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Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission, the fifth requirement (“Diversity, non-discrimination and fairness”) declares: “In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system’s life cycle. [...] This requirement is closely linked with the principle of fairness”. In this paper, we try to shed light on how closely these two distinct concepts, diversity and fairness, may be treated by focusing on information access systems and ranking literature. © 2023 Copyright for this paper by its authors.

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This paper analyses the causal relationship between external events and sports content TV audiences. To accomplish this, we explored external data related to sports TV audience behaviour within a specific time frame and applied a Granger causality analysis to evaluate the effect of external events on both TV clients' volume and viewing times. Compared to regression studies, Granger causality analysis is essential in this research as it provides a more comprehensive and accurate understanding of the causal relationship between external events and sports TV viewership. The study results demonstrate a significant impact of external events on the TV clients' volume and viewing times. External events such as the type of tournament, match popularity, interest and home team effect proved to be the most informative about the audiences. The findings of this study can assist TV distributors in making informed decisions about promoting sports broadcasts.

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Several systems that employ machine learning models are subject to strict latency requirements. Fraud detection systems, transportation control systems, network traffic analysis and footwear manufacturing processes are a few examples. These requirements are imposed at inference time, when the model is queried. However, it is not trivial how to adjust model architecture and hyperparameters in order to obtain a good trade-off between predictive ability and inference time. This paper provides a contribution in this direction by presenting a study of how different architectural and hyperparameter choices affect the inference time of a Convolutional Neural Network for network traffic analysis. Our case study focus on a model for traffic correlation attacks to the Tor network, that requires the correlation of a large volume of network flows in a short amount of time. Our findings suggest that hyperparameters related to convolution operations-such as stride, and the number of filters-and the reduction of convolution and max-pooling layers can substantially reduce inference time, often with a relatively small cost in predictive performance.

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In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify's Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.