This paper describes IEEE P7001, a new draft standard on transparency of autonomous systems. In the paper, we outline the development and structure of the draft standard. We present the rationale for transparency as a measurable, testable property. We outline five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident/accident investigators and lawyers/expert witnesses, and explain the thinking behind the normative definitions of “levels” of transparency for each stakeholder group in P7001. The paper illustrates the application of P7001 through worked examples of both specification and assessment of fictional autonomous systems.

/pdf/ieee-p7001-a-proposed-standard-on-transparency-3alooszizw.pdf

IEEE P7001: A Proposed Standard on Transparency.

Psychosocial issues, such as social isolation and loneliness among older adults and people with dementia, continue to pose challenges with a rapidly aging population worldwide. Social robots are a rapidly emerging field of technology, developed to help address the psychosocial needs of this population. Although studies have reported positive findings regarding their psychosocial benefits, their implementation in real-world practice remains a challenge. Nevertheless, little is known about the factors affecting their implementation. The purpose of this review is to provide a systematic overview of the barriers and facilitators affecting the implementation of social robots for older adults and people with dementia. The Arksey and O’Malley approach with methodological enhancement by Levac et al. was used to guide the conduct of this review. Seven electronic databases were searched. In addition, hand searching and backward citation tracing was conducted. Three independent reviewers were involved in the screening and data charting process. Findings were synthesised and categorised into the five domains outlined in the Consolidated Framework of Implementation Research (CFIR). A total of 53 studies were included in the final review. Most of the included studies were based in participants’ homes and in care facilities. Barriers and facilitators were mapped onto 18 constructs in the five domains of the CFIR. The most frequently cited barriers were mapped to the constructs within the domain of “Intervention characteristics”, where issues such as the complexity of using the technology and technical obstacles impeded implementation. Most facilitators were mapped onto the domain “Patient needs and resources”. Overall, existing research are disproportionately focused on the internal validity (i.e. characteristics) of social robots, and there is significantly less research investigating their external validity, such as organisational or wider contextual factors that can affect their implementation in real-world practice. This review has identified and synthesised the breadth of evidence on the barriers and facilitators to the implementation of social robots for older adults and people with dementia. Future research should pay more attention to investigating the contextual factors, using an implementation framework, to identify barriers and facilitators to guide the implementation of social robots.

/pdf/barriers-and-facilitators-to-the-implementation-of-social-5fkjijgnqu.pdf

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review.

The evolving field of human-robot interaction (HRI) necessitates that we better understand how social robots operate and interact with humans. This scoping review provides an overview of about 300 research works focusing on the use of the NAO robot from 2010 to 2020. This study presents one of the most extensive and inclusive pieces of evidence on the deployment of the humanoid NAO robot and its global reach. Unlike most reviews, we provide both qualitative and quantitative results regarding how NAO is being used and what has been achieved so far. We analyzed a wide range of theoretical, empirical, and technical contributions that provide multidimensional insights, such as general trends in terms of application, the robot capabilities, its input and output modalities of communication, and the human-robot interaction experiments that featured NAO (e.g. number and roles of participants, design, and the length of interaction). Lastly, we derive from the review some research gaps in current state-of-the-art and provide suggestions for the design of the next generation of social robots.

10 Years of Human-NAO Interaction Research: A Scoping Review

Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its potential negative impacts on aspects such as job security, safety, and privacy. Nevertheless, this did not receive sufficient attention, particularly within the academic community. This research systematically reviews AI and robotics research through the lens of ethics in the AEC community for the past five years. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability, by summarizing existing literature and filtering it further based on its AEC relevance. Furthermore, thirteen research topics along the process were identified based on existing AEC studies that had direct relevance to the theme of ethics in general and their parallels are further discussed. Finally, the current challenges and knowledge gaps are discussed and seven specific future research directions are recommended. This study not only signifies more stakeholder awareness of this important topic but also provides imminent steps towards safer and more efficient realization. 

Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry

Robots can be key players in the healthcare digital revolution that is underway. More specifically, assistive robots are going to profoundly affect the way healthcare is delivered. In the research and commercial field, robotic technologies have been used for physical and cognitive rehabilitation, surgery, telemedicine, drug delivery, and patient management. Robots are currently used across a range of environments, including hospitals, homes, schools, and nursing homes. Healthcare robotics is an exciting, emerging area that can benefit all stakeholders across a wide range of settings. The objective of this chapter is to investigate how this can happen given the current state of the art in the field. We will focus our argument on how they can be used as valuable agents for the acquisition of novel data, relevant to the healthcare and well-being domain.

Assistive Robots for the Elderly: Innovative Tools to Gather Health Relevant Data

Cooperation between autonomous robots and humans is becoming more and more demanding. Robots have to be able to capable of possessing and expose a wide range of cognitive functions, once humans require their help. This paper describes a cognitive architecture for human-robot interaction that allows a robot to dynamically modulate its own level of social autonomy every time a human user delegates to it a task to accomplish in her/his place. The task adoption process leverages on multiple robot’s cognitive capabilities (i.e. the ability to have a theory of mind of the user, to build a model of the world, to profile the user and to make an evaluation about its own skill trustworthiness for building the user’s profile). On the basis of these capabilities the robot is able to adapt its own level of intelligent collaboration by adopting the task at the different levels of help defined in the theory of delegation and adoption conceived by Castelfranchi and Falcone. Besides that, the architecture enhances robot’s behavior transparency because gives to it the ability to provide a comprehensive explanation of the strategy it has adopted for accomplishing the delegated task. We propose an implementation of the cognitive architecture based on JaCaMo framework, which provides support for implementing multi-agent systems and integrates different multi-agent programming dimensions.

