Suchergebnisse

Andreas Holzinger acknowledges funding support from the Austrian Science Fund (FWF), Project: P-32554 explainable Artificial Intelligence and from the European Union's Horizon 2020 research and innovation program under grant agreement 826078 (Feature Cloud). This publication reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains; Natalia Diaz-Rodriguez is supported by the Spanish Government Juan de la Cierva Incorporacion contract (IJC2019-039152-I); Isabelle Augenstein's research is partially funded by a DFF Sapere Aude research leader grant; Javier Del Ser acknowledges funding support from the Basque Government through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19); Wojciech Samek acknowledges funding support from the European Union's Horizon 2020 research and innovation program under grant agreement No. 965221 (iToBoS), and the German Federal Ministry of Education and Research (ref. 01IS18025 A, ref. 01IS18037I and ref. 0310L0207C); Igor Jurisica acknowledges funding support from Ontario Research Fund (RDI 34876), Natural Sciences Research Council (NSERC 203475), CIHR Research Grant (93579), Canada Foundation for Innovation (CFI 29272, 225404, 33536), IBM, Ian Lawson van Toch Fund, the Schroeder Arthritis Institute via the Toronto General and Western Hospital Foundation. ; Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant. ; Austrian Science Fund (FWF) P-32554 ; European Union's Horizon 2020 research and innovation program 826078 965221 ; Spanish Government Juan de la Cierva Incorporacion IJC2019-039152-I ; DFF Sapere Aude research leader grant ; Basque Government KK-2020/00049 ; consolidated research group MATHMODE T1294-19 ; Federal Ministry of Education & Research (BMBF) 01IS18025 A 01IS18037I 0310L0207C ; Ontario Research Fund RDI 34876 ; Natural Sciences Research Council NSERC 203475 ; Canadian Institutes of Health Research (CIHR) 93579 ; Canada Foundation for Innovation CGIAR CFI 29272 225404 33536 ; International Business Machines (IBM) ; Ian Lawson van Toch Fund ; Schroeder Arthritis Institute via the Toronto General and Western Hospital Foundation

This research was funded by the French ANRT (Association Nationale Recherche Technologie - ANRT) industrial Cifre PhD contract with SEGULA Technologies, the Andalusian Excellence project P18FR-4961 and the Spanish National Project PID2020-119478GB-I00. S. Tabik was supported by the Ramon y Cajal Programme (RYC-201518136). N. Diaz-Rodriguez is currently supported by the Spanish Government Juan de la Cierva Incorporacion contract (IJC2019-039152-I). ; The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience such as end-users or domain experts. In contrast, symbolic AI systems that convert concepts into rules or symbols – such as knowledge graphs – are easier to explain. However, they present lower generalization and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. The ultimate objective is to fuse DL representations with expert domain knowledge during the learning process so it serves as a sound basis for explainability. In particular, X-NeSyL methodology involves the concrete use of two notions of explanation, both at inference and training time respectively: (1) EXPLANet: Expert-aligned eXplainable Part-based cLAssifier NETwork Architecture, a compositional convolutional neural network that makes use of symbolic representations, and (2) SHAP-Backprop, an explainable AI-informed training procedure that corrects and guides the DL process to align with such symbolic representations in form of knowledge graphs. We showcase X-NeSyL methodology using MonuMAI dataset for monument facade image classification, and demonstrate that with our approach, it is possible to improve explainability at the same time as performance. ; French National Research Agency (ANR) ; SEGULA Technologies ; Andalusian Excellence project P18FR-4961 ; Spanish National Project PID2020-119478GB-I00 ; Spanish Government RYC-201518136 ; Spanish Government Juan de la Cierva Incorporacion contract IJC2019-039152-I

Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant. ; Andreas Holzinger acknowledges funding support from the Austrian Science Fund (FWF), Project: P-32554 explainable Artificial Intelligenceand from the European Union's Horizon 2020 research and innovationprogram under grant agreement 826078 (Feature Cloud). This publi-cation reflects only the authors' view and the European Commissionis not responsible for any use that may be made of the informationit contains; Natalia Díaz-Rodríguez is supported by the Spanish Gov-ernment Juan de la Cierva Incorporación contract (IJC2019-039152-I); Isabelle Augenstein's research is partially funded by a DFF Sapere Auderesearch leader grant; Javier Del Ser acknowledges funding supportfrom the Basque Government through the ELKARTEK program (3KIAproject, KK-2020/00049) and the consolidated research group MATH-MODE (ref. T1294-19); Wojciech Samek acknowledges funding Support from the European Union's Horizon 2020 research and innovationprogram under grant agreement No. 965221 (iToBoS), and the German Federal Ministry of Education and Research (ref. 01IS18025 A, ref. 01IS18037I and ref. 0310L0207C); Igor Jurisica acknowledges funding support from Ontario Research Fund (RDI 34876), Natural Sciences Research Council (NSERC 203475), CIHR Research Grant (93579),Canada Foundation for Innovation (CFI 29272, 225404, 33536), IBM, Ian Lawson van Toch Fund, the Schroeder Arthritis Institute via theToronto General and Western Hospital Foundation.