Trust is widely regarded as a critical component to building artificial intelligence (AI) systems that people will use and safely rely upon. As research in this area continues to evolve, it becomes imperative that the research community synchronizes its empirical efforts and aligns on the path toward effective knowledge creation. To lay the groundwork toward achieving this objective, we performed a comprehensive bibliometric analysis, supplemented with a qualitative content analysis of over two decades of empirical research measuring trust in AI, (...) comprising 1’156 core articles and 36’306 cited articles across multiple disciplines. Our analysis reveals several “elephants in the room” pertaining to missing perspectives in global discussions on trust in AI, a lack of contextualized theoretical models and a reliance on exploratory methodologies. We highlight strategies for the empirical research community that are aimed at fostering an in-depth understanding of trust in AI. (shrink)

The primary aim of this paper is twofold: firstly, to argue that we can enter into relation of trust with robots and AI systems (automata); and secondly, to provide a comprehensive description of the underlying mechanisms responsible for this relation of trust. To achieve these objectives, the paper first undertakes a critical examination of the main arguments opposing the concept of a trust-based relation with automata. Showing that these arguments face significant challenges that render them untenable, it thereby prepares the (...) ground for the subsequent positive analysis, proposing a framework in which these challenges can be addressed. According to this framework trust does not originate from mere reliability, but rather from an empathic relation with automata. This initial empathic relation elevates the automata to the status of what I will term "Otheroids." The paper then explores how this human-Otheroid relationship inherently possesses the seeds for the development of trust. Finally, it examines how these seeds can grow into a basic form of trust with Otheroids through the establishment of a rich history of interaction. (shrink)

This paper examines the democratic ethics of artificially intelligent polls. Driven by machine learning, AI electoral polls have the potential to generate predictions with an unprecedented level of granularity. We argue that their predictive power is potentially desirable for electoral democracy. We do so by critically engaging with four objections: (1) the privacy objection, which focuses on the potential harm of the collection, storage, and publication of granular data about voting preferences; (2) the autonomy objection, which argues that polls are (...) an obstacle to independently formed judgments; (3) the tactical voting objection, which argues that voting strategically on the basis of polls is troublesome; and finally (4) the manipulation objection, according to which malicious actors could systematically bias predictions to alter voting behaviours. (shrink)