Supporting Artificial Social Intelligence With Theory of Mind

Artificial social intelligence in teamwork: how team traits influence human-AI dynamics in complex tasks

This study examines the integration of Artificial Social Intelligence (ASI) into human teams, focusing on how ASI can enhance teamwork processes in complex tasks. Teams of three participants collaborated with ASI advisors designed to exhibit Artificial Theory of Mind (AToM) while engaged in an interdependent task. A profiling model was used to categorize teams based on their taskwork and teamwork potential and study how these influenced perceptions of team processes and ASI advisors. Results indicated that teams with higher taskwork or teamwork potential had more positive perceptions of their team processes, with those high in both dimensions showing the most favorable views. However, team performance significantly mediated these perceptions, suggesting that objective outcomes strongly influence subjective impressions of teammates. Notably, perceptions of the ASI advisors were not significantly affected by team performance but were positively correlated with higher taskwork and teamwork potential. The study highlights the need for ASI systems to be adaptable and responsive to the specific traits of human teams to be perceived as effective teammates.

Bidirectional transparency in human-agent communications: effects of direction and level of transparency

This study investigated whether bidirectional transparency, compared to agent-to-human transparency, improved human-agent collaboration. Additionally, we examined the optimal transparency levels for both humans and agents. We assessed the impact of transparency direction and level on various metrics of a human-agent team, including performance, trust, satisfaction, perceived agent's teaming skills, and mental workload. A total of 30 participants engaged in a human-agent collaborative game in a within-subject experiment with five conditions: a 2 (transparency directions: agent-to-human transparency vs. bidirectional transparency) × 2 (transparency levels: reasoning transparency vs. reasoning + projection transparency) factorial design, plus an additional action transparency condition as a control condition. The findings indicated that bidirectional transparency improved task performance without increasing the mental workload. This study recommends a bidirectional transparency mechanism, in which the agent provides transparency to humans regarding its reasoning and predictions, whereas humans offer transparency to the agent regarding their reasoning. Practitioner Summary: This study highlights the importance of bidirectional transparency in human-agent collaboration, demonstrating its effectiveness in enhancing task performance without increasing mental workload. It recommends implementing a mechanism where both humans and agents share transparency information, optimising collaboration outcomes.

Aversion to external feedback suffices to ensure agent alignment

Ensuring artificial intelligence behaves in such a way that is aligned with human values is commonly referred to as the alignment challenge. Prior work has shown that rational agents, behaving in such a way that maximizes a utility function, will inevitably behave in such a way that is not aligned with human values, especially as their level of intelligence goes up. Prior work has also shown that there is no "one true utility function"; solutions must include a more holistic approach to alignment. This paper describes apprehensive agents: agents that are architected in such a way that their effective utility function is an aggregation of a partial utility function (built by designers, to be maximized) and an expectation of negative feedback on given states (reasoned about, to be minimized). Agents are also capable of performing a temporal reasoning process that approximates designers' intentions in function of environment evolution (a necessary feature for severe mis-alignment to occur). We show that an apprehensive agent, behaving rationally, leverages this internal approximation of designers' intentions to predict negative feedback, and, as a consequence, behaves in such a way that maximizes alignment, without actually receiving any external feedback. We evaluate this strategy on simulated environments that expose mis-alignment opportunities: we show that apprehensive agents are indeed better aligned than their base counterparts and, in contrast with extant techniques, chances of alignment actually improve as agent intelligence grows.

Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities

The advent of artificial intelligence (AI) revolutionizes the ways of working in many areas of business and life science. In Medical Affairs (MA) departments of the pharmaceutical industry AI holds great potential for positively influencing the medical mission of identifying and addressing unmet medical needs and care gaps, and fostering solutions that improve the egalitarian and unbiased access of patients to treatments worldwide. Given the essential position of MA in corporate interactions with various healthcare stakeholders, AI offers broad possibilities to support strategic decision-making and to pioneer novel approaches in medical stakeholder interactions. By analyzing data derived from the healthcare environment and by streamlining operations in medical content generation, AI advances data-based prioritization and strategy execution. In this review, we discuss promising AI-based solutions in MA that support the effective use of heterogenous information from observations of the healthcare environment, the enhancement of medical education, and the analysis of real-world data. For a successful implementation of such solutions, specific considerations partly unique to healthcare must be taken care of, for example, transparency, data privacy, healthcare regulations, and in predictive applications, explainability.

