In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understa...
Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which c...
This study focuses on the interpretability of diabetic retinopathy classification models. Seven widely used interpretability methods—Gradient, SmoothGrad, Integrated Gradients, SHAP, DeepLIFT, Grad-CAM++, and ScoreCAM—are applied to asses...
This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluat...
Efficient energy management relies on uncovering meaningful consumption patterns from large-scale electricity load demand profiles. With the widespread adoption of sensor technologies such as smart meters and IoT-based monitoring systems, granular an...
Model interpretability is essential in machine learning, particularly for applications in critical fields like healthcare, where understanding model decisions is paramount. While SHAP (SHapley Additive exPlanations) has proven to be a robust tool for...
As an indispensable piece of production equipment in the industrial field, wire rope is directly related to personnel safety and the normal operation of equipment. Therefore, it is necessary to perform broken wire detection. Deep learning has powerfu...
Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box...
Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for si...
Machine learning (ML) models have achieved remarkable success in areas ranging from healthcare to autonomous systems. Yet, their inherent complexity frequently obscures the reasoning behind their decisions, undermining transparency, accountability, a...
Artificial intelligence (AI) powers breakthroughs in language processing, computer vision, and scientific discovery; yet, the increasing complexity of frontier models makes their reasoning opaque. This opacity undermines public trust, complicates dep...
Deep neural decoders have demonstrated strong performance in electroencephalography (EEG)-based motor-imagery (MI) brain–computer interfaces (BCIs), yet their internal representations remain difficult to interpret, limiting neuroscientific insi...
Currently, interpretability methods focus more on less objective human-understandable semantics. To objectify and standardize interpretability research, in this study, we provide notions of interpretability based on approximation theory. We first def...
Machine learning (ML) engineering increasingly incorporates principles from software and requirements engineering to improve development rigor; however, key non-functional requirements (NFRs) such as interpretability and explainability remain difficu...
Interpretability is becoming increasingly important for predictive model analysis. Unfortunately, as remarked by many authors, there is still no consensus regarding this notion. The goal of this paper is to propose the definition of a score that allo...
Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opa...
With the development of intelligent transportation systems (ITS), individual travel prediction has become a key technology for optimizing urban transportation. However, deep learning models are limited in decision-sensitive scenarios due to their lac...
The convergence of artificial intelligence (AI), explainable AI (XAI), and neuroscience is fostering new opportunities for understanding both machine and biological intelligence through interpretable and human-centered learning paradigms. In this Per...
Decision tree ensembles, such as Random Forests and Gradient Boosting Machines, achieve high predictive accuracy but often suffer from limited transparency due to their structural complexity. Due to this lack, interpretability challenges arise in dom...
In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a h...
The healthcare industry seeks to integrate AI into clinical applications, yet understanding AI decision making remains a challenge for healthcare practitioners as these systems often function as black boxes. Our work benchmarks the Pattern Discovery...
Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and...
Feature importance analysis is essential for interpreting machine learning models in diabetes mellitus (DM) risk prediction; however, existing interpretability methods often produce inconsistent feature rankings across models. This study proposes a u...
With continuous development of artificial intelligence, text classification has gradually changed from a knowledge-based method to a method based on statistics and machine learning. Among them, it is a very important and efficient way to classify tex...
This study introduces a novel framework that simultaneously addresses the challenges of performance accuracy and result interpretability in transcriptomic-data-based classification. Background/objectives: In biological data classification, it is chal...
While predictive accuracy is often prioritized in machine learning (ML) models, interpretability remains essential in scientific and high-stakes domains. However, diverse interpretability algorithms frequently yield conflicting explanations, highligh...
Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert’s level, interpretability (highlighting how and what a trained model learned and why it makes a specific...
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been ac...
Landslide is one of the most damaging natural hazards, causing extensive damage to the infrastructure and threatening human life. Although advances have been made in landslide susceptibility assessment by objective explainable machine learning, the i...
We introduce Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. Inspired by brains, BIMT embeds neurons in a geometric space and augments the loss function with a cost proportional to the lengt...
The rapid advancement of artificial intelligence (AI) and machine learning has revolutionised how systems process information, make decisions, and adapt to dynamic environments. AI-driven approaches have significantly enhanced efficiency and problem-...
With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time s...
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 in...
Knowledge Tracing, which estimates how students’ knowledge evolves during interactions with educational content, is a cornerstone of Intelligent Tutoring Systems. While deep learning models achieve superior predictive performance in this task,...
Objective: Sepsis is a life-threatening response to infection and a major cause of hospital mortality. Machine learning (ML) models have demonstrated better sepsis prediction performance than integer risk scores but are less widely used in clinical s...
In the field of Natural Language Inference (NLI), model interpretability remains an urgent and unresolved challenge. Existing interpretability-oriented annotated datasets are highly limited, and manually constructing natural language explanations is...
In this paper, we present a novel self-supervised framework for Sentiment Cue Extraction (SCE) aimed at enhancing the interpretability of text sentiment analysis models. Our approach leverages self-supervised learning to identify and highlight key te...
The increasing complexity of deep learning models can make it difficult to interpret and fit models beyond a purely accuracy-focused evaluation. This is where interpretable and eXplainable Artificial Intelligence (XAI) come into play to facilitate an...
Predicting student performance in the future is a crucial behavior prediction problem in education. By predicting student performance, educational experts can provide individualized instruction, optimize the allocation of resources, and develop educa...
Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs,...
Seedling count is a key indicator of wheat population during the seedling stage. Accurate seedling detection is therefore vital. Recently, deep learning techniques have been widely used to detect wheat seedlings and estimate seedling numbers from UAV...
The lack of interpretability in artificial intelligence models (i.e., deep learning, machine learning, and rules-based) is an obstacle to their widespread adoption in the healthcare domain. The absence of understandability and transparency frequently...
Large language models (LLMs) have revolutionized the field of artificial intelligence in both academia and industry, transforming how we communicate, search for information, and create content. However, these models face knowledge cutoffs and costly...
An echo state network (ESN) is an efficient recurrent neural network (RNN) that is widely used in time series prediction tasks due to its simplicity and low training cost. However, the “black-box” nature of reservoirs hinders the developm...
The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions. When modeling complex sequences, attention mechanism...
Uncertainty is present in every single prediction of Machine Learning (ML) models. Uncertainty Quantification (UQ) is arguably relevant, in particular for safety-critical applications. Prior research focused on the development of methods to quantify...
Machine learning models are increasingly being used across diverse fields, including file fragment classification. As these models become more prevalent, it is crucial to understand and interpret their decision-making processes to ensure accountabili...
Accurate tumor segmentation is crucial for clinical diagnosis, treatment planning, and efficacy evaluation in medical imaging. Although traditional image processing techniques have been widely applied in tumor segmentation, they often perform poorly...
In order to obtain a damage assessment method that can clearly express the influence mechanism of defect characteristics on fatigue damage, an integrated analytical framework combining the Shapley Additivity Interpretation (SHAP) method with the Extr...
Recent developments in NLP and web-scale document analysis have increasingly emphasized the importance of interpretability and contextual dependence in semantic representations. Although modern word embeddings achieve remarkable empirical performance...
of 1,218