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.


Procedure for profiling human-machine networks in the HUMANE profiling framework.

As machines take a more active role in human-machine networks (HMNs), they exert an increasing level of influence on other participants. Machines are not just passive participants in such networks, merely mediating communication between humans; technological advances allow greater autonomy and the performance of increasingly complex tasks.

robot-1241645While most psychology and sociological models only attribute agency to human actors, more recent models have been proposed that attribute agency also to machines, such as Actor-Network Theory (ANT) (Law, 1992) and the Double Dance of Agency (DDA) model (Rose & Jones, 2005). Nevertheless, agency definitions still seem insufficient, as Jia et al.. (2012) argue in the context of the Internet of Things (IoT). We have reviewed the existing literature on agency in the following paper that is available as a pre-print, proposing an updated definition of agency that is suitable for the analysis and design of HMNs.

Machine Agency in Human-Machine Networks; Impacts and Trust Implications, to appear in the 18th International Conference on Human-Computer Interaction, 2016.

Broadly speaking, we understand the agency of an actor, whether human or machine, as the capacity to perform activities in a particular environment in line with a set of goals/objectives that influence and shape the extent and nature of their participation. The environment in this context is bound by the HMN.

While machines cannot exhibit true direct personal agency, due to factors such as intentionality, they can exhibit agency in different ways. For example, it is useful to refer to machine agency in terms of the intentions of their human designers, as interactive technologies may be deployed to change human attitudes or behaviours  (Fogg, 1998). For example, in the field of affective computing, emotionally intelligent technologies are developed to respond and adapt to users emotional needs (Picard, 1995).

In practical terms, our definition of machine agency reflects the degree to which machine actors may a) perform activities of a personal and creative nature (e.g., supporting health care by personalising motivation strategies), b) influence other actors in the HMN, c) enable human actors to exercise proxy agency, and d) the extent to which they are perceived as having agency by human actors. Higher levels of machine agency imply a need to consider the implications of the machine’s role in the HMN, which relates to, e.g., the trust relationship between humans and machines.

Any relationship between human and machine agents must be based on trust and reliance in response to trustworthiness factors in machines (Mayer, Davis, & Schoorman, 1995; Schoorman, Mayer, & Davis, 2007). From health and safety monitoring to the smart gadgets in our homes, the increasing dependence on sophisticated technology implies a fresh look at concepts such as agency.


Accepting interactive collaboration as a real possibility, HMNs such as smart homes and advanced socio-technical robotics enable social engagement, encouraging the evolution of mutually supportive networks. For this to become a real and lasting possibility will require the development and maintenance of trust. Only on a trust basis, including a willingness to compromise, to forgive and learn how to overcome shared problems, will the full potential of HMNs become a reality. To become a reality, we argue that a revision of the original definition of agency was long overdue, not least to allow the full capabilities of sophisticated technology to combine and develop together in socially motivated HMNs limited only by human imagination.

We maintain that machine agency not only facilitates human to machine trust, but also interpersonal trust; and that trust must develop to be able to seize the full potential of future technology. For more information, please read our paper, which includes three case studies to discuss and show the trust implications pertaining to machine agency in HMNs.


Fogg, B. (1998). Persuasive computers: perspectives and research directions. In The SIGCHI conference on Human Factors in Computing (Vol. 98, pp. 225–232).

Jia, H., Wu, M., Jung, E., Shapiro, A., & Sundar, S. S. (2012). Balancing human agency and object agency: an end-user interview study of the internet of things. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (pp. 1185–1188). ACM.

Law, J. (1992). Notes on the Theory of the Actor-Network: Ordering, Strategy, and Heterogeneity. Systems Practice, 5(4), 379–393.

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model of Organizational Trust. The Academy of Management Review, 20(3), 709–734.

Picard, R. W. (1995). Affective Computing. MIT press.

Rose, J., & Jones, M. (2005). The Double Dance of Agency: A Socio-Theoretic Account of How Machines and Humans Interact. Systems, Signs & Actions, 1(1), 19–37.

Schoorman, F. D., Mayer, R. C., & Davis, J. H. (2007). An Integrative Model of Organizational Trust: Past, Present, and Future. Academy of Management Review, 32(2), 344–354.

First year of HUMANE

1st of April marks the anniversary of the start of our project!
In this post we give a short recap of our activities in HUMANE during its first year.

The Beginning!
What Are We Doing?
Literature Review
Case Studies
Dimensions and Concepts

The Beginning!

In the emerging hyper-connected era, people and appliances are online all the time. In HUMANE we focus on how work, private life, civic engagement, knowledge, creativity, and innovation are increasingly conducted in networks comprising of humans and machines. We’ve termed such networks “human-machine networks”, where network reflects a higher abstraction level than the technical system or machine.


The HUMANE kick-off meeting last year in Oslo.

The challenge is that human-machine networks cannot be developed and implemented in the same manner as networks of machine nodes alone. Creating successful solutions for human-machine networks requires awareness concerning the kind of network to be established (e.g. simple networking and information-sharing or complex collaboration), the social characteristics (e.g. non-existent, latent, weak, or strong ties), and a conceptual framework to assess the fit of the ICT solutions.

What are we doing?

The overall objective of HUMANE is to improve public and private services by uncovering how new configurations of human-machine networks change patterns of interaction, behaviour, trust and sociability, and how public and private services need to fit the specific networks involved.

