Fire fighters operate in a dangerous, dynamic, and complex environment. Artificial Intelligence (AI) systems can contribute to improve fire fighters' situation awareness and decision-making. However, the introduction of AI systems needs to be done responsibly, taking (human) values into account, especially as the situation in which fire fighters operate is uncertain and decisions have a big impact. In this research, we investigate values that are affected by the introduction of AI systems for fire services by conducting several semi-structured focus group sessions with (operational) fire service personnel. The focus group outcomes are qualitatively analyzed and key values are identified and discussed. This research is a first step in an iterative process towards a generic framework of ethical aspects for the introduction of AI systems in first response, which will give insight into the relevant ethical aspects to take into account when developing AI systems for first responders.@en

Whereas the reception task is a promising application domain for social robots, knowledge is lacking about how to design the appropriate re-usable communication styles for a reception robot. This paper presents the use and evaluation of an iterative interaction-design (ID) method with which task- and person-oriented multi-modal communication styles have been designed for such a robot. First, we report on an evaluation study of the ID-method with Industrial Design students (N =13) who designed these two communication styles for a Pepper robot. This provided a set of distinct designs of the two styles, for which the differences in design parameters were in line with social science theory. The task-oriented style showed a more formal, shorter and less chatty communication. Second, we present findings from a Mechanical Turk study conducted to evaluate the perception of these style designs. Participants (N =301) were presented with videos showing the robot acting as a receptionist and were asked to rate their perception of the robot, the service experience and the orientation of the designs. Overall, the interaction with the robot was appreciated well. The robot with a person-oriented style was perceived to be more animate and likeable. Analysis showed that chit-chat was the main contributor to the perceived difference between the person-oriented and task-oriented styles. This is an important finding as it gives interaction designers a validated best-practice approach to make interaction style more or less personal.

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Hypertension is a condition affecting most people over 45 years old. Health Self-Management offers many opportunities for prevention and cure. However, most scientific health literature is unknown by health professionals and/or patients. Per year about 200.000 new scientific papers on cardiovascular health appear, which is too much for a human to read. Hence, an LLM-based Health AI research assistant is developed for mining scientific literature on blood pressure and food. A user evaluation was conducted with n=8 participants who just completed an intensive lifestyle intervention for blood pressure self-management. They highlighted several challenges and opportunities for a Health AI, especially regarding claim transparency, data quality and risks of hallucinations. In the discussion we propose seven criteria using metadata and information characteristics to help evaluate ambiguous or conflicting health science claims. @en

A majority of employees over the age of 40 have hypertension, impacting their health and performance. A two-week self-management support (SMS) intervention was tested, with daily feedback and microlearning cycles to improve health self-management competences. On average, participants (n = 8) reduced their blood pressure from 145/92 mmHg to 126/86 mmHg. User evaluation confirmed the importance of core SMS aspects: information transfer, daily monitoring, enhancing problem solving/decision making, self-treatment using a tailored action plan, coping skills, and skilful coach follow-up. Several lessons are drawn on microlearning, peer coaching, health results, intrinsic motivation, and social learning, which appear useful for other health improvement initiatives.

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Despite a large body of research on robot learning, it has not yet been thoroughly studied how collaborating humans and robots learn reciprocally. In such situations, both humans and robots continuously learn about each other and the task through interaction. This paper addresses the research question: "How can human-robot co-learning be facilitated in physically embodied collaborative tasks?". First, we derived five requirements for successful human-robot co-learning from literature: shared goal, synchrony, interdependence, adaptability, and transparency. Based on these requirements, we designed a collaborative human-robot handover task and a robot Q-learning method. In an evaluation with six human participants co-learning was indeed found to emerge in the hand-over task. Particularly, for three of the human-robot dyads, our designed setup proved to facilitate co-learning in a way that met all five requirements. The task and robot learning method presented in this paper demonstrate how human-robot co-learning can be enabled in physically embodied tasks.

