as the latter is often a common concern.
42
Chen, Changdong, Allen Ding Tian, and Ruochen Jiang. "When Post Hoc Explanation Knocks: Consumer Responses to Explainable AI Recommendations." Journal of Interactive Marketing, December 7, 2023. http://dx.doi.org/10.1177/10949968231200221.
Full textAbstract:
Artificial intelligence (AI) recommendations are becoming increasingly prevalent, but consumers are often reluctant to trust them, in part due to the “black-box” nature of algorithm-facilitated recommendation agents. Despite the acknowledgment of the vital role of interpretability in consumer trust in AI recommendations, it remains unclear how to effectively increase interpretability perceptions and consequently enhance positive consumer responses. The current research addresses this issue by investigating the effects of the presence and type of post hoc explanations in boosting positive consumer responses to AI recommendations in different decision-making domains. Across four studies, the authors demonstrate that the presence of post hoc explanations increases interpretability perceptions, which in turn fosters positive consumer responses (e.g., trust, purchase intention, and click-through) to AI recommendations. Moreover, they show that the facilitating effect of post hoc explanations is stronger in the utilitarian (vs. hedonic) decision-making domain. Further, explanation type modulates the effectiveness of post hoc explanations such that attribute-based explanations are more effective in enhancing trust in the utilitarian decision-making domain, whereas user-based explanations are more effective in the hedonic decision-making domain.
43
Luo, Zijing, Renguang Zuo, Yihui Xiong, and Bao Zhou. "Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms." Natural Resources Research, April 12, 2023. http://dx.doi.org/10.1007/s11053-023-10200-9.
Full text
44
Marton, Sascha, Stefan Lüdtke, Christian Bartelt, Andrej Tschalzev, and Heiner Stuckenschmidt. "Explaining neural networks without access to training data." Machine Learning, January 10, 2024. http://dx.doi.org/10.1007/s10994-023-06428-4.
Full textAbstract:
AbstractWe consider generating explanations for neural networks in cases where the network’s training data is not accessible, for instance due to privacy or safety issues. Recently, Interpretation Nets ($$\mathcal {I}$$ I -Nets) have been proposed as a sample-free approach to post-hoc, global model interpretability that does not require access to training data. They formulate interpretation as a machine learning task that maps network representations (parameters) to a representation of an interpretable function. In this paper, we extend the $$\mathcal {I}$$ I -Net framework to the cases of standard and soft decision trees as surrogate models. We propose a suitable decision tree representation and design of the corresponding $$\mathcal {I}$$ I -Net output layers. Furthermore, we make $$\mathcal {I}$$ I -Nets applicable to real-world tasks by considering more realistic distributions when generating the $$\mathcal {I}$$ I -Net’s training data. We empirically evaluate our approach against traditional global, post-hoc interpretability approaches and show that it achieves superior results when the training data is not accessible.
45
Yang, Fanfan, Renguang Zuo, Yihui Xiong, Ying Xu, Jiaxin Nie, and Gubin Zhang. "Dual-Branch Convolutional Neural Network and Its Post Hoc Interpretability for Mapping Mineral Prospectivity." Mathematical Geosciences, March 22, 2024. http://dx.doi.org/10.1007/s11004-024-10137-6.
Full text
46
Velmurugan, Mythreyi, Chun Ouyang, Renuka Sindhgatta, and Catarina Moreira. "Through the looking glass: evaluating post hoc explanations using transparent models." International Journal of Data Science and Analytics, September 12, 2023. http://dx.doi.org/10.1007/s41060-023-00445-1.
Full textAbstract:
AbstractModern machine learning methods allow for complex and in-depth analytics, but the predictive models generated by these methods are often highly complex and lack transparency. Explainable Artificial Intelligence (XAI) methods are used to improve the interpretability of these complex “black box” models, thereby increasing transparency and enabling informed decision-making. However, the inherent fitness of these explainable methods, particularly the faithfulness of explanations to the decision-making processes of the model, can be hard to evaluate. In this work, we examine and evaluate the explanations provided by four XAI methods, using fully transparent “glass box” models trained on tabular data. Our results suggest that the fidelity of explanations is determined by the types of variables used, as well as the linearity of the relationship between variables and model prediction. We find that each XAI method evaluated has its own strengths and weaknesses, determined by the assumptions inherent in the explanation mechanism. Thus, though such methods are model-agnostic, we find significant differences in explanation quality across different technical setups. Given the numerous factors that determine the quality of explanations, including the specific explanation-generation procedures implemented by XAI methods, we suggest that model-agnostic XAI methods may still require expert guidance for implementation.
47
Björklund, Anton, Andreas Henelius, Emilia Oikarinen, Kimmo Kallonen, and Kai Puolamäki. "Explaining any black box model using real data." Frontiers in Computer Science 5 (August 8, 2023). http://dx.doi.org/10.3389/fcomp.2023.1143904.
