The emergence of data science has raised a wide range of concerns regarding its compatibility with the law, creating the need for experts who combine a deep knowledge of both data science and legal matters. The EU-funded LeADS project will train early-stage researchers to become legality attentive data scientists (LeADS), the new interdisciplinary profession aiming to address the aforementioned need. These scientists will be experts in both data science and law, able to maintain innovative solutions within the realm of law and help expand the legal frontiers according to innovation needs. The project will create the theoretical framework and the practical implementation template of a common language for co-processing and joint-controlling basic notions for both data scientists and jurists. LeADS will also produce a comparative and interdisciplinary lexicon.
As part of this project, VUB and LSTS are looking for two motivated researchers to cover the positions of ESR7 and ESR13.
The project will be developed within a research line contributing to bridging the gap between statistical methodologies at the basis of (supervised, unsupervised) Machine Learning and Logic in the development of AI. We aim to develop Logics for Reasoning under Uncertainty and with limited resources to analyse and check transparency and trustworthiness of AI systems. Properties of interest include but are not limited to: Causality, Safety and Fairness.
The selected candidate will join a thriving research group based at the Department of Philosophy at the University of Milan, and will be working under the joint supervision of Marcello D’Agostino and Giuseppe Primiero.
Pathology labs are undergoing an unprecedented and rapid transformation. Whole slide imaging (WSI) of histopathological specimen in combination with deep machine learning/artificial intelligence methods have accelerated the field of Computational Pathology that aims to augment, automate or improve a number of tasks that are currently performed by medical experts. Nevertheless, many challenges relating to application of artificial intelligence methods in Computational Pathology remain open.
This research project focuses on developing of novel deep machine learning algorithms/artificial intelligence methods for analysis of whole slide histopathology images. More specifically, you will develop methods that enable end-to-end training of deep machine learning models using large (up to 100,000-by-100,000 pixels) whole slide images as input. Particular attention will be put on explainable models that can give insight into the workings of the model and communicate that information to clinical experts. The developed methods will be applied to a variety of clinical applications with a particular focus on oncology.
The project will be supervised by Dr. Mitko Veta, Assistant Professor in Medical Image Analysis at TU/e Department of Biomedical Engineering. During the project you will closely collaborate with clinicians and researchers of the Department of Pathology, University Medical Center Utrecht. The PhD project is part of a large European consortium combining leading European research centers, hospitals as well as major pharmaceutical industries.
There is a strong desire in the SSH field to assign part of the resources intended for the SSH Sector Plan of the Ministry of Education, Culture and Science (OCW) to a domain wide digital SSH plan. For this purpose, the SSH Council – which represents the SSH field – initiated the Platform for Digital Infrastructure for SSH (PDI-SSH). The platform is responsible for allocating resources to digital infrastructure facilities within the SSH domain, for coordinating digital infrastructures in the SSH domain and for strategy within that domain.
PDI-SSH launches this second Call for proposals as part of the SSH Sector Plan.
Purpose of the Call
The resources of the Ministry of OCW for the SSH Sector Plan are partly targeted at a domain-wide digital infrastructure. The main objective of the domain-wide plan for digital SSH is to strengthen research and infrastructure in the field of digitisation.
The following objectives have been established: Increase and strengthen interdisciplinary SSH research into digitisation and the relevant social and scientific developments. Strengthen digital infrastructure facilities and their management within the SSH. Increase interdisciplinary collaboration between the social sciences and the humanities in the field of digitisation research and infrastructure. Ensure continuity of current initiatives within the SSH in the field of digitisation research and infrastructure. Increase the strategic capacity and strengthen the organisation of the SSH in the field of digitisation. Attract and retain SSH research talents who possess interdisciplinary expertise in the field of digitisation.
Allocating resources to digital infrastructure facilities within the SSH domain can involve both strengthening or scaling up existing initiatives and launching new initiatives. The funds in the second Call should be used for structural financing of digital infrastructure facilities serving both the social sciences and the humanities.
Current debates on artificial intelligence often conflate the realities of AI technologies with the fictional renditions of what they might one day become. They are said to be able to learn, make autonomous decisions or process information much faster than humans, which raises hopes and fears alike. What if these useful technologies will one day develop their own intentions that run contrary to those of humans?
The line between science and fiction is becoming increasingly blurry: what is already a fact, what is still only imagination; and is it even possible to make this clear-cut distinction? Innovation and development goals in the field of AI are inspired by popular culture, such as its portrayal in literature, comics, film or television. At the same time, images of these technologies drive discussions and set particular priorities in politics, business, journalism, religion, civil society, ethics or research. Fictions, potentials and scenarios inform a society about the hopes, risks, solutions and expectations associated with new technologies. But what is more, the discourses on AI, robots and intelligent, even sentient machines are nothing short of a mirror of the human condition: they renew fundamental questions on concepts such as consciousness, free will and autonomy or the ways we humans think, act and feel.
Imaginations about the human and technologies are far from universal, they are culturally specific. This is why a cross-cultural comparison is crucial for better understanding the relationship between AI and the human and how they are mutually constructed by uncovering those aspects that are regarded as natural, normal or given. Focusing on concepts, representations and narratives from different cultures, the conference aims to address two axes of comparison that help us make sense of the diverse realities of artificial intelligence and the ideas of what is human: Science and fiction, East Asia and the West.
Papers are invited on the following topics (among others): - Which meanings and functions are ascribed to AI technologies and robots? - How is science informed by popular discursive images of AI? - Which cultural differences are there concerning the relationship between the natural and the artificial? What are the particular traditions of how to represent the human and its technological surrogates? - What can the different cultural and conceptual histories tell us about our present and future with artificial intelligence?