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 goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another dimension is (...) recognized as generalization, the possibility to go over from specific to more general types of problems. A third dimension is semantic grounding. Our overall analysis connects to a number of known foundational issues in the philosophy of mind and cognition: the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and use theories of meaning. It shall finally be argued that the dimension of grounding decomposes into three sub-dimensions. And the dimension of self-learning turns out as only one of a whole range of “self-x-capacities” (based on ideas of organic computing) that span the self-x-subspace of the full AI state space. (shrink)

I describe a suite of reinforcement learning environments in which artificial agents learn to value and respond to moral content and contexts. I illustrate the core principles of the framework by characterizing one such environment, or “gridworld,” in which an agent learns to trade-off between monetary profit and fair dealing, as applied in a standard behavioral economic paradigm. I then highlight the core technical and philosophical advantages of the learning approach for modeling moral cognition, and for addressing the so-called value (...) alignment problem in AI. (shrink)

Ballard and Zhang offer a fascinating review of how computational models of human vision have evolved since David Marr proposed his Tri‐Level Hypothesis, with a focus on the refinement of algorithm descriptions over time.

From the conception of Baddeley’s visuospatial sketchpad, visual working memory and visual attention have been closely linked concepts. An attractive model has advocated unity of the two cognitive functions, with attention serving the active maintenance of sensory representations. However, empirical evidence from various paradigms and dependent measures has now firmly established an at least partial dissociation between visual attention and visual working memory maintenance e thus leaving unclear what the relationship between the two concepts is. Moreover, a focus on sensory (...) storage has treated visual working memory as a reflection of the past, with attention as a limiting resource. This view ignores what storage is for: immediate or future action. We argue that rather than serving sensory storage, attention emerges from coupling relevant sensory and action representations within working memory. Importantly, this coupling is bidirectional: First, through recurrent feedback mechanisms, action coupling results in the enhancement of the appropriate sensory memory representation. Under this view, unattended memories are currently not coupled to an action plan, but are not necessarily lost and remain available for future tasks when necessary. Second, through the very same feedback projections, attention serves as the credit assignment mechanism for the action’s outcome. When the action is successful, the associated representations are being reinforced, leading to more robust consolidation and more rapid retrieval in the future e thus explaining performance benefits for attended memories without assuming that attention serves as the maintenance mechanism. By firmly grounding VWM in the action system, the new framework integrates a range of behavioural and neurophysiological findings and avoids circularity in explaining the role of attention in working memory. (shrink)