AI systems are expected to impact the ways we communicate, learn, and interact with technology. However, there are still major concerns about their commonsense reasoning, and personalization. This article computationally explains causal (vs. statistical) inference, at different levels of abstraction, and provides three examples of how we can use do-operator, a mathematical operator for intervention, to address some of these concerns. The first example is from an educational module that I developed and implemented for undergraduate engineering students, as part of (...) an educational research project with the US National Science Foundation. For the first time, to the best of my knowledge, 117 students could successfully use do-operator in a cybersecurity investment decision, according to Bloom’s learning taxonomy. Gender did not make a significant difference in the students’ performance, according to the Mann–Whitney U test. The second example explains using do-operator in assessing the effectiveness of intelligent tutoring systems, ITS, in receiving higher grades. The third example sheds light on combining online learning and offline learning, in reinforcement learning, to find the optimal policy that maximizes reward. To shed light on future research on explainability and personalization, I offer two recommendations: 1- Learn like System 2, the conscious learner (based on Bengio’s proposal for deep learning 2.0), and 2- Preference, a process, not an object (based on preference analysis of 25,646 registrants, entities and individuals purchasing domain names). In conclusion, this article contributes to achieving the goal of human–AI: Machines that think that learn and that create. (shrink)

The purpose of this study was to investigate the relationship between authentic leadership and the three dimensions of employee well-being. Furthermore, attachment insecurity was considered as a mediating factor between authentic leadership and the three dimensions of employee well-being. Data were obtained from a field sample of 212 health care providers with patient contact at five hospitals in the North East of Iran. Initially, collected data were analyzed with multiple confirmatory factor analyses. Then, structural equation modeling was applied to test (...) proposed hypotheses. First, it was shown that authentic leadership negatively impacted attachment insecurity. Second, attachment insecurity proved to be a factor impinging upon job satisfaction. On the contrary, higher levels of attachment insecurity was associated with higher levels of perceived stress and stress symptoms. Third, it was revealed that attachment insecurity partially mediated the relationship between authentic leadership and job satisfaction and fully mediated the relationship between authentic leadership, perceived stress, and stress symptoms. According to the literature of authentic leadership, this is one of the first research studies, and literally the first in the East exploring the effects of authentic leadership on the exclusive combination of dimensions offered in this paper. Moreover, researchers in the field of management have not delved enough into attachment and its antecedents and consequences in leader–follower relationship. This is one of the first studies to provide evidence of the relationship between authentic leadership, attachment security and employee well-being. As a further analysis, the final model was separately put under the two different lens of gender and some interesting findings were discussed in the discussion. (shrink)