24 papers · ranked by Valyu relevance
Miguel A. Lago, Ghada Zamzmi, Brandon Eich, Jana G. Delfino + 2 more
Explainability features are intended to provide insight into the internal mechanisms of an Artificial Intelligence (AI) device, but there is a lack of evaluation techniques for assessing the quality of provided explanations. We propose a framework to assess and report explainable AI features in medical images. Our…
Matteo Rizzo, Alberto Veneri, Matteo Marcuzzo, Alessandro Zangari + 4 more
Explainable Artificial Intelligence, or XAI, is a vibrant research topic in the artificial intelligence community. It is raising growing interest across methods and domains, especially those involving high-stakes decision-making, such as the biomedical sector. Much has been written about the subject, yet XAI still…
Arno Leue, Akhila Bairy, Maike Schwammberger
Autonomous and software-intensive systems have been growing in occurrence, complexity, and assumed responsibility. Due to the high complexity of these systems, properties like transparency and explainability must be a focus of investigation. To date, no universally applicable definition and guide for the development of…
Yongze Song, Filip Biljecki, Gustau Camps-Valls, Peter M. Atkinson
Geospatial artificial intelligence (GeoAI) is reshaping our understanding of Earth and urban systems by integrating advanced artificial intelligence techniques with diverse geospatial data and methodologies. This backstory highlights recent GeoAI advances and applications as presented in the 11 articles in the iScience…
Patricia Marcella Evite, Ekaterina Svetlova, Doina Bucur
As Artificial Intelligence (AI) becomes increasingly embedded in financial decision-making, the opacity of complex models presents significant challenges for professionals and regulators. While the field of Explainable AI (XAI) attempts to bridge this gap, current research often reduces the implementation challenge to…
Raymond Sheh, Isaac Monteath
Explainable Artificial Intelligence (XAI) has become popular in the last few years. The Artificial Intelligence (AI) community in general, and the Machine Learning (ML) community in particular, is coming to the realisation that in many applications, for AI to be trusted, it must not only demonstrate good performance in…
Fang Tang, Renqi Zhu, Feng Yao, Junzhi Wang + 2 more
Introduction As person-job recommendation systems (PJRS) increasingly mediate hiring decisions, concerns over their “black box” opacity have sparked demand for explainable AI (XAI) solutions. Methods This systematic review examines 85 studies on explainable PJRS methods published between 2019 and August 2025, selected…
Mohamed Ebraheem, Jamie Toghranegar, Yael Bensoussan, John Michael Templeton + 1 more
Background Driven by recent advances in artificial intelligence (AI), particularly in medicine, audio-based voice and speech biomarkers are increasingly investigated for various medical applications as a complementary or even alternative modality to traditional medical devices. The adoption of deep learning techniques…
Fatemeh Nasiri, Mohsen Hooshmand, Mahdi Nouroozi
Drug—drug interactions between biotech and small-molecule drugs play a critical role in medication safety and therapeutic efficacy. However, most existing computational DDI prediction methods focus primarily on interactions between small-molecule drugs, leaving biotech–small-molecule interactions comparatively…
Boris Babic, I. Glenn Cohen, Julian Savulescu
As artificial intelligence and machine learning (AI/ML) systems become increasingly pervasive in society, their opacity-i.e., the difficulty, and sometimes impossibility, of understanding why they make the decisions they make-has become a serious problem. This is especially true in sensitive decision-making contexts…
Christian Oliva, Luis F. Lago-Fernández
The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI). However, objectively evaluating explanation fidelity and aligning XAI metrics with human-centered understanding remain critical open challenges. In this work, we propose a…
Authors not listed
Predicting drug-induced toxicity remains a central challenge in computational toxicology, particularly for organ-specific adverse effects that arise from diverse structural, biochemical, and mechanistic origins. Existing deep learning models excel at pattern recognition but often lack mechanistic interpretability…
Kaiyun Guo, Jiarui Ding
Cell type annotation is a fundamental task in single-cell genomics. Although various methods have been developed for automatic cell type annotation, they often function as black-box models, making predictions without explaining their reasoning and lacking proper uncertainty estimation for their predictions.…
Tomás Pereira, João Vitorino, Eva Maia, Isabel Praça
Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable. An explanation can appear plausible to humans but fail to capture the internal reasoning of a model, particularly when dealing with complex…
Michal Moshkovitz, Suraj Srinivas, Lesia Semenova, Nave Frost + 6 more
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: research has not yet…
David Martínez-Enguita, Thomas Hillerton, Julia Åkesson, Maria Lerm + 1 more
Genome-scale DNA methylation (DNAm) profiles capture organismal physiology, but most predictive models lack transparency and multi-level applicability. Here we develop an explainable framework that quantifies respiratory, cardiovascular, and metabolic status as bounded health scores (0–1) derived from sex-specific…
Eddie Conti, Álvaro Parafita, Axel Brando
Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models. However, most methods can produce variable attribution scores due to stochastic components in their definition. In this paper, we propose a distribution-based framework to capture the stability of…
Muwei Li
Naturalistic paradigms offer a powerful window into human cognition, but it remains difficult to link rich, continuous movie content to distributed brain activity in an interpretable way. In this study, I use a multimodal large language model (Gemini) as an automated “semantic annotator” to bridge naturalistic movie…
Authors not listed
Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full…
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Supervised deep learning has become a standard approach to deliver competitive predictive tools that allow relating the structure of molecules and their physicochemical features to properties such as binding to protein targets, performance as electronic materials, and reactivity. However, efforts to understand how…
Santiago Herce Castañon, Christopher R. Stephens
Predicting and understanding behaviour is a primary objective of many disciplines, especially human behaviour, as it is the cause of many of the world’s most pressing problems. Although it is a fundamental concept in multiple disciplines, there is no agreed operational definition of what it is. Neither is there a…
Eléonore Houdoyer, Solène Le Bars, Valérian Chambon
The sense of agency, the experience of controlling one’s actions and their consequences, is a fundamental component of human interaction with autonomous systems. As artificial intelligence increasingly mediates decision-making in domains such as autonomous driving, understanding and monitoring agency-related processes…
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High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
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Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…