Model-agnostic Explanations Research Articles

Toward explainable AI (XAI) for mental health detection based on language behavior.

Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automated detection and prediction of mental health disorders among the population. Recent research in this area has prioritized predictive accuracy over model interpretability by relying on deep learning methods. However, prioritizing predictive accuracy over model interpretability can result in a lack of transparency in the decision-making process, which is critical in sensitive applications such as healthcare. There is thus a growing need for explainable AI (XAI) approaches to psychiatric diagnosis and prediction. The main aim of this work is to address a gap by conducting a systematic investigation of XAI approaches in the realm of automatic detection of mental disorders from language behavior leveraging textual data from social media. In pursuit of this aim, we perform extensive experiments to evaluate the balance between accuracy and interpretability across predictive mental health models. More specifically, we build BiLSTM models trained on a comprehensive set of human-interpretable features, encompassing syntactic complexity, lexical sophistication, readability, cohesion, stylistics, as well as topics and sentiment/emotions derived from lexicon-based dictionaries to capture multiple dimensions of language production. We conduct extensive feature ablation experiments to determine the most informative feature groups associated with specific mental health conditions. We juxtapose the performance of these models against a "black-box" domain-specific pretrained transformer adapted for mental health applications. To enhance the interpretability of the transformers models, we utilize a multi-task fusion learning framework infusing information from two relevant domains (emotion and personality traits). Moreover, we employ two distinct explanation techniques: the local interpretable model-agnostic explanations (LIME) method and a model-specific self-explaining method (AGRAD). These methods allow us to discern the specific categories of words that the information-infused models rely on when generating predictions. Our proposed approaches are evaluated on two public English benchmark datasets, subsuming five mental health conditions (attention-deficit/hyperactivity disorder, anxiety, bipolar disorder, depression and psychological stress).

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.

Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.

The COVID-19 pandemic had a global impact and created an unprecedented emergency in healthcare and other related frontline sectors. Various Artificial-Intelligence-based models were developed to effectively manage medical resources and identify patients at high risk. However, many of these AI models were limited in their practical high-risk applicability due to their "black-box" nature, i.e., lack of interpretability of the model. To tackle this problem, Explainable Artificial Intelligence (XAI) was introduced, aiming to explore the "black box" behavior of machine learning models and offer definitive and interpretable evidence. XAI provides interpretable analysis in a human-compliant way, thus boosting our confidence in the successful implementation of AI systems in the wild. In this regard, this study explores the use of model-agnostic XAI models, such as SHapley Additive exPlanations values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), for COVID-19 symptom analysis in Indian patients toward a COVID severity prediction task. Various machine learning models such as Decision Tree Classifier, XGBoost Classifier, and Neural Network Classifier are leveraged to develop Machine Learning models. The proposed XAI tools are found to augment the high performance of AI systems with human interpretable evidence and reasoning, as shown through the interpretation of various explainability plots. Our comparative analysis illustrates the significance of XAI tools and their impact within a healthcare context. The study suggests that SHAP and LIME analysis are promising methods for incorporating explainability in model development and can lead to better and more trustworthy ML models in the future.

Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer’s disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III–IV and V–VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Understanding the black-box: towards interpretable and reliable deep learning models

Deep learning (DL) has revolutionized the field of artificial intelligence by providing sophisticated models across a diverse range of applications, from image and speech recognition to natural language processing and autonomous driving. However, deep learning models are typically black-box models where the reason for predictions is unknown. Consequently, the reliability of the model becomes questionable in many circumstances. Explainable AI (XAI) plays an important role in improving the transparency and interpretability of the model thereby making it more reliable for real-time deployment. To investigate the reliability and truthfulness of DL models, this research develops image classification models using transfer learning mechanism and validates the results using XAI technique. Thus, the contribution of this research is twofold, we employ three pre-trained models VGG16, MobileNetV2 and ResNet50 using multiple transfer learning techniques for a fruit classification task consisting of 131 classes. Next, we inspect the reliability of models, based on these pre-trained networks, by utilizing Local Interpretable Model-Agnostic Explanations, the LIME, a popular XAI technique that generates explanations for the predictions. Experimental results reveal that transfer learning provides optimized results of around 98% accuracy. The classification of the models is validated on different instances using LIME and it was observed that each model predictions are interpretable and understandable as they are based on pertinent image features that are relevant to particular classes. We believe that this research gives an insight for determining how an interpretation can be drawn from a complex AI model such that its accountability and trustworthiness can be increased.

