With the growing influence of artificial intelligence (AI) in our lives, the ethical implications of AI have received attention from various communities. Building on previous work on trust in people and technology, we advance a multidimensional, multilevel conceptualization of trust in AI and examine the relationship between trust and ethics using the data from a survey of a national sample in the U.S. This paper offers two key dimensions of trust in AI—human-like trust and functionality trust—and presents a multilevel conceptualization (...) of trust with dispositional, institutional, and experiential trust each significantly correlated with trust dimensions. Along with trust in AI, we examine perceptions of the importance of seven ethics requirements of AI offered by the European Commission’s High-Level Expert Group. Then the association between ethics requirements and trust is evaluated through regression analysis. Findings suggest that the ethical requirement of societal and environmental well-being is positively associated with human-like trust in AI. Accountability and technical robustness are two other ethical requirements, which are significantly associated with functionality trust in AI. Further, trust in AI was observed to be higher than trust in other institutions. Drawing from our findings, we offer a multidimensional framework of trust that is inspired by ethical values to ensure the acceptance of AI as a trustworthy technology. (shrink)

In this paper I argue that Artificial Intelligence and the many data science methods associated with it, such as machine learning and large language models, are first and foremost epistemic technologies. In order to establish this claim, I first argue that epistemic technologies can be conceptually and practically distinguished from other technologies in virtue of what they are designed for, what they do and how they do it. I then proceed to show that unlike other kinds of technology (_including_ other (...) epistemic technologies) AI can be uniquely positioned as an epistemic technology in that it is primarily designed, developed and deployed to be used in _epistemic contexts_ such as inquiry, it is specifically designed, developed and deployed to manipulate _epistemic content_ such as data, _and_ it is designed, developed and deployed to do so particularly through _epistemic operations_ such as prediction and analysis. As has been shown in recent work in the philosophy and ethics of AI (Alvarado, AI and Ethics, 2022a), understanding AI as an epistemic technology will also have significant implications for important debates regarding our relationship to AI technologies. This paper includes a brief overview of such implications, particularly those pertaining to explainability, opacity, trust and even epistemic harms related to AI technologies. (shrink)

Human organizations’ adoption of the paradigm of the Fourth Industrial Revolution is associated with the growth of techno-empowerment, which is the process of transferring autonomy in decision-making to intelligent machines. Particular persuasive strategies have been identified that may coax people to use intelligent devices. However, there is a substantial research gap regarding what antecedents influence human intention to assign decision-making autonomy to artificial agents. In this study, ethological and evolutionary concepts are applied to explain the drivers for autonomous assistants’ techno-empowerment. (...) The method used in the study was a 4 × 2 between-subject experiment made with 278 persons. The research tool used to collect the data was an online survey. The results show that more positive attitudes and higher trust, perceived usefulness, and perceived ease of use are correlated with higher intention to allow the autonomous assistant independence in decision-making. Second, the results suggest that the more human-like a non-human agent is, the higher the intention to empower it—but only if this agent simultaneously provides functional and visual anthropomorphic cues explainable by the mimicry effect. (shrink)

Responsible Artificial Intelligence (RAI) is a subset of the ethics associated with the use of artificial intelligence, which will only increase with the recent advent of new regulatory frameworks. However, if many firms have announced the establishment of AI governance rules, there is currently an important gap in understanding whether and why these announcements are being implemented or remain “decoupled” from operations. We assess how large global firms have so far implemented RAI, and the antecedents to RAI implementation across a (...) wide range of RAI initiatives. We find that the operationalization of RAI practices is scattered across firms, with only a fringe of companies extensively industrializing RAI. Social pressure pushes RAI design (”saying”) rather than implementation but the reverse is true for competitive pressure. AI capabilities as a bundle of data quality AI architecture, and talents are strongly associated with RAI design to scaling. (shrink)