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Social interactions form an essential aspect of people’s life, however, it is quite challenging for individuals to handle a wide range of social situations. Therefore, a variety of training systems have been developed to improve their skills. This literature review seeks to give an overview of the state of the art of technology-supported systems for social skills training. The studies eligible for inclusion described a technology-supported system with the purpose of training social skills and included an experimental or observational study to evaluate the efficacy of the system. 225 studies (224 publications) with 216 systems were identified, characterized, and analyzed in this literature review. Using the taxonomy as put forward in this study, the analysis shows that the majority of these systems were screen-based applications, with virtual reality technology being the most frequently observed. The systems most often targeted communication skills that focus on transferring information to produce greater understanding, i.e. mending general communication impairments in children with autism. In terms of functions, support for learning-by-doing was the most observed function, while focusing on job interviews provided the largest number of functions. Finally, the studies reported overwhelmingly positively regarding the systems’ impact, including 76 studies with a randomized controlled trial design. Still, most studies only used a quasi-experimental design based on self-report measures. We anticipate the proposed taxonomy to be a starting point for researchers to position their work and that the review will help them with gaining inspiration for the design and evaluation of social skills training systems.@en

To increase the effectiveness of behavior change applications, a large variety of algorithms has been developed to adapt what the applications offer, when, how, and with whom. Given the multitude of challenges related to the concept of algorithmic behavior change support, its development, evaluation, and impact on behavior change, this workshop aims to strengthen the community of people with diverse backgrounds (e.g., computer science, psychology, human-computer interaction) and roles in behavior change support (e.g., researcher, designer, practitioner). Combining keynotes of leading researchers with sessions in which individual workshop participants present their work and discuss problems with the audience, the workshop encouraged a lively exchange of ideas that benefits current and future research on algorithmic behavior change support.@en

In human-machine teams, the strengths and weaknesses of both team members result in dependencies, opportunities, and requirements to collaborate. Managing these interdependence relationships is crucial for teamwork, as it is argued that they facilitate accurate trust calibration. Unfortunately, empirical research on the influence of interdependence on trust calibration during human-machine teamwork is lacking. Therefore, we conducted an experiment (n=80) to study the effect of interdependence relationships (complete independence, complementary independence, optional interdependence, required interdependence) on human-machine trust calibration. Participants collaborated with a virtual agent during a simulated search and rescue task in teams characterized by one of the four interdependencies. A machine-induced trust violation was included in the task to facilitate dynamic trust calibration. Results show that the interdependence relationships during human-machine teamwork influence perceived trust calibration over time. Only in the teams with joint actions (optional and required interdependence) does perceived trust in the machine not recover to its initial pre-violated value. However, results show that the correlation between perceived trust in the machine and machine trustworthiness is strongest in these teams with joint actions, suggesting a more accurate trust calibration process. Overall, our findings provide some first evidence that interdependence relationships during human-machine teamwork influence human-machine trust calibration.@en

Introduction: Humans and robots are increasingly collaborating on complex tasks such as firefighting. As robots are becoming more autonomous, collaboration in human-robot teams should be combined with meaningful human control. Variable autonomy approaches can ensure meaningful human control over robots by satisfying accountability, responsibility, and transparency. To verify whether variable autonomy approaches truly ensure meaningful human control, the concept should be operationalized to allow its measurement. So far, designers of variable autonomy approaches lack metrics to systematically address meaningful human control.

Methods: Therefore, this qualitative focus group (n = 5 experts) explored quantitative operationalizations of meaningful human control during dynamic task allocation using variable autonomy in human-robot teams for firefighting. This variable autonomy approach requires dynamic allocation of moral decisions to humans and non-moral decisions to robots, using robot identification of moral sensitivity. We analyzed the data of the focus group using reflexive thematic analysis.

Results: Results highlight the usefulness of quantifying the traceability requirement of meaningful human control, and how situation awareness and performance can be used to objectively measure aspects of the traceability requirement. Moreover, results emphasize that team and robot outcomes can be used to verify meaningful human control but that identifying reasons underlying these outcomes determines the level of meaningful human control.

