Familiarity with sharing views and opinions via social media provides a possible mechanism for citizens to engage with politicians and take part in shaping policy

We all recognise that the reach of the Internet should add so much so the democratic processes [1,2]. But it turns out that there are still differences between on- and offline discussions [3], since although people are used to social networks for personal, leisure and retail activities, participatory behaviours are different when it comes to politics [2,4].

For instance, the characteristics of debate seem to differ. It appears that socio-technical systems extend but don’t really improve the quality of that debate [5]. The goal for eParticipation probably isn’t really about making political decisions or achieving consensus, but instead getting members of one group to refine what they think [6]. So the tools provided to support discussion have a careful balance to strike: they should be focused on encouraging as much participation as possible and not exclude those less comfortable online [5].

And so how do we know what an ‘ePerson’ – the citizen online – actually is and what they do [3]? Significantly, it seems that social identity affects how they interact with others [6]. That being so, these discussions will be open to peer influence [7,8]. How all of the social, technical and democratic-process concerns interact really needs to be carefully analysed and understood [2,9].

In the wake of surprising voting activities in the past year, have a look at where stakeholders think Citizen Participation online is going in our white paper.

[1]      O. for E. Co-operation and Development, Promise and problems of e-democracy: Challenges of online citizen engagement. OECD Publishing, 2004.

[2]      S. Coleman and D. F. Norris, “A new agenda for e-democracy,” 2005.

[3]      P. K. Dutt and T. Kerikmäe, “Concepts and problems associated with eDemocracy,” in Regulating eTechnologies in the European Union, Springer, 2014, pp. 285–324.

[4]      P. Panagiotopoulos, S. Sams, T. Elliman, and G. Fitzgerald, “Do social networking groups support online petitions?,” Transform. Gov. People, Process policy, vol. 5, no. 1, pp. 20–31, 2011.

[5]      E. Loukis and M. Wimmer, “A multi-method evaluation of different models of structured electronic consultation on government policies,” Inf. Syst. Manag., vol. 29, no. 4, pp. 284–294, 2012.

[6]      D. Kreiss, “The problem of citizens: E-democracy for actually existing democracy,” Soc. Media+ Soc., vol. 1, no. 2, p. 11, 2015.

[7]      J. Ronson, So You’ve Publically Shamed. Oxford, England: Picador, 2015.

[8]      C. Stott and S. Reicher, Mad Mobs and Englishmen?: Myths and realities of the 2011 riots. London, UK: Robinson, 2011.

[9]      A. Macintosh and A. Whyte, “Towards an evaluation framework for eParticipation,” Transform. Gov. People, Process policy, vol. 2, no. 1, pp. 16–30, 2008.

Post updated August 3, 2017, with link to revised version of roadmap.

The sharing economy, a.k.a. collaborative economy, is expected to see substantial growth in Europe the coming years; a much cited PwC study estimates that the value of sharing economy transactions in 2015, at 28 billion Euro, will increase to a potential value of 570 billion Euro in 2025.

Nevertheless, important challenges remain on the road from the sharing economy as an emerging current phenomenon to sharing being established as a mainstream mode of consumption in the European population. As shown in a TNS survey, only about one fifth of Europeans had used sharing economy services in 2016, and nearly half were unaware of such services

To realise the potential of the sharing economy we need to strengthen our knowledge of the challenges and opportunities ahead, and identify ways to address these. Sharing economy services are good examples of human-machine networks, as a key characteristic of these is their networked character in which the collaboration of human actors is enabled by intelligent digital platforms.

Hence, we have applied the HUMANE typology for human-machine networks to discuss how sharing economy services may develop in the near future, and how to guide this development. Specifically, we have applied the four analytical layers of the HUMANE typology: actors, relations, network extent, and network structure to guide an interview study of 19 sharing economy service owners, researchers, and policy-maker representatives.

The outcome of the study is summarised in the short white paper Roadmap for human-machine networks in the sharing economy.

Post updated August 3, 2017, with link to revised version of roadmap.

