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Search · four archives
18 papers · ranked by Valyu relevance
Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis
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 achieved through increased model complexity, turning such systems into…
Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal + 1 more
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of…
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…
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…
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…
Mohammed Saidul Islam, Iqram Hussain, Md Mezbaur Rahman, Se Jin Park + 2 more
'Md Azam Hossain' 'Ki H. Chon'] State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. Users may…
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…
Elisabeth Hildt, Mohamed Shehata
This article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS). After introducing the concept of explainability in AI and providing a short overview of AI-based clinical decision support systems (CDSSs) and the…
Michael Merry, Pat Riddle, Jim Warren
Background Wide-ranging concerns exist regarding the use of black-box modelling methods in sensitive contexts such as healthcare. Despite performance gains and hype, uptake of artificial intelligence (AI) is hindered by these concerns. Explainable AI is thought to help alleviate these concerns. However, existing…
Alexander Blanchard, Mariarosaria Taddeo
Intelligence agencies have identified artificial intelligence (AI) as a key technology for maintaining an edge over adversaries. As a result, efforts to develop, acquire, and employ AI capabilities for purposes of national security are growing. This article reviews the ethical challenges presented by the use of AI for…
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…
Mara Graziani, Lidia Dutkiewicz, Davide Calvaresi, José Pereira Amorim + 12 more
'José Pereira Amorim' 'Katerina Yordanova' 'Mor Vered' 'Rahul Nair' 'Pedro Henriques Abreu' 'Tobias Blanke' 'Valeria Pulignano' 'John O. Prior' 'Lode Lauwaert' 'Wessel Reijers' 'Adrien Depeursinge' 'Vincent Andrearczyk' 'Henning Müller'] Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to…
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…
Justin Blackman, Richard Veerapen
The necessity for explainability of artificial intelligence technologies in medical applications has been widely discussed and heavily debated within the literature. This paper comprises a systematized review of the arguments supporting and opposing this purported necessity. Both sides of the debate within the…
Jon Rueda, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona + 2 more
'Joaquín Hortal-Carmona' 'Txetxu Ausín' 'David Rodríguez-Arias'] The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John…
Adrian Erasmus, Tyler D. P. Brunet, Eyal Fisher
We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: (1) Are networks explainable, and if so, what does it…