HUMANE TYPOLOGY AND METHOD
The HUMANE method is developed to complement a process for Human-Centred Design (HCD), and is supported by an online tool for HMN profiling and transfer of design knowledge. In this tool, HMNs can be profiled according to the HUMANE typology dimensions, to review similar HMNs and to identify relevant design considerations associated with other HMNs.
The target audience for the HUMANE typology and method include practitioners within ICT development and design, as well as researchers within fields approaching the phenomenon of human-machine networks.
The work on the typology and method has been presented in four scientific conference papers, all included in Springer proceedings. Two at HCI International 2016, one at HCI International 2017, and one at the International Conference of Man-Machine Interaction 2017.
CASE STUDIES
To validate the HUMANE typology and method, and to provide feedback to drive its development, eight case studies have been conducted across the two project iterations. In addition to serving this validation and feedback purpose, the cases have served to generate new knowledge of human-machine networks within and across the case domains. The case study work has also led to a number of scientific publications including papers published in Scientific Reports and PLoS ONE. A full overview of papers based on the case studies are listed on the HUMANE website (
http://humane2020.eu/publications/(s’ouvre dans une nouvelle fenêtre)).
TYPOLOGY-DRIVEN MODELLING AND VALIDATION
This Core HMN Model has been positioned within the HUMANE methodology to help with the evaluation of HMN designs. The Core HMN Model has been applied to two HMNs as a proof of concept to demonstrate the approach, showing that it is applicable to different HMNs and has generated impact by informing the design decisions for an HMN that is under development.
We have modelled design options for an HMN called Truly Media (under development), to determine how to best help journalists collaboratively verify user-generated content to avoid running stories based on content that consists of hoaxes, rumours or deliberately misleading information (e.g. propaganda, fake news, and other untrue statements).
We have also successfully modelled edit wars in Wikipedia and how increasing the agency of bots may address this emergent behaviour wherein two agents mutually revert each other. The simulation model was able to predict the emergence of edit wars with a 91.5% accuracy on average (as high as 100% for some time periods).
FUTURE THINKING AND ROADMAPS
Roadmaps for three social domains have been developed and promoted through HUMANE, for the domains of the sharing economy, eHealth, and citizen participation. All roadmap material is promoted and easily accessible at the HUMANE project website (
https://humane2020.eu(s’ouvre dans une nouvelle fenêtre))
The roadmaps present material for future thinking (key challenges, trends, and goals), strategic goals as well as key actions and priorities for achieving these goals. The roadmaps is presented through different channels to best impact policy makers, domain professionals, ICT designers, and researchers.
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