Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

As is the case for many innovative devices that require a novel user-technology interaction, the question\ud of future consumer acceptance is central for the correct utilization of Advanced Driver Assistance Systems\ud (ADAS). But since acceptance can be determined only after the direct use of the device, information that\ud aims to promote its use should focus on improving initial acceptability before use. Acceptability is the\ud attitude toward a new device before its use, and it can be influenced by information gathered about\ud the device. The aim of the present work is to explore the influence of the quality of information about\ud the device on the acceptability of ADAS. In particular, the paper explores how information about limitations\ud and levels of automation of a advanced collision warning system (CWS) affects initial acceptability\ud of naïve potential future buyers of cars equipped with Advanced Driver Assistance Systems.\ud A total of 527 novice drivers (both male and female) rated a CWS presented as either fully automated\ud with auto-brake or partially-automated and either with or without explicit mention of its use applicability\ud and limitations. Results show that the quantity and quality of information on device-specific features\ud can systematically change the initial acceptability of the safety device. Pleasantness of use and perceived\ud benefits for safety were found to be the most important factors for the novice drivers

Background: This paper, in the frame of social acceptance of renewable energies and innovative community-based production and consumption models, aims at supporting a data-driven approach able to deal with climate change and identify and quantify the psycho-sociological dimensions and factors that could support the transition from a technology-driven approach to a consumer-driven approach throughout the emerging “ prosumer business models”. In addition to the already existing Social Acceptance dimensions, this paper tries to identify a purely individual psychological fourth dimension to understand the processes and factors that underlie individual acceptance and adoption of new renewable energy business models, with the realization of a Prosumer Acceptance Index. Methods: Questionnaire data collection has been performed throughout an online survey platform, combining standardized and ad-hoc questions adapted for the research purposes, based on the developed theoretical model. To identify the main factors (individual/social) influencing the relation with renewable energy technology adoption, a Factorial Analysis has been conducted to identify the latent variables that are related to each other. Linear regression has been conducted to identify and quantify the factors that could better predict behavioural intention to become a prosumer. Results: Five latent psychological factors were revealed: concern about environmental issues, interest in energy sharing, concern on climate change, social influence and impact on bill cost. Three variables were found to significantly measure and predict the scores of the “Acceptance in becoming a prosumer” ad hoc scale: attitude, economic incentive and age. Conclusions: This research can facilitate policymakers and stakeholders to better understand which relevant psycho-sociological factors are intervening in the renewable energy technology acceptance processes and what and how specifically target when proposing change towards sustainable energy production and consumption.