Discussion: Based on our results, we propose an evaluation method that can verify if dynamic task allocation using variable autonomy in human-robot teams for firefighting ensures meaningful human control over the robot. This method involves subjectively and objectively quantifying traceability using human responses during and after simulations of the collaboration. In addition, the method involves semi-structured interviews after the simulation to identify reasons underlying outcomes and suggestions to improve the variable autonomy approach.@en

Artificial Intelligence systems are more and more being introduced into first response; however, this introduction needs to be done responsibly. While generic claims on what this entails already exist, more details are required to understand the exact nature of responsible application of AI within the first response domain. The context in which AI systems are applied largely determines the ethical, legal, and societal impact and how to deal with this impact responsibly. For that reason, we empirically investigate relevant human values that are affected by the introduction of a specific AI-based Decision Aid (AIDA), a decision support system under development for Fire Services in the Netherlands. We held 10 expert group sessions and discussed the impact of AIDA on different stakeholders. This paper presents the design and implementation of the study and, as we are still in process of analyzing the sessions in detail, summarizes preliminary insights and steps forward. @en

Artificial intelligence (AI) is increasingly prevalent in our daily lives, setting specific requirements for responsible development and deployment: The AI should be explainable and inclusive. Despite substantial research and development investment in explainable AI, there is a lack of effort into making AI explainable and inclusive to people with cognitive disabilities as well. In this paper, we present the first steps towards this research topic. We argue that three main questions guide this research, namely: 1) How explainable should a system be?; 2) What level of understanding can the user reach, and what is the right type of explanation to help them reach this level?; and 3) How can we implement an AI system that can generate the necessary explanations? We present the current state of the art in research on these three topics, the current open questions and the next steps. Finally, we present the challenges specific to bringing these three research topics together, in order to eventually be able to answer the question of how to make AI systems explainable also to people with cognitive disabilities.@en

Despite their prevalence in eHealth applications for behavior change, persuasive messages tend to have small effects on behavior. Conditions or states (e.g., confidence, knowledge, motivation) and characteristics (e.g., gender, age, personality) of persuadees are two promising components for more effective algorithms for choosing persuasive messages. However, it is not yet sufficiently clear how well considering these components allows one to predict behavior after persuasive attempts, especially in the long run. Since collecting data for many algorithm components is costly and places a burden on users, a better understanding of the impact of individual components in practice is welcome. This can help to make an informed decision on which components to use. We thus conducted a longitudinal study in which a virtual coach persuaded 671 daily smokers to do preparatory activities for quitting smoking and becoming more physically active, such as envisioning one’s desired future self. Based on the collected data, we designed a Reinforcement Learning (RL)-approach that considers current and future states to maximize the effort people spend on their activities. Using this RL-approach, we found, based on leave-one-out cross-validation, that considering states helps to predict both behavior and future states. User characteristics and especially involvement in the activities, on the other hand, only help to predict behavior if used in combination with states rather than alone. We see these results as supporting the use of states and involvement in persuasion algorithms. Our dataset is available online.@en

When humans and AI-agents collaborate, they need to continuously learn about each other and the task. We propose a Team Design Pattern that utilizes adaptivity in the behavior of human and agent team partners, causing new Collaboration Patterns to emerge. Human-AI Co-Learning takes place when partners can formalize recognized patterns of collaboration in a commonly shared language, and can communicate with each other about these patterns. For this, we developed an ontology of Collaboration Patterns. An accompanying Graphical User Interface (GUI) enables partners to formalize and refine Collaboration Patterns, which can then be communicated to the partner. The ontology was evaluated empirically with human participants who viewed video recordings of joint human-agent activities. Participants were requested to identify Collaboration Patterns in the footage, and to formalize patterns by using the ontology’s GUI. Results show that the ontology supports humans to recognize and define Collaboration Patterns successfully. To improve the ontology, it is suggested to include pre- and post-conditions of tasks, as well as parallel actions of team members.

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We outline two points of criticism. Firstly, we argue that robots do constitute a separate category of beings in people's minds rather than being mere depictions of non-robotic characters. Secondly, we find that (semi-)automatic processes underpinning communicative interaction play a greater role in shaping robot-directed speech than Clark and Fischer's theory of social robots as depictions indicate.