Foreign direct investment and local interpretable model-agnostic explanations: a rational framework for FDI decision making

PurposeThis study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.Design/methodology/approachData for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.FindingsThe findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.Research limitations/implicationsThis research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.Practical implicationsThrough this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.Originality/valueAn IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

A novel dataset and local interpretable model-agnostic explanations (LIME) for monkeypox prediction

In image-based diagnosis, machine learning recently showed great potential, particularly in the detection of cancer, the identification of tumour cells, and the diagnosis of COVID-19. Similar methods could be used to detect monkeypox on human skin, however there isn’t a public dataset with data on monkeypox that can be used to train and evaluate machine learning models. In order to address this, the dataset “Monkeypox2022” has been created and made available on GitHub (https://github.com/Angmo21720/data-set). Images from internet and open-source sources were used to construct the collection; these sources did not impose any restrictions on use, including commercial use. Additionally, the enhanced VGG16 model was proposed and ran two Studies for comparison. According to the findings, the model’s AUC Score for identifying monkeypox patients in Study One was 0.972, while its AUC Score for identifying such patients in Study Two was 0.748. Deeper understanding of the characteristics that distinguish a monkeypox infection is provided by using Local Interpretable Model-Agnostic Explanations (LIME), which is used to decipher predictions and reveal the features utilized by a model in decision-making.

SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction

The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain. This attention stems from concerns regarding transparency, legal and ethical considerations, and the medical significance of predictions generated by these deep neural networks in clinical decision support systems. To address this matter, our study delves into the application of four well-established interpretability methods: Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), Gradient-weighted Class Activation Mapping (Grad-CAM), and Layer-wise Relevance Propagation (LRP). Leveraging the approach of transfer learning with a multi-label-multi-class chest radiography dataset, we aim to interpret predictions pertaining to specific pathology classes. Our analysis encompasses both single-label and multi-label predictions, providing a comprehensive and unbiased assessment through quantitative and qualitative investigations, which are compared against human expert annotation. Notably, Grad-CAM demonstrates the most favorable performance in quantitative evaluation, while the LIME heatmap score segmentation visualization exhibits the highest level of medical significance. Our research underscores both the outcomes and the challenges faced in the holistic approach adopted for assessing these interpretability methods and suggests that a multimodal-based approach, incorporating diverse sources of information beyond chest radiography images, could offer additional insights for enhancing interpretability in the medical domain.

Insulation aging condition assessment of transformer in the visual domain based on SE-CNN

The aging of transformer oil–paper insulation (OPI) contributes significantly to transformer failure and reduced service life. Therefore, reliable and precise evaluation of the aging status of transformer insulation condition is crucial in averting transformer failure. This article proposes a novel deep learning-based aging condition assessment technique of transformers by combining FDS (Frequency domain spectroscopy) and squeeze-and-excitation-enabled convolutional neural network (SE-CNN). An intrinsic product-based transformation using a Gramian angular field is employed to dimensionally expand the 1-D FDS signals acquired from OPI samples into 2D images. Each feature extraction channel in the SE-CNN-based aging diagnosis framework is assigned a specific weight, enabling the model to concentrate on the most relevant features while discarding the rest. Furthermore, the effect of moisture variation on the model is also considered under three different conditions to ensure the consequence of compensation for the effect of moisture on FDS. A comparison with traditional machine learning models has also been made to ensure the viability of the proposed methodology. Additionally, the model is interpreted by creating explanation weight plots of the feature coefficients using a local interpretable model-agnostic explanations (LIME) method in Python. The findings demonstrate that the novel aging diagnosis framework visually assesses the transformer’s OPI system with the highest accuracy of 99.12% in the test set. Furthermore, it facilitates the automatic extraction of deep aging features, thus providing better generalization and convergence capability.

#DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring.

