Search · four archives
Search · four archives
26 papers · ranked by Valyu relevance
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot + 8 more
'Adrien Bennetot' 'Siham Tabik' 'Alberto Barbado' 'Salvador García' 'Sergio Gil-López' 'Daniel Molina' 'Richard Benjamins' 'Raja Chatila' 'Francisco Herrera'] aTECNALIA, 48160 Derio, Spain bENSTA, Institute Polytechnique Paris and INRIA Flowers Team, Palaiseau, France cUniversity of the Basque Country (UPV/EHU), 48013…
Madalina Busuioc
Artificial intelligence (AI) algorithms govern in subtle yet fundamental ways the way we live and are transforming our societies. The promise of efficient, low-cost, or “neutral” solutions harnessing the potential of big data has led public bodies to adopt algorithmic systems in the provision of public services. As AI…
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…
Jean-Marc Fellous, Guillermo Sapiro, Andrew Rossi, Helen Mayberg + 1 more
'Michele Ferrante'] The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for…
David Medina-Ortiz, Ashkan Khalifeh, Hoda Anvari-Kazemabad, Mehdi D. Davari
Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in…
David B. Resnik, Mohammad Hosseini
Using artificial intelligence (AI) in research offers many important benefits for science and society but also creates novel and complex ethical issues. While these ethical issues do not necessitate changing established ethical norms of science, they require the scientific community to develop new guidance for the…
John A. McDermid, Yan Jia, Zoe Porter, Ibrahim Habli
In recent years, several new technical methods have been developed to make AI-models more transparent and interpretable. These techniques are often referred to collectively as ‘AI explainability’ or ‘XAI’ methods. This paper presents an overview of XAI methods, and links them to stakeholder purposes for seeking an…
Muhammad Salar Khan, Mehdi Nayebpour, Meng-Hao Li, Hadi El-Amine + 6 more
'Naoru Koizumi' 'James L. Olds' 'Isa Ebtehaj' 'Sayed M. Bateni' 'Babak Mohammadi' 'Stanislav N. Gorb'] European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect…
Frank Emmert‐Streib, Olli Yli‐Harja, Matthias Dehmer
rooted perspective Authors: ['Frank Emmert‐Streib' 'Olli Yli‐Harja' 'Matthias Dehmer'] We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability…
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…
Anastasia Angelopoulou, Epaminondas Kapetanios, David Harris Smith, Volker Steuber + 2 more
'Volker Steuber' 'Bencie Woll' 'Frauke Zeller'] Autonomous vehicles, social and industrial robots, image-based medical diagnosis, voice-based knowledge and control systems (e.g., Alexa, Siri), and recommendation systems are some application domains, where AI/ML-assisted digital artifacts already support daily routines…
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…
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…
Leilani H. Gilpin, Cecilia Testart, Nathaniel Fruchter, Julius Adebayo
'Julius Adebayo'] There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government officials, etc.) Questions that experts in artificial intelligence (AI) ask opaque systems provide inside explanations…
Sebastian Bruckert, Bettina Finzel, Ute Schmid
Increasing quality and performance of artificial intelligence (AI) in general and machine learning (ML) in particular is followed by a wider use of these approaches in everyday life. As part of this development, ML classifiers have also gained more importance for diagnosing diseases within biomedical engineering and…
Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon + 5 more
'Florence d’Alché–Buc' 'James Eagan' 'Winston Maxwell' 'Pavlo Mozharovskyi' 'Jayneel Parekh'] The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical…
Atoosa Kasirzadeh
The social implications of algorithmic decision-making in sensitive contexts have generated lively debates among multiple stakeholders, such as moral and political philosophers, computer scientists, and the public. Yet, the lack of a common language and a conceptual framework for an appropriate bridging of the moral…
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…
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…
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…
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).…
Authors not listed
Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Marcel van Gerven
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…