Exposure to false information is becoming a common occurrence in our daily lives. New developments in artificial intelligence are now used to produce increasingly sophisticated multimedia false content, such as deepfakes, making false information even more challenging to detect and combat. This creates expansive opportunities to mislead individuals into believing fabricated claims and negatively influence their attitudes and behavior. Therefore, a better understanding of how individuals perceive such content and the variables related to the perceived trustworthiness of deepfakes is needed. In the present study, we developed and validated the Perceived Deepfake Trustworthiness Questionnaire (PDTQ) in English, Italian, and Slovene. This was done in three phases. First, we developed the initial pool of items by reviewing previous studies, generating items via interviews and surveys, and employing artificial intelligence. Second, we shortened and adapted the questionnaire according to experts’ evaluation of content validity and translated the questionnaire into Italian and Slovene. Lastly, we evaluated the psychometric characteristics via a cross-sectional study in three languages (N = 733). The exploratory factor analyses suggested a two-factor solution, with the first factor measuring the perceived trustworthiness of the content and the second measuring the perceived trustworthiness of the presentation. This factorial structure was replicated in confirmatory factor analyses. Moreover, our analyses provided support for PDTQ’s reliability, measurement invariance across all three languages, and its construct and incremental validity. As such, the PDTQ is a reliable, measurement invariant, and valid tool for comprehensive exploration of individuals’ perception of deepfake videos.

This paper proposes a formal account of AI trustworthiness, connecting both intrinsic and perceived trustworthiness in an operational schematization. We argue that trustworthiness extends beyond the inherent capabilities of an AI system to include significant influences from observers' perceptions, such as perceived transparency, agency locus, and human oversight. While the concept of perceived trustworthiness is discussed in the literature, few attempts have been made to connect it with the intrinsic trustworthiness of AI systems. Our analysis introduces a novel schematization to quantify trustworthiness by assessing the discrepancies between expected and observed behaviors and how these affect perceived uncertainty and trust. The paper provides a formalization for measuring trustworthiness, taking into account both perceived and intrinsic characteristics. By detailing the factors that influence trust, this study aims to foster more ethical and widely accepted AI technologies, ensuring they meet both functional and ethical criteria.

The advent of the so-called Companion Robots is raising many ethical concerns among scholars and in the public opinion. Focusing mainly on robots caring for the elderly, in this paper we analyze these concerns to distinguish which are directly ascribable to robotic, and which are instead pre-existent. One of these is the "deception objection", namely the ethical unacceptability of deceiving the user about the simulated nature of the robot's behaviors. We argue on the inconsistency of this charge, as today formulated. After that, we underline the risk, for human-robot interaction, to become a hallucinatory relation where the human would subjectify the robot in a dynamic of meaning-overload. Finally, we analyze the definition of "quasi-other" relating to the notion of "uncanny". The goal of this paper is to argue that the main concern about Companion Robots is the simulation of a human-like interaction in the absence of an autonomous robotic horizon of meaning. In addition, that absence could lead the human to build a hallucinatory reality based on the relation with the robot.