The latest developments in the field of Artificial Intelligence (AI) have given rise to many ethical and socioeconomic concerns. Nonetheless, the impact of AI technologies is evident and tangible in our everyday life. This dichotomy leads to mixed feelings towards AI: people recognize the positive impact of AI, but they also show concerns, especially about their privacy and security.In this paper, we try to understand whether the implicit and explicit attitudes towards AI are coherent. We investigated explicit and implicit attitudes towards AI by combining a self-report measure and an implicit measure, i.e., the Implicit Association Test. We analysed the explicit and implicit responses of 829 participants. Results revealed that while most of the participants explicitly express a positive attitude towards 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 towards AI.
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.
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