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Search · four archives
28 papers · ranked by Valyu relevance
Arun Das, Paul Rad
—Nowadays, deep neural networks are widely used in mission critical systems such as healthcare, self-driving vehicles, and military which have direct impact on human lives. However, the black-box nature of deep neural networks challenges its use in mission critical applications, raising ethical and judicial concerns…
Lindsay Wells, Tomasz Bednarz
Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as a response to the need for increased transparency and trust in AI. This is particularly important as AI is used in sensitive domains with societal, ethical, and safety implications. Work in XAI has primarily focused on…
Yiming Zhang, Ying Weng, Jonathan Lund, Oliver Faust + 2 more
'U Rajendra Acharya'] In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of…
Ahmad Kamal Mohd Nor, Srinivasa Rao Pedapati, Masdi Muhammad, Víctor Leiva + 1 more
Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need…
Bettina Finzel
Explainable artificial intelligence (XAI) is gaining importance in physiological research, where artificial intelligence is now used as an analytical and predictive tool for many medical research questions. The primary goal of XAI is to make AI models understandable for human decision-makers. This can be achieved in…
Bas H. M. van der Velden
There has been an increasing trend of using artificial intelligence (AI) in high-stakes decision-making that has an impact on human lives, including but not limited to the criminal justice system, autonomous vehicles, food safety, and radiology . The current standard for AI in radiology is deep learning . Deep learning…
Prashant Gohel, Priyanka Singh, Manoranjan Mohanty
Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI.…
Julie Gerlings, Arisa Shollo, Ioanna Constantiou
The diffusion of artificial intelligence (AI) applications in organizations and society has fueled research on explaining AI decisions. The explainable AI (xAI) field is rapidly expanding with numerous ways of extracting information and visualizing the output of AI technologies (e.g. deep neural networks). Yet, we have…
Amirehsan Ghasemi, Soheil Hashtarkhani, David L. Schwartz, Arash Shaban‐Nejad
'Arash Shaban‐Nejad'] Title: Abstract With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians…
Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder
—Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on…
MD Abdullah Al Nasim, Parag Biswas, Abdur Rashid, Angona Biswas + 1 more
'Kishor Datta Gupta'] One of today's most significant and transformative technologies is the rapidly developing field of artificial intelligence (AI). Defined as a computer system that simulates human cognitive processes, AI is present in many aspects of our daily lives, from the self-driving cars on the road to the…
Clive Gomes, Lalitha Natraj, Shijun Liu, Anushka Datta
In this survey paper, we deep dive into the field of Explainable Artificial Intelligence (XAI). After introducing the scope of this paper, we start by discussing what an "explanation" really is. We then move on to discuss some of the existing approaches to XAI and build a taxonomy of the most popular methods. Next, we…
Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab, Rabeb Mizouni + 4 more
'Alyssa Song' 'Robin Cohen' 'Hadi Otrok' 'Azzam Mourad'] Abstract—The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning…
Peter B. R. Hartog, Fabian Krüger, Samuel Genheden, Igor V. Tetko
Stakeholders of machine learning models desire explainable artificial intelligence (XAI) to produce human-understandable and consistent interpretations. In computational toxicity, augmentation of text-based molecular representations has been used successfully for transfer learning on downstream tasks. Augmentations of…
Shakran Mahmood, Colin Teo, Jeremy Sim, Wei Zhang + 7 more
The rapid advancement of artificial intelligence (AI) has sparked renewed discussions on its trustworthiness and the concept of eXplainable AI (XAI). Recent research in neuroscience has emphasized the relevance of XAI in studying cognition. This scoping review aims to identify and analyze various XAI methods used to…
Jacqueline Beinecke, Anna Saranti, Alessa Angerschmid, Bastian Pfeifer + 3 more
Lack of trust in artificial intelligence (AI) models in medicine is still the key blockage for the use of AI in clinical decision support systems (CDSS). Although AI models are already performing excellently in systems medicine, their black-box nature entails that patient-specific decisions are incomprehensible for the…
Sahil Sharma, Muskaan Singh, Liam McDaid, Saugat Bhattacharyya
Explainable Artificial Intelligence (XAI) is crucial in healthcare as it helps make intricate machine learning models understandable and clear, especially when working with diverse medical data, enhancing trust, improving diagnostic accuracy, and facilitating better patient outcomes. This paper thoroughly examines the…
Davor Horvatić, Tomislav Lipic
Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society. Most of the recent success in AI comes from the…
Haomiao Wang, Julien Aligon, Julien May, Emmanuel Doumard + 6 more
Although the benefits of machine learning (ML) are undeniable in health-care, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. The challenge is to identify explanations that make sense to the biomedical expert. This work proposes…
Francisco Javier Cantero Zorita, Mikel Galafate, Javier M. Moguerza, Isaac Martín de Diego + 2 more
'Isaac Martín de Diego' 'Mauricio González' 'Gema Gutiérrez Peña'] Recent advancements in Artificial Intelligence (AI) have transformed decision-making in aeronautics and aerospace. These advancements in AI have brought with them the need to understand the reasons behind the predictions generated by AI systems and…
Bryant Han, Qingling Duan, Ting Hu
Machine learning models in biomedicine have become increasingly complex, often functioning as black boxes. However, understanding contributors to disease and making actionable health interventions requires interpretable models. Common explainable AI methods like SHAP focus on feature importance but fall short in…
Alena Kalyakulina, Igor Yusipov, Maria Giulia Bacalini, Claudio Franceschi + 2 more
DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific pipeline for joint data processing. We propose a comprehensive approach of combined DNA methylation datasets to classify…
Christina Humer, Henry Heberle, Floriane Montanari, Thomas Wolf + 4 more
The introduction of machine learning to small molecule research – an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate – has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or…
Yongbing Zhao, Jinfeng Shao, Yan W Asmann
While explainable artificial intelligence has emerged with aim at interpreting how the machine learning models make decisions, many model explainers have been developed in computer vision field. By far, there still lacks an understanding of the applicability of these model explainers in biological study. To address…
Eugen Hruska, Liang Zhao, Fang Liu
Interpretation of chemistry on an atomic scale improves with explainable artificial intelligence (XAI). The parts of the molecule with the most significant influence on the chemical property of interest can be visualized with atomwise and bondwise attributions. Nonetheless, the attributions from different XAI methods…
Marco Bertolini, Linlin Zhao, Djork-Arné Clevert, Floriane Montanari
The field of explainable AI applied to molecular property prediction models has often been reduced to deriving atomic contributions. This has impaired the interpretability of such models, as chemists rather think in terms of larger, chemically meaningful structures, which often do not simply reduce to the sum of their…
Authors not listed
Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…
Authors not listed
Explainability methods in machine learning-driven research are increasingly being used, but it remains challenging to assess their reliability without deeply investigating the specific problem at hand. In this work, we present a Python-based Workflow for Interpretability Scoring using matched molecular Pairs (WISP).…