Corals are colonial animals within the Phylum Cnidaria that form coral reefs, playing a significant role in marine environments by providing habitat for fish, mollusks, crustaceans, sponges, algae, and other organisms. Global climate changes are causing more intense and frequent thermal stress events, leading to corals losing their color due to the disruption of a symbiotic relationship with photosynthetic endosymbionts. Given the importance of corals to the marine environment, monitoring coral reefs is critical to understanding their response to anthropogenic impacts. Most coral monitoring activities involve underwater photographs, which can be costly to generate on large spatial scales and require processing and analysis that may be time-consuming. The Marine Ecology Laboratory (LECOM) at the Federal University of Rio Grande do Norte (UFRN) developed the project "#DeOlhoNosCorais" which encourages users to post photos of coral reefs on their social media (Instagram) using this hashtag, enabling people without previous scientific training to contribute to coral monitoring. The laboratory team identifies the species and gathers information on coral health along the Brazilian coast by analyzing each picture posted on social media. To optimize this process, we conducted baseline experiments for image classification and semantic segmentation. We analyzed the classification results of three different machine learning models using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The best results were achieved by combining EfficientNet for feature extraction and Logistic Regression for classification. Regarding semantic segmentation, the U-Net Pix2Pix model produced a pixel-level accuracy of 86%. Our results indicate that this tool can enhance image selection for coral monitoring purposes and open several perspectives for improving classification performance. Furthermore, our findings can be expanded by incorporating other datasets to create a tool that streamlines the time and cost associated with analyzing coral reef images across various regions.

Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

Abstract A startup is a recently established business venture led by entrepreneurs, to create and offer new products or services. The discovery of promising startups is a challenging task for creditors, policymakers, and investors. Therefore, the startup survival rate prediction is required to be developed for the success/failure of startup companies. In this paper, the feature selection using the Convex Least Angle Regression Least Absolute Shrinkage and Selection Operator (CLAR-LASSO) is proposed to improve the classification of startup survival rate prediction. The Swish Activation Function based Long Short-Term Memory (SAFLSTM) is developed for classifying the survival rate of startups. Further, the Local Interpretable Model-agnostic Explanations (LIME) model interprets the predicted classification to the user. Existing research such as Hyper Parameter Tuning (HPT)-Logistic regression, HPT-Support Vector Machine (SVM), HPT-XGBoost, and SAFLSTM are used to compare the CLAR-LASSO. The accuracy of the CLAR-LASSO is 95.67% which is high when compared to the HPT-Logistic regression, HPT-SVM, HPT-XGBoost, and SAFLSTM.

Evidence-based machine learning algorithm to predict failure following cartilage procedures in the knee

IntroductionClinical decision-making is highly based on expert opinion. Machine learning is increasingly used to develop patient-specific risk prediction analysis to improve patient selection prior to surgery. ObjectivesTo develop machine learning algorithms to predict failure of surgical procedures that address cartilage defects of the knee and detect variables associated with failure. MethodsAn institutional database was queried for cartilage procedures performed between 2000 and 2018. Failure was defined as revision cartilage surgery or knee arthroplasty. One hundred and one preoperative and intraoperative features were evaluated as potential predictors. Four machine learning algorithms were trained and internally validated. ResultsOne thousand and ninety-one patients with a minimum follow-up of 2 years were included and underwent chondroplasty (n = 560; 51%), osteochondral allograft transplantation (n = 306; 28%), microfracture (n = 150; 14%), autologous chondrocyte implantation (n = 39; 4%), or osteochondral autograft transplantation (n = 36; 3%). The Random Forest algorithm was the best-performing algorithm, with an area under the curve of 0.765 and a Brier score of 0.135. The most important features for predicting failure were symptom duration, age, body mass index, lesion grade, and total lesion area. Local Interpretable Model-agnostic Explanations analysis provided patient-specific comparisons for the risk of failure of an individual patient being assigned various types of cartilage procedures. ConclusionsMachine learning algorithms were accurate in predicting the risk of failure following cartilage procedures of the knee, with the most important features in descending order being symptom duration, age, body mass index, lesion grade, and total lesion area. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee.

The Potential of a CT-Based Machine Learning Radiomics Analysis to Differentiate Brucella and Pyogenic Spondylitis.

Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are common spinal infections with similar manifestations, making their differentiation challenging. This study aimed to explore the potential of CT-based radiomics features combined with machine learning algorithms to differentiate PS from BS. This retrospective study involved the collection of clinical and radiological information from 138 patients diagnosed with either PS or BS in our hospital between January 2017 and December 2022, based on histopathology examination and/or germ isolations. The region of interest (ROI) was defined by two radiologists using a 3D Slicer open-source platform, utilizing blind analysis of sagittal CT images against histopathological examination results. PyRadiomics, a Python package, was utilized to extract ROI features. Several methods were performed to reduce the dimensionality of the extracted features. Machine learning algorithms were trained and evaluated using techniques like the area under the receiver operating characteristic curve (AUC; confusion matrix-related metrics, calibration plot, and decision curve analysis to assess their ability to differentiate PS from BS. Additionally, permutation feature importance (PFI; local interpretable model-agnostic explanations (LIME; and Shapley additive explanation (SHAP) techniques were utilized to gain insights into the interpretabilities of the models that are otherwise considered opaque black-boxes. A total of 15 radiomics features were screened during the analysis. The AUC value and Brier score of best the model were 0.88 and 0.13, respectively. The calibration plot and decision curve analysis displayed higher clinical efficiency in the differential diagnosis. According to the interpretation results, the most impactful features on the model output were wavelet LHL small dependence low gray-level emphasis (GLDN). The CT-based radiomics models that we developed have proven to be useful in reliably differentiating between PS and BS at an early stage and can provide a reliable explanation for the classification results.

Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing

In the explainable artificial intelligence (XAI) field, an algorithm or a tool can help people understand how a model makes a decision. And this can help to select important features to reduce computational costs to realize high-performance computing. But existing methods are usually used to visualize important features or highlight active neurons, and few of them show the importance of relationships between features. In recent years, some methods based on a white-box approach have taken relationships between features into account, but most of them can only work on some specific models. Although methods based on a black-box approach can solve the above problems, most of them can only be applied to tabular data or text data instead of image data. To solve these problems, we propose a local interpretable model-agnostic explanation approach based on feature relationships. This approach combines the relationships between features into the interpretation process and then visualizes the interpretation results. Finally, this paper conducts a lot of experiments to evaluate the correctness of relationships between features and evaluates this XAI method in terms of accuracy, fidelity, and consistency.

An explainable hybrid deep learning architecture for WiFi-based indoor localization in Internet of Things environment

The indoor positioning service is one of the essential services needed in the Internet of Things ecosystem. Recently, many researchers have focused on the fingerprinting method, which is a method based on signal mapping with the Received Signal Strength Indicator (RSSI) values obtained from the WiFi access points. However, the fingerprinting method is particularly challenging due to some difficulties, such as RSSI variance over time, device diversity, and similarities of fingerprints in indoor networks. For this reason, machine learning and deep learning methods are used for many purposes, such as estimating the location of the building, floor, or the rooms. Detecting the location of a room or more than one reference point in a room becomes a more difficult problem because neighboring reference points’ fingerprints are very similar to each other. This study proposes a WiFi-based XAI-empowered deep learning architecture to predict the reference points in a room or corridor. We present a hybrid deep learning-based method that uses Long-Short-Term Memory to capture long-term dependencies between the signal features, and Convolutional Neural Network to extract local spatial signal patterns. Our deep learning aims to enrich fingerprinting data of each sample to capture more meaningful feature maps coming from different angles. Moreover, the method applies effective filtering and dimension scaling on the data to regulate the RSS values and capture more discriminative patterns using particle filter and sparse autoencoder. To provide local and global explanations for indoor localization estimations, the proposed architecture comprises two Explainable Artificial Intelligence techniques as Interpretable Model-Agnostic Explanations, and SHapley Additive exPlanations. The experimental results demonstrate that the proposed architecture achieves higher accuracy values for all datasets than the baseline deep learning methods.

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks.

Optical Coherence Tomography (OCT) is an imperative symptomatic tool empowering the diagnosis of retinal diseases and anomalies. The manual decision towards those anomalies by specialists is the norm, but its labor-intensive nature calls for more proficient strategies. Consequently, the study recommends employing a Convolutional Neural Network (CNN) for the classification of OCT images derived from the OCT dataset into distinct categories, including Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The average k-fold (k = 10) training accuracy, test accuracy, validation accuracy, training loss, test loss, and validation loss values of the proposed model are 96.33%, 94.29%, 94.12%, 0.1073, 0.2002, and 0.1927, respectively. Fast Gradient Sign Method (FGSM) is employed to introduce non-random noise aligned with the cost function's data gradient, with varying epsilon values scaling the noise, and the model correctly handles all noise levels below 0.1 epsilon. Explainable AI algorithms: Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are utilized to provide human interpretable explanations approximating the behaviour of the model within the region of a particular retinal image. Additionally, two supplementary datasets, namely, COVID-19 and Kidney Stone, are assimilated to enhance the model's robustness and versatility, resulting in a level of precision comparable to state-of-the-art methodologies. Incorporating a lightweight CNN model with 983,716 parameters, 2.37×108 floating point operations per second (FLOPs) and leveraging explainable AI strategies, this study contributes to efficient OCT-based diagnosis, underscores its potential in advancing medical diagnostics, and offers assistance in the Internet-of-Medical-Things.

An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.