In the networked society, the people and the machines we rely on are approaching a pattern of always on, always connected. Our activities at work and in our private lives increasingly depend on the networks of humans and machines. The proliferation of technology and increasingly complex interplay between humans and machines bears significant challenges for the design of what we refer to as human-machine networks (HMNs). We expose and address the challenges via the HUMANE method.

We observe that the successful outcome of highly diverse activities such as industrial innovation processes, sharing economy transactions, citizen science, and news verification practices are increasingly dependent on HMNs. However, for European industry and public sector to benefit from efficacious HMNs, we need to understand the key characteristics of such networks, and how these characteristics in turn affects people’s experience and behaviour in the networks, and how the networks evolve. For example:

  • How does the increasing reliance on machine intelligence in HMNs affect privacy and trust perceptions?
  • How does the centralized organization of platforms for open innovation or citizen science affect motivation to contribute?
  • How to ensure constructive collaboration between humans and bots in platforms such as Wikipedia?

A key assumption in HUMANE is that the purposeful design for HMNs will benefit from understanding the characteristics of the HMN in which it is to support. By complementing socio technical systems design and human-centred design, we seek to understand HMNs not only as particular and contextually embedded, but as holding a set of generic characteristics which may apply to HMNs across application areas and domains. We have captured key characteristics within the HUMANE typology, which includes the network actors (human and machine) and their relations, as well as the network structure and extent.

We also need new approaches to the design of HMNs. Through traditions such as socio-technical systems design and human-centred design we are provided frameworks for analysing the context of technology design and processes for involving users and stakeholders in the design process to ensure effective and efficient work support. However, designing for networks of humans and machines remains challenging.

Examples studied by HUMANE researchers include online innovation platforms that fail to strengthen innovation capabilities (Lüders, 2016), how procedures for including and excluding people in online collaboration may work against their intentions (Rudas et al., 2017), and how the excessive use of bots in a network for collaboration may change or, at worst, challenge collaborative culture (Tsvetkova et al., 2017). Failing to design for purposeful participation and interaction within HMNs may threaten both the value of investment in a design, but also potentially represent lost opportunities to improve on the quality and competitiveness of European society.

To make the HUMANE typology actionable for analysis and design for HMNs, we have developed a five-step process for characterisation and analysis of the HMN, as well as the transfer of design knowledge and experience. The process is referred to as the HUMANE method, which includes the following:

  1. Context and scoping: Explicate the purpose and objectives of the HMN.
  2. Network characterisation: Establish the HMN profile through the typology dimensions and identify similar HMNs.
  3. Implication analysis: Analyse key implications of the HMN profile in terms of user experience and motivation, behaviour and collaboration, innovation and improvement and/or privacy and trust.
  4. Design considerations: Cross-domain identification and utilization of design knowledge and experience through a design pattern approach. Design knowledge is shared through what we refer to as design considerations.
  5. Evaluation: Assess and refine current design proposals on the basis of the input from the HUMANE approach.

Overview of the HUMANE method, mapped to the phases of the human-centred design process.

The typology and method is intended as a supplement to the human-centred design process (ISO 2010), addressing analysis, requirements, design, and evaluation. Specifically, the typology and method is intended to support initial strategic reflections and considerations at the outset of the design process. The typology and method have been applied and evaluated in eight collaborating cases within the HUMANE project. However, this is not limited to the design process. One of the key benefits include facilitating cross-disciplinary communication, providing a framework for discussing and understanding HMNs in general. For example, it has been used as part of a process that has led to three policy-focused roadmaps for future HMNs in the areas of the sharing economy, e-health and citizen participation.

For a more detailed overview of the HUMANE typology and method, including an application example, please see our method white paper.

Engen, V. and Følstad, A. (eds) (2017). The HUMANE typology and method – supporting the analysis and design of human-machine networks. Zenodo.


ISO. (2010). Ergonomics of human–system interaction — Part 210: Human-centred design for interactive systems. Geneva, Switzerland: International Organization for Standardization.

Lüders, M. (2016). Innovating with users online? How network-characteristics affect collaboration for innovation. Journal of Media Innovations, 3(1), 4–22.