Social-cognitive Skills Training on Interpersonal Understanding of Social Norms During Adolescence

Background

Social-cognitive skills training (SCST) in a therapeutic setup can result in more positive outcomes when incorporated with psychotherapy, especially among adolescents with minor social-cognitive impairments in their social interactions. It may result in multifarious benefits to mitigate their social-cognitive dysfunction. This study aimed to identify the effects of SCST on interpersonal understanding of social norms in adolescents with low social cognition.

Methods

In this quasi-experimental research, 80 adolescents (10-19 years) with low social cognition, no previous experience of skills training, and absence of any psychological disorders, especially those that affect their social-cognitive functioning, with assent from the participants and written informed consent from the parents/guardian and a score below 58 on the Need For Social-Cognition Scale, were included. They were randomly allocated into SCST or waitlist control group. SCST consists of 20 sessions with indoor activities, games, and discussions, and it has been arranged for 1 hour per 3 days a week for 3 months. Edinburgh social cognition test (ESCoT) was used to assess the degree of interpersonal understanding of social norms among adolescents as part of pre and posttests.

Results

The Wilcoxon Sign Ranked Test showed that the interpersonal understanding of social norms after SCST is significantly higher than the interpersonal understanding of social norms SCST with a large effect size. The mean (standard deviation) scores in the ESCoT test improved significantly (P < 0 .001) following [W = 0.001, P < .001, r = -1.000].

Conclusion

SCST effectively improves the interpersonal understanding of social norms, an essential developmental milestone during adolescence. It highlights the importance of focusing on mental health as a developmental asset that can influence social-cognitive development in the future.

Individual and team profiling to support theory of mind in artificial social intelligence

We describe an approach aimed at helping artificial intelligence develop theory of mind of their human teammates to support team interactions. We show how this can be supported through the provision of quantifiable, machine-readable, a priori information about the human team members to an agent. We first show how our profiling approach can capture individual team member characteristic profiles that can be constructed from sparse data and provided to agents to support the development of artificial theory of mind. We then show how it captures features of team composition that may influence team performance. We document this through an experiment examining factors influencing the performance of ad-hoc teams executing a complex team coordination task when paired with an artificial social intelligence (ASI) teammate. We report the relationship between the individual and team characteristics and measures related to task performance and self-reported perceptions of the ASI. The results show that individual and emergent team profiles were able to characterize features of the team that predicted behavior and explain differences in perceptions of ASI. Further, the features of these profiles may interact differently when teams work with human versus ASI advisors. Most strikingly, our analyses showed that ASI advisors had a strong positive impact on low potential teams such that they improved the performance of those teams across mission outcome measures. We discuss these findings in the context of developing intelligent technologies capable of social cognition and engage in collaborative behaviors that improve team effectiveness.

Self-beliefs, Transactive Memory Systems, and Collective Identification in Teams: Articulating the Socio-Cognitive Underpinnings of COHUMAIN

Socio-cognitive theory conceptualizes individual contributors as both enactors of cognitive processes and targets of a social context's determinative influences. The present research investigates how contributors' metacognition or self-beliefs, combine with others' views of themselves to inform collective team states related to learning about other agents (i.e., transactive memory systems) and forming social attachments with other agents (i.e., collective team identification), both important teamwork states that have implications for team collective intelligence. We test the predictions in a longitudinal study with 78 teams. Additionally, we provide interview data from industry experts in human-artificial intelligence teams. Our findings contribute to an emerging socio-cognitive architecture for COllective HUman-MAchine INtelligence (i.e., COHUMAIN) by articulating its underpinnings in individual and collective cognition and metacognition. Our resulting model has implications for the critical inputs necessary to design and enable a higher level of integration of human and machine teammates.