Top Research Papers on XAI

Complex machine learning models perform better. However, we consider these models as black boxes. That’s where Explainable AI (XAI) comes into play. Understanding why a model makes a specific prediction can be as crucial as its accuracy for many applications, researchers, and decision-makers. In many real-world applications, the explainability and transparency of AI systems are indispensable. The research community and industry are giving growing attention to explainability and explainable AI. Compared to traditional machine learning methods, deep neural networks (DNNs) have been very successf...

Explainable Artificial Intelligence (XAI) has emerged as a critical facet in the realm of machine learning and artificial intelligence, responding to the increasing complexity of models, particularly deep neural networks, and the subsequent need for transparent decision making processes. This research paper delves into the essence of XAI, unraveling its significance across diverse domains such as healthcare, finance, and criminal justice. As a countermeasure to the opacity of intricate models, the paper explores various XAI methods and techniques, including LIME and SHAP, weighing their interp...

Artificial Intelligence (AI) and Machine Learning (ML) are gaining increasing attention regarding their potential applications in auditing. One major challenge of their adoption in auditing is the lack of explainability of their results. As AI/ML matures, so do techniques that can enhance the interpretability of AI, a.k.a., Explainable Artificial Intelligence (XAI). This paper introduces XAI techniques to auditing practitioners and researchers. We discuss how different XAI techniques can be used to meet the requirements of audit documentation and audit evidence standards. Furthermore, we demon...

A review on interpretabilities suggested by different research works and categorize them is provided, hoping that insight into interpretability will be born with more considerations for medical practices and initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.

This paper presents a comprehensive review of the state-of-the-art on how to build a Trustworthy and eXplainable AI, taking into account that AI is a black box with little insight into its underlying structure.

This paper will review recent approaches to constructing taxonomies of XAI methods and discuss general challenges concerning them as well as their individual advantages and limitations, and propose and discuss three possible solutions.

A taxonomy and categorizing the XAI techniques based on their scope of explanations, methodology behind the algorithms, and explanation level or usage which helps build trustworthy, interpretable, and self-explanatory deep learning models is proposed.

An in-depth and systematic review of recent studies that use XAI models in the scope of the IoT domain and classify the studies according to their methodology and application areas.

Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback, as several applications in the real world are critical tasks, such as the medical field or the autonomous driving field. The explainability of models applied on time series has not gather much attention compared to the computer vision or the natural language processing fields. In this paper, we present an overview of existing explainable AI (XAI) methods applied on time series and illustrate the type of explanations ...

This Systematic Literature Review (SLR) identifies 138 relevant articles from 2005 to 2022 and highlights empirical examples demonstrating XAI's potential benefits in the financial industry and concisely defines the existing challenges, requirements, and unresolved issues in applying XAI in the financial sector.

A narrative review of XAI and a taxonomy of the XAI literature comprising its precepts and approaches are developed to help alleviate the scepticism and hesitancy toward AI adoption and integration in construction.

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have ...

Floods are natural hazards that lead to devastating financial losses and large displacements of people. Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area. The design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning models. Although these models have achieved better accuracy than traditional models, they are not widely used by stakeholders due to their black-box nature. In this study, we propose the application of an explainable artificial intelligence (XAI) model that incorporate...

An overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis methods and an outlook of future opportunities for XAI inmedical image analysis are presented.

It is argued that the use of post-hoc explanatory methods is useful in many cases, but that these methods have limitations that prohibit reliance as the sole mechanism to guarantee fairness of model outcomes in high-stakes decision-making.

A model is provided that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata and can serve researchers from the variety of different disciplines involved in XAI as a common ground.

This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts.

Despite widespread adoption and outstanding performance, machine learning models are considered as “black boxes”, since it is very difficult to understand how such models operate in practice. Therefore, in the power systems field, which requires a high level of accountability, it is hard for experts to trust and justify decisions and recommendations made by these models. Meanwhile, in the last couple of years, Explainable Artificial Intelligence (XAI) techniques have been developed to improve the explainability of machine learning models, such that their output can be better understood. In thi...

This research describes how a Shapley additive explanations (SHAP) model can be utilized to interpret the results of a deep learning model that is developed for wildfire susceptibility prediction and infer that developing an explainable model would aid in comprehending the model's decision to map wildfire susceptibility, pinpoint high-contributing components in the prediction model, and consequently control fire hazards effectively.

This study introduces an explainable artificial intelligence (XAI) approach of convolutional neural networks (CNNs) for classification in vibration signals analysis using Gradient class activation mapping (Grad-CAM) to generate the attention of model.