Rudas, C., Surányi, O., Yasseri, T., & Török, J. (2017). Understanding and coping with extremism in an online collaborative environment: A data-driven modeling. PLoS ONE 12(3): e0173561. https://doi.org/10.1371/journal.pone.0173561

Tsvetkova, M., García-Gavilanes, R., Floridi, L. and Yasseri, T. (2017). Even good bots fight: the case of Wikipedia. PLoS ONE 12(2): e0171774. https://doi.org/10.1371/journal.pone.0171774

If you’re working on the design of a Human-Machine Network, you may wonder what would happen if you implemented a particular network design. It may look promising, but what would happen in practice? How would it affect people’s behaviour? Would it provide the benefits you hope for? We can help answer such questions via simulation modelling.

The HUMANE typology (Følstad et al., 2015, 2016, 2017) allows us to characterise Human-Machine Networks (HMNs) at a high level, to understand, analyse and communicate key aspects of the network pertaining to both their design and how they may be used by the humans and machines that make up the network.

We have created a method for applying the HUMANE typology to the design process, which is intended as a supplement to the human-centred design process (ISO, 2010). In order to further help understand the potential impact of the design options that may emerge from following the HUMANE method (Følstad et al., 2017), we have explored an approach to modelling and simulation HMNs.

Modelling and simulating HMNs is non-trivial. They are complex networks prone to unpredictable and emergent behaviour stemming from the interactions between both humans and machines. Therefore, we have developed a Core HMN Model to aid the modelling task. This model reflects key aspects of HMNs captured in the HUMANE typology to describe the actors, their interactions and structure of the network. A conceptual view of this model is shown below in Figure 1.


Figure 1 – Class hierarchy of entities of the Core HMN Model

At the most basic level, an HMN can be considered as a collection of Nodes and Edges that are connected in a network. A Node, also known as a vertex, could be one of two types: an Artefact or an Agent. The key difference between these two is that the latter has agency and the former does not, representing, e.g., a file, a forum post or a Wikipedia article. Depending on whether we talk about “conscious intentionality” or “programmed intentionality” we can distinguish between Human and Machine agents and attribute agency to both as active participants in HMNs as per the HUMANE typology (Følstad et al., 2015, 2016, 2017).

An Edge is a link between two Nodes, signifying that there are one or more types of relationship between the two Nodes. The nature and properties of the respective relationships, such as trust and trustworthiness, influence the interactions between the two agents, which is encapsulated within a Connection (from the Agent to the Edge, see Figure 2). We have included a Connection entity to reflect i) the possibility that there may be multiple relationships between two nodes, and ii) the possibility that the relationship properties from Node A to B may be different from Node B to A. For example, if nodes A and B are both human agents, one person may trust the other more than the other trusts them back. Consequently, their actions may differ when they interact with one another.



Figure 2 – Illustration of two nodes with their connections to two directional edges.

The Core HMN Model has been applied to two HMNs as a proof of concept to demonstrate the approach. We have 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 core model was extended to include human contributors and bots; both of which have the ability to create new articles, edit existing articles and revert contributions. 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). With the aim of increasing the reliability and quality of information in Wikipedia, we simulated increasing machine agency by introducing a bot with the capability to detect edit wars and notify agents to end ongoing edit wars. By doing this, we observed a significant reduction in the duration of edit wars.

We have also modelled design options for an HMN that is under development, Truly Media, 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). The core model was extended to include journalists (human) interacting with a conflict resolution tool (machine) part of Truly Media platform to verify Twitter content (artefact). While the simulation results showed a positive impact of the conflict resolution tool, sophisticated approaches to evaluate users’ credibility, for example, had a negligible impact on the verification process. As such, the simulation model helped inform the prioritisation and implementation of features in the platform.

Interested readers will be able to read the full report on this work on the HUMANE website later this year when it has been formally reviewed by the EC. Until then, you may reach out to us by email for a copy.

The Core HMN Model is freely available as an open source Java library on GitHub.


Featured image credit: http://www.presentermedia.com/


Følstad, A., Eide, A. W., Pickering, J. B., Tsvetkova, M., Gavilanes, R. G., Yasseri, T., & Engen, V. (2015). D2.1 Typology and Method v1.