Towards trustworthiness and transparency in social human-robot interaction

Trust has been clearly identified as a key concept for human–machine interaction (HMI): on the one hand, users should trust artificial systems; on the other hand, devices must be able to estimate both how much other agents trust them and how trustworthy the other agents are. Indeed, the applications of trust in these scenarios are so complex that often, the interaction models consider only a part of the possible interactions and not the system in its entirety. On the contrary, in this work, we made the effort to consider the different types of interaction together, showing the advantages of this approach and the problems it allows to face. After the theoretical formalization, we introduce an agent simulation to show the functioning of the proposed model. The results of this work provide interesting insights for the evolution of HMI models.

/pdf/modeling-interaction-in-human-machine-systems-a-trust-and-cu21qeek.pdf

Modeling Interaction in Human–Machine Systems: A Trust and Trustworthiness Approach

A big challenge in human–robot interaction (HRI) is the design of autonomous robots that collaborate effectively with humans, exposing behaviors similar to those exhibited by humans when they interact with each other. Indeed, robots are part of daily life in multiple environments (i.e., cultural heritage sites, hospitals, offices, touristic scenarios and so on). In these contexts, robots have to coexist and interact with a wide spectrum of users not necessarily able or willing to adapt their interaction level to the kind requested by a machine: the users need to deal with artificial systems whose behaviors must be adapted as much as possible to the goals/needs of the users themselves, or more in general, to their mental states (beliefs, goals, plans and so on). In this paper, we introduce a cognitive architecture for adaptive and transparent human–robot interaction. The architecture allows a social robot to dynamically adjust its level of collaborative autonomy by restricting or expanding a delegated task on the basis of several context factors such as the mental states attributed to the human users involved in the interaction. This collaboration has to be based on different cognitive capabilities of the robot, i.e., the ability to build a user’s profile, to have a Theory of Mind of the user in terms of mental states attribution, to build a complex model of the context, intended both as a set of physical constraints and constraints due to the presence of other agents, with their own mental states. Based on the defined cognitive architecture and on the model of task delegation theorized by Castelfranchi and Falcone, the robot’s behavior is explainable by considering the abilities to attribute specific mental states to the user, the context in which it operates and its attitudes in adapting the level of autonomy to the user’s mental states and the context itself. The architecture has been implemented by exploiting the well known agent-oriented programming framework Jason. We provide the results of an HRI pilot study in which we recruited 26 real participants that have interacted with the humanoid robot Nao, widely used in HRI scenarios. The robot played the role of a museum assistant with the main goal to provide the user the most suitable museum exhibition to visit.

/pdf/collaborative-autonomy-human-robot-interaction-to-the-test-2rh54ts5.pdf

Collaborative Autonomy: Human–Robot Interaction to the Test of Intelligent Help

Autonomy is crucial in cooperation. The complexity of HRI scenarios requires autonomous robots able to exploit their superhuman computations (based on DNN, Machine Learning techniques and Big Data) in a trustworthy way. Trustworthiness is not only a matter of accuracy, privacy or security, but it is becoming more and more a matter of adaptation to humans agency. As claimed by Falcone and Castelfranchi, autonomy means the possibility of dislaying or providing an unexpected behavior (including refusal) that departs from a requested (agreed upon or not) behavior. In this sense, the autonomy to decide how to adopt a task delegated by the user, with respect to her/his own real needs and goals, distinguishes intelligent and trustworthy robots from highly performing robots. This kind of smart help can be provided only by cognitive robots able to represent and ascribe mental states (beliefs, goals, intentions, desires etc.) to their interlocutors. The mental states attribution can be the result of complex reasoning mechanisms or can be fast and automatic, based on scripts, roles, categories or stereotypes typically exploited by humans every time they interact in everyday life. In all these cases, robots that build and use cognitive models of humans (that have a Theory of Mind of their interlocutors), have to operate also a meta-evaluation of their own predictive skills to build those models. Robots have to be endowed with the capability to self-trust their skills to interpret the interlocutors and the context, for producing smart and effective decisions towards humans. After exploring the main concepts that make collaboration between humans and robots trustworthy and effective, we present the first of a series of experiments draw for testing different aspects of a designed cognitive architecture for trustworthy HRI. This architecture, based on consolidated theoretical principles (theory of social adjustable autonomy, theory of mind, theory of trust) has the main goal to build cognitive robots that provide smart, trustworthy collaboration, every time a human requires their help. In particular, the experiment has been designed in order to demonstrate how the robot’s capability to learn its own level of self-trust on its predictive abilities in perceiving the user and building a model of her/him, allows it to establish a trustworthy collaboration and to maintain a high level of user’s satisfaction, with respect to the robot’s performance, also when these abilities progressively degrade.

Human-robot interaction through adjustable social autonomy

In this paper we present a cognitive model to support reasoning and decision making on socially adaptive task delegation and adoption. The designed model allows a robot to dynamically modulate to dynamically modulate its own level of collaborative autonomy, by restricting or expanding a received task delegation, on the basis of several context factors as the needs of other users involved in the interaction. We exploit principles underlying theory of delegation, theory of mind and BDI agent modelling, in order to build a decision making system for realworld teaming between autonomous agents. The model has been developed by using JaCaMo framework, which provides support for implementing multi-agent systems and integrates different multi-agent programming dimensions. We tested our model in a specific domain on the humanoid robot Nao, widely adopted in human-robot interaction applications. The support study has established that the model provides the robot with the ability to modify its social autonomy and to handle possible collaborative conflicts due to the initiative to help the user beyond her/his request.

/pdf/a-computational-model-for-cognitive-human-robot-interaction-2zwlt2xb8i.pdf

A Computational Model for Cognitive Human-Robot Interaction: An Approach Based on Theory of Delegation.

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