Whether choosing teammates for a project or partners for everyday life tasks, people constantly decide with whom to work. However, in these decisions, they often overemphasize characteristics that are not directly relevant to task performance. For example, prioritizing a partner’s trustworthiness for a task where competence is more important for good task performance. Artificial intelligence (AI) systems have the potential to mitigate these judgment errors by guiding decision-makers toward placing greater weight on traits that are more predictive of success for the specific task at hand. Although the potential usefulness of such systems is evident, previous work leaves unclear under what conditions and for what type of AI support people are willing to rely on and trust AI systems for such relational decisions (i.e., selecting a collaboration partner). To bridge this gap, our study examined how different forms of AI support shape users’ perceptions of the AI’s intellectual and social capabilities, their sense of autonomy, and their willingness to rely on and trust in AI when selecting a partner for a collaborative task. To do this, a total of 397 participants designed ideal partners for two collaborative tasks while receiving one of three forms of AI support: (1) recommendation, (2) explanation, or (3) knowledge nudges. This was tested in two different tasks: a competency-based task and a trustworthiness-based task. We found that richer AI support (through explanations or nudges) enhances perceived AI’s social and intellectual capabilities, but not autonomy. Perceptions of intellectual capabilities, rather than social capabilities, predict greater reliance. Both perceptions of AI capabilities mediate the effect of the type of AI support on reliance. Overall, the study advances understanding of human–AI collaboration by revealing how AI design features shape user perceptions and reliance when users need to evaluate and select their collaborators.

Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We first conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. The study features the insights of 50 HI researchers, and shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Based on these results, we developed a quality model for HI teams composed of seven high-level quality attributes, further refined into 16 specific ones. To evaluate the relevance and understanding of the proposed attributes, we conducted a second empirical investigation by staging competitions in which participants used the quality model to develop and analyze HI usage scenarios. Our analysis of 48 collected scenarios, which we openly release, confirms the proposed attributes' relevance and highlights insights that emerge when designers consider the quality model in HI system design.

As intelligent technology and applications have become an integral part of nearly all aspects of people's daily lives, many intelligent systems have been designed to help people navigate the complex space of social interactions. One prominent strategy for such intelligent support is providing meaningful Ad Hoc Interventions (ADI), e.g., through timely notifications. An alternative is Technology-Supported Reflection (TSR), e.g., by offering information about activities in one's past for personal insights. In contrast to straight-up interventions, the aim of the latter strategy is not to directly augment human skills but instead support learning and personal growth over time. However, while TSR has seen widespread interest in applications in some areas, such as physical fitness and mental health, its use for improving human social interactions has not yet been systematically explored. Concretely, it is currently unclear 1) what forms of self-reflection systems intend to support, 2) how their different technological components (e.g., data collection, information integration) are involved in providing support, and 3) what common limitations and design challenges they face. In this article, we present the results of a systematic literature review focusing on these questions to provide a structured foundation for targeted research. Concretely, we identified and analysed a collection of 23 relevant papers, each describing a system deploying TSR to support humans with elements of social interactions.We constructed a framework with a set of features to comprehensively describe and analyze the systems that support self-reflection, including their application domains, how they fit into the existing design framework, how they facilitate learning through reflection, how adaptive they are to individual users, and how they were evaluated. Finally, we propose a direction for designing systems that support individual's social interactions through self-reflection in an adaptive manner.

How people evaluate, select, and engage with others in cooperative settings significantly impacts their well-being, happiness, and success. However, navigating these processes is complex. Equipping systems with the ability to recognize, interpret, and even engage during such socio-cognitive processes can increase their potential to support humans in these socio-cognitive processes and be more successful in adjusting to the social environment they are embedded in (e.g., understanding human preferences and attitudes), leading to better quality interactions and decision-making for future partners. Yet, the developments of such systems depend on available datasets. However, based on our knowledge, no dataset exists that can be used to model partner selection for joint tasks. To support research focused on creating such intelligent systems, we introduce the PARSEL dataset – a comprehensive corpus of dyadic interactions designed for computational modeling of PARtner SELection processes and collaborative behavior. In total, 297 participants took part in the datasets. The dataset contains measurements of partner selection decisions over three different stages, as well as factors that may influence partner selection in the context of (online) social interactions. It includes audiovisual recordings that offer fine-grained behavioral cues used during these interactions, self-reported traits, and reported perceptions of person-, situation- and team-specific phenomena. By providing this resource, we aim to foster advancements in computational methods that can effectively model and augment socio-cognitive processes, contributing to socially aware intelligent systems and enhanced human-system interactions.

Type II diabetes is a complex health condition requiring patients to closely and continuously collaborate with healthcare professionals and other caretakers on lifestyle changes. While intelligent products have tremendous potential to support such Diabetes Lifestyle Management (DLM), existing products are typically conceived from a technology-centered perspective that insufficiently acknowledges the degree to which collaboration and inclusion of stakeholders is required. In this article, we argue that the emergent design philosophy of Hybrid Intelligence (HI) forms a suitable alternative lens for research and development. In particular, we (1) highlight a series of pragmatic challenges for effective AI-based DLM support based on results from an expert focus group, and (2) argue for HI’s potential to address these by outlining relevant research trajectories.

