In the past decade, the fields of machine learning and artificial intelligence (AI) have seen unprecedented developments that raise human-machine interactions (HMI) to the next level.Smart machines, i.e., machines endowed with artificially intelligent systems, have lost their character as mere instruments. This, at least, seems to be the case if one considers how humans experience their interactions with them. Smart machines are construed to serve complex functions involving increasing degrees of freedom, and they generate solutions not fully anticipated by humans. (...) Consequently, their performances show a touch of action and even autonomy. HMI is therefore often described as a sort of “cooperation” rather than as a mere application of a tool. Some authors even go as far as subsuming cooperation with smart machines under the label ofpartnership, akin to cooperation between human agents sharing a common goal. In this paper, we explore how far the notion of shared agency and partnership can take us in our understanding of human interaction with smart machines. Discussing different topoi related to partnerships in general, we suggest that different kinds of “partnership” depending on the form of interaction between agents need to be kept apart. Building upon these discussions, we propose a tentative taxonomy of different kinds of HMI distinguishing coordination, collaboration, cooperation, and social partnership. (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)

The sociomotor framework outlines a possible role of social action effects on human action control, suggesting that anticipated partner reactions are a major cue to represent, select, and initiate own body movements. Here, we review studies that elucidate the actual content of social action representations and that explore factors that can distinguish action control processes involving social and inanimate action effects. Specifically, we address two hypotheses on how the social context can influence effect-based action control: first, by providing unique social (...) features such as body-related, anatomical codes, and second, by orienting attention towards any relevant feature dimensions of the action effects. The reviewed empirical work presents a surprisingly mixed picture: while there is indirect evidence for both accounts, previous studies that directly addressed the anatomical account showed no signs of the involvement of genuinely social features in sociomotor action control. Furthermore, several studies show evidence against the differentiation of social and non-social action effect processing, portraying sociomotor action representations as remarkably non-social. A focus on enhancing the social experience in future studies should, therefore, complement the current database to establish whether such settings give rise to the hypothesized influence of social context. (shrink)