Experimental studies investigating the contribution of conscious intention to the generation of a sense of agency for one’s own actions tend to rely upon a narrow definition of intention. Often it is operationalized as the conscious sensation of wanting to move right before movement. Existing results and discussion are therefore missing crucial aspects of intentions, namely intention as the conscious sensation of wanting to move in advance of the movement. In the present experiment we used an intentional binding paradigm, in (...) which we distinguished between immediate intention, as usually investigated, and longer standing intention. The results showed that the binding effect was significantly enhanced for distal intentions compared to proximal intentions, indicating that the former leads to stronger sense of agency. Our finding provides empirical support for a crucial distinction between at least two types of intention when addressing the efficacy of conscious intentions. (shrink)

In this article, we suggest that the study of social interactions and the development of a “sense of agency” in joint action can help determine the content of relevant explanations to be implemented in artificial systems to make them “explainable.” The introduction of automated systems, and more broadly of Artificial Intelligence, into many domains has profoundly changed the nature of human activity, as well as the subjective experience that agents have of their own actions and their consequences – an experience (...) that is commonly referred to as sense of agency. We propose to examine the empirical evidence supporting this impact of automation on individuals’ sense of agency, and hence on measures as diverse as operator performance, system explicability and acceptability. Because of some of its key characteristics, AI occupies a special status in the artificial systems landscape. We suggest that this status prompts us to reconsider human–AI interactions in the light of human–human relations. We approach the study of joint actions in human social interactions to deduce what key features are necessary for the development of a reliable sense of agency in a social context and suggest that such framework can help define what constitutes a good explanation. Finally, we propose possible directions to improve human–AI interactions and, in particular, to restore the sense of agency of human operators, improve their confidence in the decisions made by artificial agents, and increase the acceptability of such agents. (shrink)