Defining human-AI teaming the human-centered way: a scoping review and network analysis.

Introduction: With the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals. Methods: The aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field. Results: Through network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date. Discussion: We propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems.

The Job Perception Inventory: considering human factors and needs in the design of human-AI work.

Introduction: Artificial intelligence (AI) is seen as a driver of change, especially in the context of business, due to its progressive development and increasing connectivity in operational practice. Although it changes businesses and organizations vastly, the impact of AI implementation on human workers with their needs, skills, and job identity is less considered in the development and implementation process. Focusing on humans, however, enables unlocking synergies as well as desirable individual and organizational outcomes. Methods: The objective of the present study is (a) to develop a survey-based inventory from the literature on work research and b) a first validation with employees encountering an AI application. The Job Perception Inventory (JOPI) functions as a work-analytical tool to support the human-centered implementation and application of intelligent technologies. It is composed of established and self-developed scales, measuring four sections of work characteristics, job identity, perception of the workplace, and the evaluation of the introduced AI. Results: Overall, the results from the first study from a series of studies presented in this article indicate a coherent survey inventory with reliable scales that can now be used for AI implementation projects. Discussion: Finally, the need and relevance of the JOPI are discussed against the background of the manufacturing industry.

Human-robot interaction: how worker influence in task allocation improves autonomy.

Task allocation research is often efficiency-focussed, but procedural and work-psychological perspectives are required to enable human-centred human-robot interaction (HRI). Hence, the motivational and cognitive outcomes of the degree of worker influence over task allocation are relevant to research objects for allocation process design. In a laboratory experiment, 87 participants manufactured goods in collaboration with a robot under three conditions: (1) a support system decided the allocation, (2) a support-system allocation could be revised, (3) the participant determined the allocation. Conditions affected mental effort, process control and autonomy, resulting in higher values when participants allocated tasks themselves. Satisfaction with the process appears lower with no worker influence. Trust in the support-system moderates the condition effect, with higher satisfaction depending on trust when a system is involved in allocation. An allocation made by the workers and adaptability is preferred. Results show the importance of worker influence over task allocation in HRI. Practitioner Summary: Our experiment on allocation processes seeks to satisfy the gap in human-centred psychological research on task allocation in human-robot interaction (HRI). For successful, ergonomic HRI, it is found that workers should be provided with influence over task allocation.

Psychological Effects of the Allocation Process in Human-Robot Interaction - A Model for Research on ad hoc Task Allocation.

Task allocation is immensely important when it comes to designing human-robot interaction (HRI), but although it is the shaping part of the interaction, it is merely regarded as a process with its own effects on human thinking and behavior. This study aims at linking research from different fields like psychological theory, HRI and allocation optimization to create a new process model of ad hoc task allocation in human-robot interaction. It addresses the process characteristics and psychological outcomes of a real-time allocation process that integrates the worker. To achieve this, we structured the process into steps and identified relevant psychological constructs associated with them. The model is a first step toward ergonomic research on the self-organized allocation of tasks in HRI, but may also be an inspiration for practitioners designing HRI systems. To create successful work in HRI, designing the technology is an important foundation, but a participative, thought-out process for allotting tasks could be the key to adequate autonomy, work satisfaction and successful cooperation.