Recommendation systems and assistants (from now on, recommenders) - algorithms suggesting items or providing solutions based on users' preferences or requests [99, 105, 141, 166] - influence through online platforms most actions of our day to day life. For example, recommendations on social media suggest new social connections, those on online retail platforms guide users' product choices, navigation services offer routes to desired destinations, and generative AI platforms produce content based on users' requests. Unlike other AI tools, such as medical diagnostic support systems, robotic vision systems, or autonomous driving, which assist in specific tasks or functions, recommenders are ubiquitous in online platforms, shaping our decisions and interactions instantly and profoundly. The influence recommenders exert on users' behaviour may generate long-lasting and often unintended effects on human-AI ecosystems [131], such as amplifying political radicalisation processes [82], increasing CO2 emissions in the environment [36] and amplifying inequality, biases and discriminations [120]. The interaction between humans and recommenders has been examined in various fields using different nomenclatures, research methods and datasets, often producing incongruent findings.

High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values. This kind of data requires extensive preprocessing, limiting the applicability of existing Time Series Classification and Time Series Extrinsic Regression techniques. For this reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates notions from time series convolution and 1D-SAX to handle univariate and multivariate time series with varying lengths and missing values. We evaluate BORF on Time Series Classification and Time Series Extrinsic Regression tasks using the full UEA and UCR repositories, demonstrating its competitive performance against state-of-the-art methods. Finally, we outline how this representation can naturally provide saliency and feature-based explanations.

This problem is of scientific and practical relevance since event data is common in many real-world scenarios and sparks interest in many fields including medicine, epidemiology, engineering, earth science, economics, finance, and social science. In medicine, events can represent various situations, such as incidents, test results, diagnoses and symptoms, and medications. The advent of wearable devices and apps also allows tracking human activities, such as eating, working, sleeping, traveling, etc. Events also characterize movement patterns such as trajectories or taxi/car/public transportation adoptions. In engineering, events can represent phenomena occurring in complex environments, such as failures occurring in industrial processes. In earth science, monitoring and modeling phenomena such as volcano eruptions, seismic events, or floods are of crucial importance.

Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems.

Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., carbon dioxide emissions and population exposure to pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.