In recent years, Augmented Reality (AR) technology has permeated various domains. This paper focuses on the critical aspect of enhancing customer interaction within AR e-commerce environments by investigating the impact of virtual product size manipulation on usability, user experience, and shopping satisfaction. We tested two manipulation modalities: an unconstrained scaling modality, enabling users to manually adjust product dimensions, and an assisted modality providing automatic 1:1 scaling. Using the Microsoft HoloLens 2 AR headset, we engaged 40 participants with small and large virtual products in shopping scenarios using these two manipulation modalities. Results show that users found the automatic manipulation modality to provide a superior user experience, being more effective, easy, useful, and pleasant when interacting with large virtual products. For small virtual products, they expressed a preference for free manipulation. Customer satisfaction with the shopping experience is positive, however, product size and manipulation modality affect the repatronage intention. The findings offer insights into designing AR e-commerce interfaces, highlighting that providing different manipulation modalities depending on the size of the products allows for enriching the shopping experience and improving the AR market potential.

Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values . The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders.

Recommender systems, in real-world circumstances, tend to limit user exposure to certain topics and to overexpose them to others to maximize performance. However, repeated exposure to biased content could lead to the so-called echo chamber phenomenon: especially in social network environments, people encounter only information that reflects their previous beliefs and opinions, reinforcing them. This phenomenon could have worrying consequences for society, including the spread of aggressive, unhealthy, or risky behaviors. Some persons can be more affected than others by echo-chambers. We define as sensitive the users whose behavior could be influenced by the over- or under-exposure to certain items due to the echo-chamber effect, and as influential the items that could influence the behavior of such users. In this paper, we address the problem of recommending influential items to sensitive users. We formalize the problem and propose three techniques that can be used to diversify the distributions of influential items in order to positively affect sensitive users’ behavior. Recommendations that meet this diversity criterion could potentially avoid dangerous societal consequences and simultaneously promote healthier lifestyles. We tested the proposed techniques in a real-world dataset by considering two different case studies that involved potentially aggressive and potentially depressed users. All techniques have been proven to be effective and allow high performance to be maintained while diversifying recommendations.

The identification of emotions conveyed by faces as genuine or not is a topic under-explored. While some controversial insights are available for happiness (where genuine happiness is supposed to create crow's feet around the eyes), nothing is known regarding other emotions. This topic is important as human beings are known to perform around the chance level to identify genuine emotions. It is thus pivotal to identify the relevant features for the correct identification of a facial emotion as genuine. To this aim, we capitalized on explainable artificial intelligence (XAI), characterized by a superior ability to detect subtle patterns in the data. Two different XAI models were created and applied to a dataset including 50 participants displaying both genuine and not genuine emotions. Results revealed that ML models achieved high accuracies in genuineness discrimination (78 % of average) while being robust and interpretable. The robustness of the results, ensured by their stability across different models and experimental conditions, is critical to improving the generalizability of the results. Our XAI algorithms provided interpretable results as they correctly identified facial muscle movements that are critical for the classification of emotions as genuine or fake.

Handwriting verification is an important task in the legal framework where the paternity of handwritten wills or con-tracts needs to be ascertained. Due to the lack of automated tools, the court currently relies exclusively on handwriting experts, who often show a wide degree of uncertainty in their responses. Machine learning models, and in particular artificial Neural Networks (NNs) might be a valuable aid to experts by providing an objective and automated instrument for handwriting analysis, especially when expert witnesses are undecided. However, at the state of the art there is a scarcity of datasets which are suitable for training NNs for the handwriting verification task, preventing the development of models accurate enough to be introduced into forensic practice. In this paper, a dataset of 3320 genuine and 3320 forged handwritten samples from 166 subjects is presented, considering two scenarios: copy (imitation of handwriting maintaining the same text, 3320 samples) and spontaneous production (imitation of handwriting generating a new text, 3320 samples). We provide baseline results for deep siamese convolutional neural networks, that are deep learning models widely adopted in similar tasks. In the proposed dataset, such NNs were able to reach accuracies over 75% in identifying forged samples in the copy scenario and of almost 82% in the spontaneous production scenario. Finally, a sample of 550 humans were tested in the same classification task. The experiments show that NNs perform significantly better than humans in the spontaneous production scenario, which is more complex than the copy one. We publicly release our dataset to encourage the future development of advanced Deep Learning models for the forged handwriting verification task.

