Are some brain injury patients improving more than ohers?

Predicting the evolution of individuals is a rather new mining task with applications in medicine. Medical researchers are interested in the progress of a disease and in the evolution of individuals subjected to treatment. We investigate the evolution of patients on the basis of medical tests before and during treatment after brain trauma: we want to understand how similar patients can become to healthy participants. We face two challenges. First, we have less information on healthy participants than on the patients. Second, the values of the medical tests for patients, even after treatment started, remain well-separated from those of healthy people; this is typical for neurodegenerative diseases, but also for further brain impairments. Our approach encompasses methods for modelling patient evolution and for predicting the health improvement of different patient subpopulations, dealing with the above challenges. We test our approach on a cohort of patients treated after brain trauma and a corresponding cohort of controls

Web Futures: Inclusive, Intelligent, Sustainable The 2020 Manifesto for Web Science

International audienceThis Manifesto was produced from the Perspectives Workshop 18262 entitled "10 Years of Web Science" that took place at Schloss Dagstuhl from June 24-29, 2018. At the Workshop, we revisited the origins of Web Science, explored the challenges and opportunities of the Web, and looked ahead to potential futures for both the Web and Web Science. We explain issues that society faces in the Web by the ambivalences that are inherent in the Web. All the enormous benefits that the Web offers-for information sharing, collective organization and distributed activity, social inclusion and economic growth-will always carry along negative consequences, too, and 30 years after its creation negative consequences of the Web are only too apparent. The Web continues to evolve and its next major step will involve Artificial Intelligence (AI) at large. AI has the potential to amplify positive and negative outcomes, and we explore these possibilities, situating them within the wider debate about the future of regulation and governance for the Web. Finally, we outline the need to extend Web Science as the science that is devoted to the analysis and engineering of the Web, to strengthen our role in shaping the future of the Web and present five key directions for capacity building that are necessary to achieve this: (i), supporting interdisciplinarity, (ii), supporting collaboration, (iii), supporting the sustainable Web, (iv), supporting the Intelligent Web, and (v), supporting the Inclusive Web. Our writing reflects our background in several disciplines of the social and technical sciences and that these disciplines emphasize topics to various extents. We are acutely aware that our observations occupy a particular point in time and are skewed towards our experience as Western scholars-a limitation that Web Science will need to overcome

Bias in data-driven artificial intelligence systems—An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Bias in data-driven artificial intelligence systems - An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

The horse in European history, 1550-1900

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Towards Trajectory Data Warehouses

Data warehouses have received the attention of the database community as a technology for integrating all sorts of transactional data, dispersed within organisations whose applications utilise either legacy (non-relational) or advanced relational database systems. Data warehouses form a technological framework for supporting decision-making processes by providing informational data. A data warehouse is defined as a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management of decision-making process

Towards Trajectory Data Warehouses

Data warehouses have received the attention of the database community as a technology for integrating all sorts of transactional data, dispersed within organisations whose applications utilise either legacy (non-relational) or advanced relational database systems. Data warehouses form a technological framework for supporting decision-making processes by providing informational data. A data warehouse is defined as a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management of decision-making process