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With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.

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In this article we introduce Mementos: the first multimodal corpus for computational modeling of affect and memory processing in response to video content. It was collected online via crowdsourcing and captures 1995 individual responses collected from 297 unique viewers responding to 42 different segments of music videos. Apart from webcam recordings of their upper-body behavior (totaling 2012 minutes) and self-reports of their emotional experience, it contains detailed descriptions of the occurrence and content of 989 personal memories triggered by the video content. Finally, the dataset includes self-report measures related to individual differences in participants' background and situation (Demographics, Personality, and Mood), thereby facilitating the exploration of important contextual factors in research using the dataset. We describe 1) the construction and contents of the corpus itself, 2) analyse the validity of its content by investigating biases and consistency with existing research on affect and memory processing, 3) review previously published work that demonstrates the usefulness of the multimodal data in the corpus for research on automated detection and prediction tasks, and 4) provide suggestions for how the dataset can be used in future research on modeling Video-Induced Emotions, Memory-Associated Affect, and Memory Evocation.

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Hypertension Self-Management is more powerful when done in groups, and with daily (e)Support for maximum impact. Small intervention groups enable high degrees of personalization, interaction, and learning. We compare three Self-Management Support (SMS) pilots of two weeks duration, in which various tools and daily microlearning strategies were used. Average blood pressure improvements in the pilots were 161/112 to 129/90 mmHg, resp. 145/92 to 126/86 mmHg, and 155/95 to 139/85 mmHg. User evaluations (n=20) were collected on perceived effectiveness of the various support components. This showed the importance of core SMS components: information transfer, daily monitoring, promoting health competences and follow-up. A tentative cross-case conclusion is that more daily social learning and microlearning feedback helps build more success: for blood pressure results and for competence building.@en

Assistive robots are expected to contribute to the solution of major societal problems in healthcare, such as the increasing number of elderly who need informal and professional care over a long period of time. Most of the research focuses on the development of humanlike robots to facilitate human-robot interaction and strengthen the social, cognitive and affective processes. However, there are some possible downsides of this type of "robot humanizing", like raising high expectations and causing incorrect mental models of the robots. Machine-like robots, on the other hand, may help to build more realistic mental models and expectations but might bring about less fluent interactions and less pronounced experiences (i.e., less to remember). To test if a human-like robot indeed brings about better interaction fluency and memory recall, we designed two types of robots for a joint human-robot music listening activity: A human-like and a machine-like robot (Pepper). Thirty students participated in the experiment managed by a Wizard-of-Oz set-up. As expected, the human-like robot proved to perform better in terms of fluency and memory recall. Currently, we are preparing a follow-up experiment, consisting of longer sessions with the elderly to see whether this effect persists for this age group and how far the human- or machine-likeness influences the elderly's understanding and expectations of the robot's capabilities.

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Virtual coaches have the potential to address the low adherence common to eHealth applications for behavior change by, for example, providing motivational support. However, given the multitude of factors affecting users’ attitudes toward virtual coaches, more insights are needed on how such virtual coaches can be designed to affect these attitudes in a specific use context positively. Especially valuable are insights that are based on users interacting with such a virtual coach for longer. We thus conducted a study in which more than 500 smokers interacted with the text-based virtual coach Sam in five sessions. In each session, Sam assigned smokers a new preparatory activity for quitting smoking and provided motivational support for doing the activity. Based on a mixed-methods analysis of users’ willingness to continue working and their relationship with Sam, we obtained eight themes for users’ attitudes toward Sam. These themes relate to whether Sam is seen as human or artificial, specific characteristics of Sam (e.g., caring character), the interaction with Sam, and the relationship with Sam. We used these themes to formulate literature-based recommendations to guide designers of virtual coaches for behavior change. For example, letting the virtual coach get to know users and disclose more information about itself may improve its relationship with users.@en

This document is an encore abstract of the paper “Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior” presented at AAMAS 2023.@en