The EU-funded CREXDATA project plans to develop a platform for real-time critical situation management, including flexible action planning and agile decision-making. CREXDATA will produce the algorithmic apparatus, software architecture and tools for gathering federated predictive analytics and forecasting in the face of uncertainty. The proposed framework will facilitate proactive decision-making, by providing highly accurate and transparent short- and long-term forecasts, explainable via advanced visual analytics and accurate, real-time, augmented reality facilities.
The recent wave of machine-learning (ML) based Artificial-Intelligence (AI) technologies is having a huge societal and economic impact, with AI being (often silently) embedded in most of our everyday experiences (such as virtual assistants, tracking devices, social media, recommender systems). The research community (and society in general) has already realised that the current centralised approach to AI, whereby our personal data are centrally collected and processed through opaque ML systems (“black-boxes”), is not an acceptable and sustainable model in the long run.
Systems based on artificial intelligence (AI) are increasingly being used in applications automatically issuing decisions or assessments. They can impact individuals or groups of people with regard to important questions like payments or medical treatment but AI bias can be an issue. The sources of biases of AI decisions can be automatically derived data; algorithms processing data; or use of applications. To eliminate AI biases on all of three stages, the EU-funded NoBIAS project will develop fairness-aware algorithms.
Migration represents a constantly rising social and political concern for many governments. As a result, a better understanding of the drivers and dynamics of migration is important to ensure effective and successful migration governance. The EU-funded HumMingBird project intends to study the origins of migration and their interrelation with the tendency of people to emigrate. In this effort, the role migration data play is instrumental. Data will provide key information concerning drivers, geography, incentives and instruments related to the migration movements.
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 EU-funded HumanE-AI-Net project brings together leading European research centres, universities and industrial enterprises into a network of centres of excellence. Leading global artificial intelligence (AI) laboratories will collaborate with key players in areas, such as human-computer interaction, cognitive, social and complexity sciences. The project is looking forward to drive researchers out of their narrowly focused field and connect them with people exploring AI on a much wider scale.
Maximising opportunities and minimising risks associated with artificial intelligence (AI) requires a focus on human-centred trustworthy AI. This can be achieved by collaborations between research excellence centres with a technical focus on combining expertise in theareas of learning, optimisation and reasoning. Currently, this work is carried out by an isolated scientific community where research groups are working individually or in smaller networks.
SoBigData++ strives to deliver a distributed, Pan-European, multi-disciplinary research infrastructure for big social data analytics, coupled with the consolidation of a cross-disciplinary European research community, aimed at using social mining and big data to understand the complexity of our contemporary, globally-interconnected society. SoBigData++ is set to advance on such ambitious tasks thanks to SoBigData, the predecessor project that started this construction in 2015.
Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions.
The Humane AI Flagship will develop the scientific foundations and technological breakthroughs needed to shape the ongoing artificial intelligence (AI) revolution.
