13 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…
Julia Amann, Dennis Vetter, Stig Nikolaj Blomberg, Helle Collatz Christensen + 11 more
'Helle Collatz Christensen' 'Megan Coffee' 'Sara Gerke' 'Thomas K. Gilbert' 'Thilo Hagendorff' 'Sune Holm' 'Michelle Livne' 'Andy Spezzatti' 'Inga Strümke' 'Roberto V. Zicari' 'Vince Istvan Madai' '' 'Henry Horng-Shing Lu'] Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper…
Vaishak Belle, Ioannis Papantonis
Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However…
Laura Arbelaez Ossa, Georg Starke, Giorgia Lorenzini, Julia E Vogt + 2 more
'David M Shaw' 'Bernice Simone Elger'] Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma and how much explanation…
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…
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…
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…
Jay Hegdé, Evgeniy Bart
In everyday life, we rely on human experts to make a variety of complex decisions, such as medical diagnoses. These decisions are typically made through some form of weakly guided learning, a form of learning in which decision expertise is gained through labeled examples rather than explicit instructions. Expert…
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…
Derek Leben
This paper will propose that explanations are valuable to those impacted by a model's decisions (model patients) to the extent that they provide evidence that a past adverse decision was unfair. Under this proposal, we should favor models and explainability methods which generate counterfactuals of two types. The first…
Kotaro Okazaki, Katsumi Inoue
Due to advances in computing power and internet technology, various industrial sectors are adopting IT infrastructure and artificial intelligence (AI) technologies. Recently, data-driven predictions have attracted interest in high-stakes decision-making. Despite this, advanced AI methods are less often used for such…
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…
Emanuele Albini, Antonio Rago, Pietro Baroni, Francesca Toni
The pursuit of trust in and fairness of AI systems in order to enable human-centric goals has been gathering pace of late, often supported by the use of explanations for the outputs of these systems. Several properties of explanations have been highlighted as critical for achieving trustworthy and fair AI systems, but…