The democratization of artificial intelligence (AI) involves extending access to AI technologies beyond specialized technical experts to a broader spectrum of users and organizations. This paper provides an overview of AI’s historical context and evolution, emphasizing the concept of AI democratization. Current trends shaping AI democratization are analyzed, highlighting key challenges and opportunities. The roles of pivotal stakeholders, including technology firms, educational entities, and governmental bodies, are examined in facilitating widespread AI adoption. A comprehensive framework elucidates the components, drivers, challenges, and strategies crucial to AI democratization. This framework is subsequently applied in the context of scenario analyses, offering insights into potential outcomes and implications. The paper concludes with recommendations for future research directions and strategic actions to foster responsible and inclusive AI development globally.

The ongoing COVID-19 pandemic is creating disruptive changes in urban mobility that may compromise the sustainability of the public transportation system. As a result, worldwide cities face the need to integrate data from different transportation modes to dynamically respond to changing conditions. This article combines statistical views with machine learning advances to comprehensively explore changing urban mobility dynamics within multimodal public transportation systems from user trip records. In particular, we retrieve discriminative traffic patterns with order-preserving coherence to model disruptions to demand expectations across geographies and show their utility to describe changing mobility dynamics with strict guarantees of statistical significance, interpretability and actionability. This methodology is applied to comprehensively trace the changes to the urban mobility patterns in the Lisbon city brought by the current COVID-19 pandemic. To this end, we consider passenger trip data gathered from the three major public transportation modes: subway, bus, and tramways. The gathered results comprehensively reveal novel travel patterns within the city, such as imbalanced demand distribution towards the city peripheries, going far beyond simplistic localized changes to the magnitude of traffic demand. This work offers a novel methodological contribution with a solid statistical ground for the spatiotemporal assessment of actionable mobility changes and provides essential insights for other cities and public transport operators facing mobility challenges alike.

This study aims to contribute to more sustainable mobility solutions by proposing robust and actionable methods to assess the resilience of a multimodal transport system. Resilience is seen in a dynamic lean setting, looking at aspects in the network topology and user’s flow and demand throughout a parameterizable period. We hypothesize that this network’s appropriate multi-layered and traffic-sensitive modeling can promote the integrated analysis of different transport modes and support an improved resilience analysis. We operationalize the lean resilience conceptual construct with the proposed muLtImodal traNsportation rEsilience aSsessment (LINES) methodological process. Using the city of Lisbon as a study case, we illustrate the relevance of the proposed methodology to detect actionable vulnerabilities in the bus–tram–subway network.

Gamification is applied in academia and industry and is being studied in the last years. Gamification is nowadays applied to several areas, such as learning, computer science, business, marketing, tourism, among others. However, we need to understand to what extent it is studied in academic context. We present a bibliographic study on gamification research, by identifying publications trends. This paper presents the gamification related areas, result from a publication keyword analysis of the scientific publications on gamification. In this paper, we also identify the game elements, game mechanics, and game dynamics, used as part of any gamification implementation. In this study are also present the most used gamification theoretical models.

Machine Learning is widely used by practitioners to solve complex challenges. However, despite being trusted by 76% of the public, scientists struggle to explain the rationale behind machine learning-based decisions. This is concerning because research has shown that people often rely on inaccurate machine learning recommendations, even when the system is not confident or they have prior knowledge. To address these issues, there is a crucial need for greater transparency and education around machine learning decision making. In this work, we propose a set of guidelines and design implications to communicate eXplainable Artificial Intelligence models to the general audience. We do this through a literature review of the latest and eXplainable Artificial Intelligence methods and validate these insights through a user study encompassing the participants’ interpretations of the eXplainable Artificial Intelligence solutions. Combining the insights from this mixed-method study, we identify seven main communication guidelines for improving machine learning models understanding. This study contributes to the broader discussion of ethical implications surrounding opaque machine learning models in decision-making. Through the development of guidelines, we hope to bridge the gap between machine learning experts and the public, enabling a better common understanding of its increasing importance in our lives.