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

Part 3: MAKE Privacy ; International audience ; Exponential trends in data generation are presenting today's organizations, economies and governments with challenges never encountered before, especially in the field of privacy and data security. One crucial trade-off regulators are facing regards the simultaneous need for publishing personal information for the sake of statistical analysis and Machine Learning in order to increase quality levels in areas like medical services, while at the same time protecting the identity of individuals. A key European measure will be the introduction of the General Data Protection Regulation (GDPR) in 2018, giving customers the 'right to be forgotten', i.e. having their data deleted on request. As this could lead to a competitive disadvantage for European companies, it is important to understand which effects deletion of significant data points has on the performance of ML techniques. In a previous paper we introduced a series of experiments applying different algorithms to a binary classification problem under anonymization as well as perturbation. In this paper we extend those experiments by multi-class classification and introduce outlier-removal as an additional scenario. While the results of our previous work were mostly in-line with our expectations, our current experiments revealed unexpected behavior over a range of different scenarios. A surprising conclusion of those experiments is the fact that classification on an anonymized dataset with outliers removed in beforehand can almost compete with classification on the original, un-anonymized dataset. This could soon lead to competitive Machine Learning pipelines on anonymized datasets for real-world usage in the marketplace.

International audience ; In this short editorial we present some thoughts on present and future trends in Artificial Intelligence (AI) generally, and Machine Learning (ML) specifically. Due to the huge ongoing success in machine learning, particularly in statistical learning from big data, there is rising interest of academia, industry and the public in this field. Industry is investing heavily in AI, and spin-offs and start-ups are emerging on an unprecedented rate. The European Union is allocating a lot of additional funding into AI research grants, and various institutions are calling for a joint European AI research institute. Even universities are taking AI/ML into their curricula and strategic plans. Finally, even the people on the street talk about it, and if grandma knows what her grandson is doing in his new start-up, then the time is ripe: We are reaching a new AI spring. However, as fantastic current approaches seem to be, there are still huge problems to be solved: the best performing models lack transparency, hence are considered to be black boxes. The general and worldwide trends in privacy, data protection, safety and security make such black box solutions difficult to use in practice. Specifically in Europe, where the new General Data Protection Regulation (GDPR) came into effect on May, 28, 2018 which affects everybody (right of explanation). Consequently, a previous niche field for many years, explainable AI, explodes in importance. For the future, we envision a fruitful marriage between classic logical approaches (ontologies) with statistical approaches which may lead to context-adaptive systems (stochastic ontologies) that might work similar as the human brain.

The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like medicine and law. The formulation of methods to augment the construction of predictive models with domain knowledge can provide support for producing human understandable explanations for predictions. This runs in parallel with AI regulatory concerns, like the European Union General Data Protection Regulation, which sets standards for the production of explanations from automated or semi-automated decision making. Despite the fact that all this research activity is the growing acknowledgement that the topic of explainability is essential, it is important to recall that it is also among the oldest fields of computer science. In fact, early AI was re-traceable, interpretable, thus understandable by and explainable to humans. The goal of this research is to articulate the big picture ideas and their role in advancing the development of XAI systems, to acknowledge their historical roots, and to emphasise the biggest challenges to moving forward.

Intro -- Preface -- Organization -- Contents -- Invited Papers -- The Community Assessment of Risk and Treatment Strategies (CARTS): An Integrated Care Pathway to Manage Frailty and Functional Decline in Community Dwelling Older Adults -- Abstract -- 1 Introduction -- 2 The Community Assessment of Risk and Treatment Strategies (CARTS) -- 3 Impact of the CARTS Programme -- 3.1 Older Adults -- 3.2 Healthcare Providers -- 3.3 Healthcare Systems -- 4 External Validation of the CARTS ICP -- 4.1 Portugal -- 4.2 Spain -- 4.3 Australia -- 5 Challenges and Solutions for Scaling-up CARTS -- 6 Conclusion -- 7 Future Development of the CARTS Project -- References -- Advancing Ambient Assisted Living with Caution -- 1 Introduction -- 2 Scenario -- 3 State of the Art in Ambient Assisted Living -- 3.1 Ambient Assisted Living -- 3.2 Assisted Healthcare -- 3.3 Medication Support -- 4 On the Horizon -- 4.1 Potential Problems -- 4.2 Potential Solutions -- 4.3 Tradeoffs -- 4.4 Legal Aspects to Consider -- 5 One Possible Future -- 6 Conclusion -- References -- Ambient Assisted Living -- Ambient Technology to Support Elderly People in Outdoor Risk Situations -- Abstract -- 1 Introduction -- 2 Methodology -- 3 Major Risks in Outdoor Environment -- 3.1 Fall -- 3.2 Wandering and Disorientation -- 3.3 Health Issues -- 3.4 Traffic Accidents Risk -- 3.5 Fatigue Risk -- 3.6 Mistreatment, Abuse and Criminal Actions -- 3.7 Psychological Related Risks -- 4 Assistive Technologies for Major Outdoor Risks -- 4.1 Assistive Technology for Fall Risk -- 4.2 Assistive Technology for Wandering Risk -- 4.3 Assistive Technology for Health Issues (M-Health) -- 4.4 Assistive Technologies for Other Risks in Outdoor Environment -- 4.4.1 AT for Traffic Accidents -- 4.4.2 AT for Fatigue Risk -- 4.4.3 AT for Crime Risk -- 4.4.4 AT for Psychological Related Risks.

Abstract
Purpose
This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance. Our analysis highlights why researchers, practitioners and especially policymakers, should pay attention to this phenomenon.

Methods
The paper examines the ways in which governments and public agencies are addressing the underuse of AI. As governments and international organizations often acknowledge the limitations of their own initiatives, the contribution explores the causes of the current issues and suggests ways to improve initiatives for digital health.

Results
Recommendations address the development of standards, models of regulatory governance, assessment of the opportunity costs of underuse of technology, and the urgency of the problem.

Conclusions
The exponential pace of AI advances and innovations makes the risks of underuse of AI increasingly threatening.

Graphical Abstract

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.