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1. Swarm Intelligence Amplifies the IQ of Collaborating Teams

   https://doi.org/10.1109/AI4I.2019.00036  

   Rosenberg, Louis; Willcox, Gregg (September 2019, ai4i 2019)

   In the natural world, Swarm Intelligence (SI) is a well-known phenomenon that enables groups of organisms to make collective decisions with significantly greater accuracy than the individuals could do on their own. In recent years, a new AI technology called Artificial Swarm Intelligence (ASI) has been developed that enables similar benefits for human teams. It works by connecting networked teams into real-time systems modeled on natural swarms. Referred to commonly as “human swarms” or “hive minds,” these closed-loop systems have been shown to amplify group performance across a wide range of tasks, from financial forecasting to strategic decision-making. The current study explores the ability of ASI technology to amplify the IQ of small teams. Five small teams answered a series of questions from a commonly used intelligence test known as the Raven’s Standard Progressive Matrices (RSPM) test. Participants took the test first as individuals, and then as groups moderated by swarming algorithms (i.e. “swarms”). The average individual achieved 53.7% correct, while the average swarm achieved 76.7% correct, corresponding to an estimated IQ increase of 14 points. When the individual responses were aggregated by majority vote, the groups scored 56.7% correct, still 12 IQ points less than the real-time swarming method. 

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2. Perception of Social Intelligence in Robots Performing False-Belief Tasks

   https://doi.org/10.1109/RO-MAN46459.2019.8956467  

   Sturgeon, Stephanie; Palmer, Andrew; Blankenburg, Janelle; Feil-Seifer, David (October 2019, International Symposium on Robot and Human Interactive Communication (RO-MAN))

   This study evaluated how a robot demonstrating a Theory of Mind (ToM) influenced human perception of social intelligence and animacy in a human-robot interaction. Data was gathered through an online survey where participants watched a video depicting a NAO robot either failing or passing the Sally-Anne false-belief task. Participants (N = 60) were randomly assigned to either the Pass or Fail condition. A Perceived Social Intelligence Survey and the Perceived Intelligence and Animacy subsections of the Godspeed Questionnaire were used as measures. The Godspeed was given before viewing the task to measure participant expectations, and again after to test changes in opinion. Our findings show that robots demonstrating ToM significantly increase perceived social intelligence, while robots demonstrating ToM deficiencies are perceived as less socially intelligent. 

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3. Strategies for the Inclusion of Human Members within Human-Robot Teams

   https://doi.org/10.1145/3319502.3374808  

   Strohkorb Sebo, Sarah; Dong, Ling Liang; Chang, Nicholas; Scassellati, Brian (March 2020, Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction)

   Team member inclusion is vital in collaborative teams. In this work, we explore two strategies to increase the inclusion of human team members in a human-robot team: 1) giving a person in the group a specialized role (the 'robot liaison') and 2) having the robot verbally support human team members. In a human subjects experiment (N = 26 teams, 78 participants), groups of three participants completed two rounds of a collaborative task. In round one, two participants (ingroup) completed a task with a robot in one room, and one participant (outgroup) completed the same task with a robot in a different room. In round two, all three participants and one robot completed a second task in the same room, where one participant was designated as the robot liaison. During round two, the robot verbally supported each participant 6 times on average. Results show that participants with the robot liaison role had a lower perceived group inclusion than the other group members. Additionally, when outgroup members were the robot liaison, the group was less likely to incorporate their ideas into the group's final decision. In response to the robot's supportive utterances, outgroup members, and not ingroup members, showed an increase in the proportion of time they spent talking to the group. Our results suggest that specialized roles may hinder human team member inclusion, whereas supportive robot utterances show promise in encouraging contributions from individuals who feel excluded. 

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4. Measuring Presence and Performance in a Virtual Reality Police Use of Force Training Simulation Prototype

   Garcia, Edward T; Ware, Stephen G; Baker, Lewis J (April 2019, Proceedings of the 32nd AAAI International Conference of the Florida Artificial Intelligence Research Society)

   In this paper we describe a virtual reality training simulation designed to help police officers learn use of force policies. Our goal is to test a training simulation prototype by measuring improvements to presence and performance. If successful, this can lead to creating a full-scale virtual reality narrative training simulation. The simulation uses a planner-based experience manager to determine the actions of agents other than the participant. Participants’ actions were logged, physiological data was recorded, and the participants filled out questionnaires. Player knowledge attributes were authored to measure participants’ understanding of teaching materials. We demonstrate that when participants interact with the simulation using virtual reality they experience greater presence than when using traditional screen and keyboard controls. We also demonstrate that participants’ performance improves over repeated sessions. 

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5. EPIC: Enhanced Privacy and Integrity Considerations for Research (Tutorial)

   https://doi.org/10.1145/3640544.3645249  

   Alzughbi, Hanna; Caine, Kelly; Iqbal, Mehtab; Knijnenburg, Bart; Lee, Hansen; McGregor, Susan E; Sidnam-Mauch, Emily (March 2024, ACM)

   This tutorial engages researchers in a series of collaborative activities towards Enhanced Privacy and Integrity Considerations (EPIC) for human subjects research in the artificial intelligence (AI) field. The tutorial aims to identify common challenges to study integrity, convey best practices for protecting participants at the point of study design, and discuss how to best design tools to support robust, privacy-enhancing human subjects research in AI. In particular, the tutorial provides hands-on training on how to determine sample size and collect participant demographics in a way that prioritizes data integrity, participant privacy, and sample representativeness. Tutorial participants discuss and troubleshoot the unique challenges to and opportunities for designing robust and ethical human-centered AI research. 

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