There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and viewed as a black-box. The goal of this study is to extract knowledge from machine learning models for county-level variation in obesity prevalence. This study shows the application of explainable artificial intelligence methods to machine learning models of cross-sectional obesity prevalence data collected from 3,142 counties in the United States. County-level features from 7 broad categories: health outcomes, health behaviors, clinical care, social and economic factors, physical environment, demographics, and severe housing conditions. Explainable methods applied to random forest prediction models include feature importance, accumulated local effects, global surrogate decision tree, and local interpretable model-agnostic explanations. The results show that machine learning models explained 79% of the variance in obesity prevalence, with physical inactivity, diabetes, and smoking prevalence being the most important factors in predicting obesity prevalence. Interpretable machine learning models of health behaviors and outcomes provide substantial insight into obesity prevalence variation across counties in the United States.

Explainable Artificial Intelligence in Hydrology: Interpreting Black-Box Snowmelt-Driven Streamflow Predictions in an Arid Andean Basin of North-Central Chile

In recent years, a new discipline known as Explainable Artificial Intelligence (XAI) has emerged, which has followed the growing trend experienced by Artificial Intelligence over the last decades. There are, however, important gaps in the adoption of XAI in hydrology research, in terms of application studies in the southern hemisphere, or in studies associated with snowmelt-driven streamflow prediction in arid regions, to mention a few. This paper seeks to contribute to filling these knowledge gaps through the application of XAI techniques in snowmelt-driven streamflow prediction in a basin located in the arid region of north-central Chile in South America. For this, two prediction models were built using the Random Forest algorithm, for one and four months in advance. The models show good prediction performance in the training set for one (RMSE:1.33, R2: 0.94, MAE:0.55) and four (RMSE: 5.67, R2:0.94, MAE: 1.51) months in advance. The selected interpretation techniques (importance of the variable, partial dependence plot, accumulated local effects plot, Shapley values and local interpretable model-agnostic explanations) show that hydrometeorological variables in the vicinity of the basin are more important than climate variables and this occurs both for the dataset level and for the months with the lowest streamflow records. The importance of the XAI approach adopted in this study is discussed in terms of its contribution to the understanding of hydrological processes, as well as its role in high-stakes decision-making.

Explanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease

Directing research on Alzheimer’s disease toward only early prediction and accuracy cannot be considered a feasible approach toward tackling a ubiquitous degenerative disease today. Applying deep learning (DL), Explainable artificial intelligence, and advancing toward the human-computer interface (HCI) model can be a leap forward in medical research. This research aims to propose a robust explainable HCI model using SHAPley additive explanation, local interpretable model-agnostic explanations, and DL algorithms. The use of DL algorithms—logistic regression (80.87%), support vector machine (85.8%), k -nearest neighbor (87.24%), multilayer perceptron (91.94%), and decision tree (100%)—and explainability can help in exploring untapped avenues for research in medical sciences that can mold the future of HCI models. The presented model’s results show improved prediction accuracy by incorporating a user-friendly computer interface into decision-making, implying a high significance level in the context of biomedical and clinical research.

An Explainable Framework to Predict Child Sexual Abuse Awareness in People Using Supervised Machine Learning Models

Child sexual abuse (CSA) is a type of abuse in which an individual exploits a kid/adolescent sexually. CSA can happen in several places, such as schools, households, hostels, and other public spaces. However, a large number of people, including parents, do not have an awareness of this sensitive issue. Artificial intelligence (AI) and machine learning (ML) are being used in various disciplines in the modern era. Hence, supervised machine learning models have been used to predict child sexual abuse awareness in this study. The dataset contains answers provided by 3002 people regarding CSA. A questionnaire dataset obtained though crowdsourcing has been used to predict a person’s knowledge level regarding sexual abuse in children. Heterogenous ML and deep learning models have been used to make accurate predictions. To demystify the decisions made by the models, explainable artificial intelligence (XAI) techniques have also been utilized. XAI helps in making the models more interpretable, decipherable, and transparent. Four XAI techniques: Shapley additive values (SHAP), Eli5, QLattice, and local interpretable model-agnostic explanations (LIME), have been utilized to demystify the models. Among all the classifiers, the final stacked model obtained the best results with an accuracy of 94% for the test dataset. The excellent results demonstrated by the classifiers point to the use of artificial intelligence in preventing child sexual abuse by making people aware of it. The models can be used real time in facilities such as schools, hospitals, and other places to increase awareness among people regarding sexual abuse in children.