The work world is set to undergo major changes thanks to advancements in automation and artificial intelligence and is beginning to promote new forms of collaboration. The transition from a technologysupporting environment to a collaborative environment in which people and technology work together to achieve their goals requires a fundamental change in the way we design, build, and ultimately deploy information systems. Most work on information system design focuses on the effective augmentation of humans. However, little is known about constructing a sustainable mutually beneficial collaboration between human and machine. To better understand this relationship, we perform a case study drawing on ethnographic evidence collected during a multi-year design science research project with a major service provider for unit load device management in the air cargo industry that resulted in an artifact for human-machine collaboration (HMC). Our study takes a closer look at the co-evolution that emerges from the collaboration between human and artificial agents over time, in which both parties influence each other, the underlying tasks, and their environment. Our analysis reveals three facets of symbiotic co-evolution: agents' evolution, activity evolution, and structural evolution. The findings contribute to the HMC knowledge base and have implications for future HMC design initiatives.

The vision of a symbiotic partnership between humans and machines has existed since the 1960s. With this paper we provide the first conceptualization of the human-machine symbiosis (HMS) and make three important contributions: we present the fundamentals of HMS by focusing on objectives, requirements, and boundaries; we propose a framework for the design of HMS; and we review HMS research and, specifically, what the literature says with respect to whether HMS has already been achieved.

No abstract

In this paper, we present a method for systematic literature search based on the symbiotic partnership between the human researcher and intelligent agents. Using intelligence amplification, we leverage the calculation power of computers to quickly and thoroughly extract data, calculate measures, and visualize relationships between scientific documents with the ability of domain experts to perform qualitative analysis and creative reasoning. Thus, we create a foundation for a collaborative literature search system (CLSS) intended to aid researches in performing literature reviews, especially for interdisciplinary and evolving fields of science for which keyword-based literature searches result in large collections of documents beyond humans' ability to process or the extensive use of filters to narrow the search output risks omitting relevant works. Within this article, we propose a method for CLSS and demonstrate its use on a concrete example of a literature search for a review of the literature on human-machine symbiosis.

In this paper we propose and demonstrate a software tool for symbiotic human-machine analysis, applicable for structured literature reviews (SLR). We present a seed-based search of bibliographic information, resulting in document clustering and graph visualization. Through a collaborative human-machine effort we show how to detect potential bridging articles and paradigm shifts. The overarching goal is to support the SLR process, especially for developing fields of science, as well as interdisciplinary fields, where similar concepts can be overlooked as they are associated with different keywords and belong to different groups, yet share common ideas. Finally, we demonstrate the application of the tool with two literature search and visualization examples.