To this end, HUMANE seeks to develop a HUMANE typology and method, tailored to a human-centred design process and applicable to ICT developers and designers, and a HUMANE roadmap tailored to the need to support future thinking, regulatory activities, and policy-making. During the first 12 months of the project, the first version of the HUMANE typology and method has been developed. We will start developing the HUMANE roadmap in the second year of the project.

Literature review


Journals publishing articles related to human-machine networks.

In order to develop a theoretically grounded typology, we needed to first review the literature. It wasn’t an easy task to scope and review the massive body of knowledge generated on human-machine networks over the past few centuries under various names and titles. Read how we did so and about our review paper here.



Our main task during the first year was to provide a typological framework, within which we can profile existing and/or hypothetical networks. The ideal typology should be simple, yet comprehensive enough to be able to capture and distinguish different cases in a meaningful way. It also has to provide the needed base for the second part of the project, the road map. The developed typology has to talk to the design patterns and the socio-temporal dynamics of networks. Yet it has to be abstract enough and easy to comprehend! We are happy to announce that we completed our first version of the HUMANE Typology just in time. Read about it and the corresponding paper here.

Case studies


Joint profile for the peer-to-peer reselling network of Snapsale.


Do you know that HUMANE is not solely a theoretical project? We have 6 case studies, through which we inspire, develop, and validate our typology. A by-product of this process is the new knowledge that we generate on each of these cases. For example, read about our work on the “dynamics of disagreement in Wikipedia” here.

Dimensions and concepts

Through developing the HUMANE Typology, we discuss various aspects and dimensions of human-machine networks. While some of these aspects are easier to grasp, like “geographical extension of the network”, others are more sophisticated and need further investigation. This has resulted in additional analytical publications. Read the one on “Machine Agency; Impact and Trust” here.


You know where to find our papers as well as a link to our Mendeley group!

And if you’re reading this you probably know our website, but you might also want to check our Twitter account, and you might even want to follow us!

One of the main objectives of the HUMANE project is to come up with a typology and profiling framework of Human-Machine Networks. We just finished a paper based on the first revision of our typology. The paper is going to be presented at the 18th International Conference on Human-Computer Interaction International, Toronto, Canada, 17 – 22 July 2016. A pre-print of the paper is available here, the abstract of which reads: 

In this paper we outline an initial typology and framework for the purpose of profiling human-machine networks, that is, collective structures where humans and machines interact to produce synergistic effects. Profiling a human-machine network along the dimensions of the typology is intended to facilitate access to relevant design knowledge and experience. In this way the profiling of an envisioned or existing human-machine network will both facilitate relevant design discussions and, more importantly, serve to identify the network type. We present experiences and results from two case trials: a crisis management system and a peer-to-peer reselling network. Based on the lessons learnt from the case trials we suggest potential benefits and challenges, and point out needed future work.


Joint profile for the peer-to-peer reselling Human-Machine Network of Snapsale

We just posted a new pre-print titled: Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration.

Disagreement and conflict are a fact of social life and considerably affect our well-being and productivity. Such negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. We overcome this challenge by investigating the patterns in the timing and configuration of contributions to a large online collaboration community. We analyze sequences of reverts of contributions to Wikipedia, the largest online encyclopedia, and investigate how often and how fast they occur compared to a null model that randomizes the order of actions to remove any systematic clustering. We find evidence that individuals systematically attack the same person and attack back their attacker; both of these interactions occur at a faster response rate than expected. We also establish that individuals come to defend an attack victim but we do not find evidence that attack victims “pay it forward” or that attackers collude to attack the same individual. We further find that high-status contributors are more likely to attack many others serially, status equals are more likely to revenge attacks back, while attacks by lower-status contributors trigger attacks forward; yet, it is the lower-status contributors who also come forward to defend third parties. The method we use can be applied to other large-scale temporal communication and collaboration networks to identify the existence of negative social interactions and other social processes.


An example of all reverts done in the English language Wikipedia within one day.


A pre-print of our review article on human-machine networks is now available on arXiv. The article is entitled “Understanding Human-Machine Networks: A Cross-Disciplinary Survey” and is currently under peer review. In the article, we systematically survey high-impact and recent articles from many different disciplines using scientometrics. We particularly focus on design challenges associated with different types of human-machine networks.


Journals publishing articles related to human-machine networks. The colored dots are Scopus journals that include articles with the key terms we identified; the background uncolored dots do not include such articles.

Here is the abstract:

In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations.The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

Social Network

Social Network by Kevin Dooley:

We recently completed a review of the existing literature on human-machine networks.  Human-machine networks are complex systems of human actors and computing devices or sensors that interact to produce synergy. In other words, human-machine networks result in outcomes that neither a human social network, nor a computer network can produce independently.  We often use computers to communicate with others but most of the time the computers are simply a medium for an interaction that could have otherwise occurred face-to-face. E-mail and phone conversations are good example for machine-mediated communication. Sometimes, however, computers can radically transform how we interact and what we produce.  Our review focuses exactly on those network instances. We identified eight different types of human-machine networks: public-resource computing, crowdsourcing, web search engines, crowd sensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. For each type we collected articles that concern issues related to the design of such a network. We then selected 10-20 articles that are either new and promising or already well cited. These articles serve as a good starting point for those who want to learn more about each network type. The list of articles can be found here. For a broader introduction to the topic of human-machine networks, we are also sharing our Mendeley reference collection.

Project Starts

The Project has officially started on 1st April 2015!