As renewable energy and electrification expand rapidly, many electrical distribution grids experience grid congestion. This situation leads to long waiting lists for parties seeking a new grid connection or aiming to expand their existing grid connection. In addition to traditional grid enforcements, distribution system operators are developing ways to manage congestion by steering electricity supply and demand. As grid congestion limits the previously abundant resource of grid capacity, the challenge of how to fairly distribute this now-scarce resource raises new questions about nondiscrimination and broader notions of justice. This study, grounded in energy justice, explores the distributive and procedural injustices people experience with increasing grid congestion. Our research focuses on The Netherlands, where more than 10,000 parties await new grid connections. Through 16 semi-structured interviews with people either affected by or involved in mitigating grid congestion, our thematic analysis reveals three key categories: (1) injustices arising from legacy policies, legislation, and social norms; (2) injustices due to unclear regulations, inconsistent policies, and policy gaps; and (3) injustices related to changing relationships between DSOs and affected parties. These findings highlight that grid congestion is fundamentally sociotechnical; while congestion is both constrained and addressed by technical factors, institutional and social factors such as legacy policies, social norms and communication, significantly influence perceptions of injustice. Our findings call for a comprehensive integration of justice principles within the institutional (e.g. regulation, policy, markets, social norms), technical (e.g. grid infrastructure, IT systems), and social (e.g. community engagement, communication) components of grid infrastructure.

Integrating human support with chatbot-based behavior change interventions raises three challenges: (1) attuning the support to an individual’s state (e.g., motivation) for enhanced engagement, (2) limiting the use of the concerning human resources for enhanced efficiency, and (3) optimizing outcomes on ethical aspects (e.g., fairness). Therefore, we conducted a study in which 679 smokers and vapers had a 20% chance of receiving human feedback between five chatbot sessions. We find that having received feedback increases retention and effort spent on preparatory activities. However, analyzing a reinforcement learning (RL) model fit on the data shows there are also states where not providing feedback is better. Even this “standard” benefit-maximizing RL model is value-laden. It not only prioritizes people who would benefit most, but also those who are already doing well and want feedback. We show how four other ethical principles can be incorporated to favor other smoker subgroups, yet, interdependencies exist.

This full-day workshop focuses on advancing research and development in socially-competent agents that care. Many agent platforms and kinds of embodiments of agents that have a primary focus on caring, one way or another, for the end users. In this workshop, we aim to look at value-based design, inclusiveness, empathetic design and long-term human-agent interaction for socially competent agents that care.

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 letter 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 letter demonstrate how human-robot co-learning can be enabled in physically embodied tasks.

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.

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.

When a human makes a decision, an observer may want to understand the reasons and motivations behind the decision. This understanding is important when IVAs are involved in contextual decision-making or coaching practices. To address this challenge, we propose that an agent’s understanding of its user should include knowledge of the user’s underlying values. Humans prioritise different values – sometimes contradictory – in a manner that depends on the context. We present a method where the agent and user build the required context-sensitive value model together. We use Schwartz’s value theory, which places individuals’ values into ten categories. In a between-subject experiment, with three sessions on different days, we elicit user values by presenting them with moral dilemmas in different contexts on the first day, refine the model by asking users to argue about contradictions on the second day, and let them reflect on the model that they have built together with the system on the third day. We find that users exposed to a value-aware condition are more likely to agree with the robot’s representations of their values post-reflection than those in a baseline. Participants also prioritise different values depending on the context, agreeing with previous findings.

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.

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.

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.

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.

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.

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.

Fairness has recently gained significant attention in the scientific literature on algorithmic control systems for congestion management. However, many diverse conceptualizations of fairness have been presented. This paper aims to categorize these varying conceptualizations by reviewing existing literature on congestion management. It examines how researchers approach decisions concerning the scoping of fairness problems, the selection of fairness principles, and the choice of evaluation metrics. Findings highlight a need for more justification of fairness conceptualizations in literature as well as a need for standardized evaluation metrics and more empirical grounding and validation. The insights provided can help researchers and practitioners consider fairness comprehensively in the design of algorithmic control systems for congestion management.

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.

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.

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.

Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released our own reinforcement learning agent environment, “the Room”, where an agent has to learn how to encode, store, and retrieve memories to maximize its return by answering questions. We show that our deep Q-learning based agent successfully learns whether a short-term memory should be forgotten, or rather be stored in the episodic or semantic memory systems. Our experiments indicate that an agent with human-like memory systems can outperform an agent without this memory structure in the environment.

For human-agent teams to be successful, agent explanations are crucial. These explanations should ideally be personalized by adapting them to intended human users. So far, little work has been conducted on personalized agent explanations during human-agent teamwork. Therefore, an online experiment (n = 60) was conducted to compare personalized agent explanations against a baseline of non-personalized explanations. We implemented four agents who adapted their explanations during a search and rescue task randomly, or based on human workload, performance, or trust. Results show that personalized explanations can increase explanation satisfaction and trust in the agent, but also decrease performance. Therefore, we conclude that personalized agent explanations can be beneficial to human-agent teamwork, but that user modelling and personalization techniques should be carefully considered.

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.