Work and leisure are increasingly conducted within the context of networks of humans and machines. In these networks, we are provided extended capacities for communicating and interacting with others, connecting with friends through social media or collaborating with colleagues through online project places. Furthermore, we increasingly appreciate machine nodes as active agents in our networks, intelligently filtering the content to which we are exposed, providing decision support from myriad of underlying sensors, or acting as collaborators or opponents in online games.
With this growing importance of human-machine networks, supporting the purposeful design of such networks becomes ever more important. In HUMANE we aim to fill a gap in the literature on human-centred design, targeting the design for constellations of humans and machines. Much used resources already exist on the design of user interfaces as well as software and hardware systems. There also exist worthwhile resources on the design of specific forms of human-machine network, such as the design of social networks. What is lacking, however, is an approach that supports learning and transfer of design knowledge across sectors or categories of human-machine networks.
As a first step towards such an approach, we are in HUMANE developing a typology of human-machine networks. In a recently released HUMANE_typology_and_method, we present the initial version of the typology and profiling framework. The typology and profiling framework has been applied in trial analyses using the six use-cases of HUMANE as test cases. We present the outcome of this trial analysis, including key implications of particular HMN profiles for motivation, collaboration, and trust.
Highlights from the typology and profiling framework has also been presented at the HCI International conference in Toronto, June 2016. If you prefer a shorter read than the entire report, you find the paper here.