Full textAbstract:
In recent years the use of complex machine learning has increased drastically. These complex black box models trade interpretability for accuracy. The lack of interpretability is troubling for, e.g., socially sensitive, safety-critical, or knowledge extraction applications. In this paper, we propose a new explanation method, SLISE, for interpreting predictions from black box models. SLISE can be used with any black box model (model-agnostic), does not require any modifications to the black box model (post-hoc), and explains individual predictions (local). We evaluate our method using real-world datasets and compare it against other model-agnostic, local explanation methods. Our approach solves shortcomings in other related explanation methods by only using existing data instead of sampling new, artificial data. The method also generates more generalizable explanations and is usable without modification across various data domains.
48
Xiao, Li-Ming, Yun-Qi Wan, and Zhen-Ran Jiang. "AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity." BMC Bioinformatics 22, no. 1 (December 2021). http://dx.doi.org/10.1186/s12859-021-04509-6.
Full textAbstract:
Abstract Background More and more Cas9 variants with higher specificity are developed to avoid the off-target effect, which brings a significant volume of experimental data. Conventional machine learning performs poorly on these datasets, while the methods based on deep learning often lack interpretability, which makes researchers have to trade-off accuracy and interpretability. It is necessary to develop a method that can not only match deep learning-based methods in performance but also with good interpretability that can be comparable to conventional machine learning methods. Results To overcome these problems, we propose an intrinsically interpretable method called AttCRISPR based on deep learning to predict the on-target activity. The advantage of AttCRISPR lies in using the ensemble learning strategy to stack available encoding-based methods and embedding-based methods with strong interpretability. Comparison with the state-of-the-art methods using WT-SpCas9, eSpCas9(1.1), SpCas9-HF1 datasets, AttCRISPR can achieve an average Spearman value of 0.872, 0.867, 0.867, respectively on several public datasets, which is superior to these methods. Furthermore, benefits from two attention modules—one spatial and one temporal, AttCRISPR has good interpretability. Through these modules, we can understand the decisions made by AttCRISPR at both global and local levels without other post hoc explanations techniques. Conclusion With the trained models, we reveal the preference for each position-dependent nucleotide on the sgRNA (short guide RNA) sequence in each dataset at a global level. And at a local level, we prove that the interpretability of AttCRISPR can be used to guide the researchers to design sgRNA with higher activity.
49
S. S. Júnior, Jorge, Jérôme Mendes, Francisco Souza, and Cristiano Premebida. "Survey on Deep Fuzzy Systems in Regression Applications: A View on Interpretability." International Journal of Fuzzy Systems, June 5, 2023. http://dx.doi.org/10.1007/s40815-023-01544-8.
Full textAbstract:
AbstractDeep learning (DL) has captured the attention of the community with an increasing number of recent papers in regression applications, including surveys and reviews. Despite the efficiency and good accuracy in systems with high-dimensional data, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. This paper aims to investigate the state-of-the-art of existing deep fuzzy systems (DFS) for regression, i.e., methods that combine DL and FLS with the aim of achieving good accuracy and good interpretability. Within the concept of explainable artificial intelligence (XAI), it is essential to contemplate interpretability in the development of intelligent models and not only seek to promote explanations after learning (post hoc methods), which is currently well established in the literature. Therefore, this work presents DFS for regression applications as the leading point of discussion of this topic that is not sufficiently explored in the literature and thus deserves a comprehensive survey.
50
Tiwari, Devisha Arunadevi, and Bhaskar Mondal. "A Unified Framework for Cyber Oriented Digital Engineering using Integration of Explainable Chaotic Cryptology on Pervasive Systems." Qeios, May 3, 2024. http://dx.doi.org/10.32388/60nk7h.
Full textAbstract:
Cyber Oriented Digital Engineering (CODE) aims to safeguard pervasive systems, cyber physical systems (CPS), internet of things (IoT) and embedded systems (ES) against advanced cyberattacks. Cyber oriented digital engineering pilots are earnestly required to secure transmission and credential exchanges during machine to machine (M2M) zero trust (ZT) communication. In order to construct the CODE pilot as a pivot of zero trust (ZT) communication, systems engineering employing chaotic cryptology primitives has been investigated. The empirical results with analysis of findings on its integration on real life platforms are presented as a pervasive framework, in this work. The focus was bestowed in developing an explainable approach, addressing both ante hoc and post hoc explanation needs. Ante hoc explanation ensures transparency in the encryption process, fostering user trust, while post hoc explanation facilitates the understanding of decryption outcomes. The properties of explainable approaches are investigated, emphasizing the balance between security and interpretability. Chaotic systems are employed to introduce a dynamic layer of complexity, enhancing encryption robustness. The article aims to contribute to the evolving field of explainable chaotic cryptology, bridging the gap between cryptographic strength and user comprehension in CODE pilot based zero trust (ZT) exchanges in multimedia content protection. Thus, this research is a communication brief case containing significant early findings and groundbreaking results studied as a part of a longer, multi-year analysis. Innovative techniques and pragmatic investigations have been discussed as a part of result dissemination in the empirical findings.