The latest developments in the field of artificial intelligence (AI) have given rise to many ethical and socio-economic concerns. Nonetheless, the impact of AI technologies is evident and tangible in our everyday life. This dichotomy leads to mixed feelings toward AI: people recognize the positive impact of AI, but they also show concerns, especially about their privacy and security. In this article, we try to understand whether the implicit and explicit attitudes toward AI are coherent. We investigated explicit and implicit attitudes toward AI by combining a self-report measure and an implicit measure, i.e., the implicit association test. We analyzed the explicit and implicit responses of 829 participants. Results revealed that while most of the participants explicitly express a positive attitude toward AI, their implicit responses seem to point in the opposite direction. Results also show that, in both the explicit and implicit measures, females show a more negative attitude than males, and people who work in the field of AI are inclined to be positive toward AI.

The papers in this special section focus on the interaction with artificial intelligence (AI) systems using human-centered applications. AI methods are being applied to numerous areas, including medicine, security, transportation, industry, smart homes and cities, business, social sciences, and psychology. AI is currently a part of our daily lives. People interact continuously with AI: it is inside houses, computers, mobile phones, and applications. AI can make predictions and give suggestions for movies, songs, or future purchases based on our previous choices. It affects the society and economy. People are fascinated by AI in the ways it improves and facilitates human life (improving health care and discharging workers from heavy or dangerous jobs). People are also concerned with AI’s implementation risks, such as ethical, security, and privacy issues. There are also concerns that AI machines may replace humans in various activities. AI researchers and practitioners have been facing these issues and further research is needed to design technical and regulatory applicable solutions. This special issue (SI) investigates a broad range of issues deriving from human interaction with AI. We encouraged interdisciplinary and multidisciplinary contributions toward understanding how AI could improve human life in various fields.

Recently, the increasing spread of Online Social Networks (OSNs) provided an unprecedented opportunity of analysing online traces of human behaviour to get insight on individuals and society. Among the others, the possibility of predicting users' political orientation relying on data extracted from OSNs received growing attention. In this study, we introduce and make publicly available a dataset composed of 6.685 unique Twitter users and 9.593.055 Tweets. Differently from most of the dataset currently available in the literature, here, each user was manually labeled according to their political orientation by a pool of human judges, using strict inclusion criteria. Further, we address the feasibility of the automatic classification of Italian Twitter users' political orientation based on their Tweets content. Our analysis focuses first on implementing a series of classifiers with the aim of predicting users' political preference as right- or left-oriented. The built models were then evaluated for inferring the political orientation of those users supporting “Movimento 5 Stelle” (M5S), an Italian political party with a still unclear political leaning. Results show high performances on the left-right classification task, with accuracy rates up to 93%. Finally, classification performances obtained on M5S supporters and possible applications of our findings are discussed.

In this paper, we propose a novel method, based on keystroke dynamics, to distinguish between fake and truthful personal information written via a computer keyboard. Our method does not need any prior knowledge about the user who is providing data. To our knowledge, this is the first work that associates the typing human behavior with the production of lies regarding personal information. Via experimental analysis involving 190 subjects, we assess that this method is able to distinguish between truth and lies on specific types of autobiographical information, with an accuracy higher than 75%. Specifically, for information usually required in online registration forms (e.g., name, surname and email), the typing behavior diverged significantly between truthful or untruthful answers. According to our results, keystroke analysis could have a great potential in detecting the veracity of self-declared information, and it could be applied to a large number of practical scenarios requiring users to input personal data remotely via keyboard.