In this article we introduceMementos: 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, andMood), 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 thevalidityof its content by investigating biases and consistency with existing research on affect and memory processing, 3) review previously published work that demonstrates theusefulnessof 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 modelingVideo-Induced Emotions,Memory-Associated Affect, andMemory Evocation.

Intelligent systems might benefit from automatically detecting when a stimulus has triggered a user’s recollection of personal memories, e.g., to identify that a piece of media content holds personal significance for them. While computational research has demonstrated the potential to identify related states based on facial behavior (e.g., mind-wandering), the automatic detection of spontaneous recollections specifically has not been investigated this far. Motivated by this, we present machine learning experiments exploring the feasibility of detecting whether a video clip has triggered personal memories in a viewer based on the analysis of their Head Rotation, Head Position, Eye Gaze, and Facial Expressions. Concretely, we introduce an approach for automatic detection and evaluate its potential for predictions using in-the-wild webcam recordings. Overall, our findings demonstrate the capacity for above chance detections in both settings, with substantially better performance for the video-independent variant. Beyond this, we investigate the role of person-specific recollection biases for predictions of our video-independent models and the importance of specific modalities of facial behavior. Finally, we discuss the implications of our findings for detecting recollections and user-modeling in adaptive systems.

The ability to automatically infer relevant aspects of human users’ thoughts and feelings is crucial for technologies to adapt their behaviors in complex interactions intelligently (e.g., social robots or tutoring systems). Research on multimodal analysis has demonstrated the potential of technology to provide such estimates for a broad range of internal states and processes. However, constructing robust enough approaches for deployment in real-world applications remains an open problem. The MSECP-Wild workshop series serves as a multidisciplinary forum to present and discuss research addressing this challenge. This 4th iteration focuses on addressing varying contextual conditions (e.g., throughout an interaction or across different situations and environments) in intelligent systems as a crucial barrier for more valid real-world predictions and actions. Submissions to the workshop span efforts relevant to multimodal data collection and context-sensitive modeling. These works provide important impulses for discussions of the state-of-the-art and opportunities for future research on these subjects.

Enabling computer-based applications to display intelligent behavior in complex social settings requires them to relate to important aspects of how humans experience and understand such situations. One crucial driver of peoples’ social behavior during an interaction is the interdependence they perceive, i.e., how the outcome of an interaction is determined by their own and others’ actions. According to psychological studies, both the nonverbal behavior displayed by Motivated by this, we present a series of experiments to automatically recognize interdependence perceptions in dyadic face-to-face negotiations using these sources. Concretely, our approach draws on a combination of features describing individuals’ Facial, Upper Body, and Vocal Behavior with state-of-the-art algorithms for multivariate time series classification. Our findings demonstrate that differences in some types of interdependence perceptions can be detected through the automatic analysis of nonverbal behaviors. We discuss implications for developing socially intelligent systems and opportunities for future research.

Empirical evidence suggests that the emotional meaning of facial behavior in isolation is often ambiguous in real-world conditions. While humans complement interpretations of others' faces with additional reasoning about context, automated approaches rarely display such context-sensitivity. Empirical findings indicate that the personal memories triggered by videos are crucial for predicting viewers' emotional response to such videos ?- in some cases, even more so than the video's audiovisual content. In this article, we explore the benefits of personal memories as context for facial behavior analysis. We conduct a series of multimodal machine learning experiments combining the automatic analysis of video-viewers' faces with that of two types of context information for affective predictions: \beginenumerate* [label=(\arabic*)] \item self-reported free-text descriptions of triggered memories and \item a video's audiovisual content \endenumerate*. Our results demonstrate that both sources of context provide models with information about variation in viewers' affective responses that complement facial analysis and each other.

This paper contributes to the automatic estimation of the subjective emotional experience that audio-visual media content induces in individual viewers, e.g. to support affect-based recommendations. Making accurate predictions of these responses is a challenging task because of their highly person-dependent and situation-specific nature. Findings from psychology indicate that an important driver for the emotional impact of media is the triggering of personal memories in observers. However, existing research on automated predictions focuses on the isolated analysis of audiovisual content, ignoring such contextual influences. In a series of empirical investigations, we (1) quantify the impact of associated personal memories on viewers' emotional responses to music videos in-the-wild and (2) assess the potential value of information about triggered memories for personalizing automatic predictions in this setting. Our findings indicate that the occurrence of memories intensifies emotional responses to videos. Moreover, information about viewers' memory response explains more variation in video-induced emotions than either the identity of videos or relevant viewer-characteristics (e.g. personality or mood). We discuss the implications of these results for existing approaches to automated predictions and describe ways for progress towards developing memory-sensitive alternatives.

An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. This is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.