Accessible Cultural Heritage through Explainable Artificial Intelligence

International audienceEthics Guidelines for Trustworthy AI advocate for AI technology that is, among other things, more inclusive. Explainable AI (XAI) aims at making state of the art opaque models more transparent, and defends AI-based outcomes endorsed with a rationale explanation, i.e., an explanation that has as target the non-technical users. XAI and Responsible AI principles defend the fact that the audience expertise should be included in the evaluation of explainable AI systems. However, AI has not yet reached all public and audiences , some of which may need it the most. One example of domain where accessibility has not much been influenced by the latest AI advances is cultural heritage. We propose including minorities as special user and evaluator of the latest XAI techniques. In order to define catalytic scenarios for collaboration and improved user experience, we pose some challenges and research questions yet to address by the latest AI models likely to be involved in such synergy

Big data and IoT for chronic patients monitoring

Developed countries are characterized by aging population and economical crisis, so it is desirable to reduce the costs of public and private healthcare systems. It is necessary to streamline the health system resources leading to the development of new medical services based on telemedicine, remote monitoring of chronic patients, personalized health services, new services for dependants, etc. New medical applications based on remote monitoring will significantly increasing the volume of health information to manage, including data from medical and biological sensors, is then necessary process this huge volume of data using techniques from Big Data. In this paper we propose one potential solution for creating those new services, based on Big Data processing and vital signs monitoring.Ministerio de Industria, Turismo y Comercio (TSI-020100-2011-83); Ministerio de Ciencia e Innovación (TIN-2009-14057-C03-01).0.339 SJR (2014) Q2, 102/234 Computer science (miscellaneous); Q4, 94/120 Theoretical computer scienceUE