RESUMEN
Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are "weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.
RESUMEN
The application of e-Science technologies to disciplines in the arts and humanities raises major questions as to how those technologies can be most usefully exploited, what tools and infrastructures are needed for that exploitation, and what new research approaches can be generated. This paper reviews a number of activities in the UK and Europe in the last 5 years which have sought to address these questions through processes of experimentation and targeted infrastructure development. In the UK, the AHeSSC (Arts and Humanities e-Science Support Centre) has played a coordinating role for seven projects funded by the Arts and Humanities e-Science Initiative. In Europe, DARIAH (Digital Research Infrastructure for the Arts and Humanities) has sought to develop a deeper understanding of research information and communication in the arts and humanities, and to inform the development of e-infrastructures accordingly. Both sets of activity have indicated a common requirement: to construct a framework which consistently describes the methods and functions of scholarly activity which underlie digital arts and humanities research, and the relationships between them. Such a 'methodological commons' has been formulated in the field of the digital humanities. This paper describes the application of this approach to arts and humanities e-Science, with reference to the early work of DARIAH and AHeSSC.
RESUMEN
Several virtual research environments (VREs) have been developed to address specific tasks or application domains. Building on the experiences and use cases coming out of these projects, this paper addresses the creation of more general-purpose VREs for the humanities, which move beyond specific, focused tasks, and instead provide services and environments that support more general-purpose humanities research activities. Specifically, we are investigating use cases related to the organization and integration of the dispersed and heterogeneous information on which such research is based. These use cases are highly interactive, interpretative and researcher centric, addressing topics such as annotation environments and support for 'active-reading' processes and scholarly dialogues. We present the background to our work and the technical approach taken, and report the results obtained so far.
