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The increasing digitization and datification of all aspects of people's daily life, and the consequent growth in the use of personal data, are increasingly challenging the current development and adoption of Machine Learning (ML). First, the sheer complexity and amount of data available in these applications strongly demands for ML algorithms that can be trained directly on complex structures, which can be naturally described by graphs. In fact, graphs inherently capture information about entities, their attributes, and relationships between them. Directly applying ML to graphs relieves domain experts and data scientists from the challenging and time-consuming problem of designing a suitable vector-based data representation used by classical ML techniques. Second, ML algorithms should not only be designed to achieve high technical and functional standards; as the automated decisions provided by these algorithms can have a relevant impact on people's lives, their behavior has to be aligned with the values and principles of individuals and society. This demands for designing automated algorithms that we, as humans, can trust, fulfilling the requirements of fairness, robustness, privacy, and explainability. Third, designing effective ML algorithms requires skills and expertise developed at different levels. This substantially hinders the democratization and widespread availability of such technology for society at large, which in turn demands for improving the level of automatization and systematization of their design process, while also providing guarantees on their performance. For this reason, this paper provides an overview of the current works focused towards learning trustworthily, automatically, and with guarantees on graphs. [ABSTRACT FROM AUTHOR]

A substantial effort is underway to make robots useful outside controlled environments and without direct human supervision. In practice, this can happen only after a rigorous certification process which ensures that several requirements, e.g., operation safety, are met. Automated verification of control programs can be part of cost-effective methodologies to support certification, but it is widely recognized as difficult to attain in robotics, because of several traits that characterize robot design and implementation. One such trait is the widespread usage of middleware to implement control programs in a distributed fashion. In these cases, the challenge of verification is made steep mainly by the fact that the correctness of control software depends on middleware components whose structured models might not be available, or too difficult to obtain from their documentation. Our proposal is to ease the application of automated verification techniques by identifying abstract middleware models in the form of finite-state automata. The identification procedure is itself largely automated, and the only prerequisite is for the middleware to be available for controlled experimentation. Once middleware models are computed, behaviors that would lead to unsafe operation can be spotted automatically on a composition of identified middleware and control software models using model checking techniques. The approach is based on our tool AIDE – Automata IDentification Engine – to identify abstract middleware models, and the model checker SPIN to verify control units. To validate our approach, we consider four different case studies built on YARP publish–subscribe middleware. Our results confirm that AIDE enables the extension of precise engineering methods to distributed control software in robotics. [ABSTRACT FROM AUTHOR]