Følstad, A., Engen, V., Mulligan, W., Pickering, B., Pultier, A., Yasseri, T., & Walland, P. (2017). D2.3 The HUMANE typology and method.

Følstad, A., Engen, V., Yasseri, T., Gavilanes, R. G., Tsvetkova, M., Jaho, E., … Pultier, A. (2016). D2.2 Typology and Method v2.

ISO. (2010). Ergonomics of human–system interaction — Part 210: Human-centred design for interactive systems. Geneva, Switzerland: International Organization for Standardization.



Photo: JLogan – Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=2668394

It is a common cry, particularly around the time of elections and referenda, that ordinary citizens don’t vote and they don’t engage directly with their elected representatives. Many observers now turn to the power of social media hoping to solve what is seen as a problem of democracy.

They want to encourage ordinary on-line citizens to get more involved in the political scene surrounding government and policy that affects them. Although this is the hope, it is more difficult to initiate than at first appears. People talk to each other over social media, they broadcast their opinions to the world over twitter and they track current affairs as reported by news websites, but they don’t engage directly in the democratic process. If Twitter and other social networks have the power of influence  [1], then by its agency citizen participation can potentially move to another level. And we are not just thinking here about the human participants in the network, but also about the role played by machines and their potential intervention in network-based exchanges [2-4].

In this context, we can validly ask the question of where we think that citizen participation HMNs are headed. We need to avoid generating suspicion around unexpected government use of online engagement [5-8], and go back to those most directly involved in citizen participation, asking questions like: Who is involved? Who needs to be involved? And what do they expect from the network?

With that in mind, we approached attendees from a series of related workshops. In response to the questions we raised via anonymised survey, they told us:

  • Online engagement is common: digital literacy is not an issue;
  • Lack of governmental involvement is a frustration: citizens will talk, but they expect to be listened to;
  • It’s the citizens themselves, and not just government organisations or their representatives that are regarded as key players: what citizens are talking about is important.

Some of the responses we got back were as we might have expected, but some were not. Trust is obviously the number one issue in everyone’s mind. The public don’t trust politicians to listen to them. And the politicians recognise that trust is hard to gain when the whole business of politics is the conflict between differing proposals and ideologies. But what was interesting is that the highest ranked motivation to improve participation from all parties was to create a culture of engagement amongst the public, so that they don’t feel that such engagement with politicians is pointless or ineffective. This understanding tells us where the network needs to be. We can therefore begin to map out the citizen participation HMN, but significantly, starting from a target destination for the roadmap, and not where it’s starting from.

If we know where we are trying to get to with the roadmap, then we need to know how we intend to get there. With that in mind, the HUMANE methodology provides valuable insights. The dimensions, as described by [9], are the levers that can be adjusted to effect change in the network. The levels of human and machine agency, the tie strength between actors, the network extent and the network structure can all be developed and adjusted to create different network outcomes such as the meta-dimensions of trust and motivation – clearly the most important identified drivers for success. You cannot tell people to have more trust, but by changing the levels of agency and tie strength so communication and self-efficacy can be improved, potentially leading to greater trust and confidence. Similarly engaging others through changing the extent of the network and the structure, allowing participants more freedom for communication paths (bottom up) rather than dictating behaviour (top down) has the potential to improve motivation. These strategies – which include descriptions of when and how they may be of use – provide on the one hand indications of the types of challenges which the HMN may face in reaching the goals identified by stakeholders in our survey, and on the other, ways to circumvent or handle those challenges.

Bringing together our survey results with HUMANE allows us to approach roadmapping for citizen participation HMNs in a rather different way. We are less concerned with where we are now. Instead, using HUMANE outcomes, we can form an understanding of where actors in the network want to be and offer them guidance and strategies to achieve their goals and overcome potential hurdles along the way.


[1] W.L. Bennett, “The personalization of politics political identity, social media, and changing patterns of participation,” The ANNALS of the American Academy of Political and Social Science, vol. 644, no. 1, 2012, pp. 20-39; DOI 10.1177/0002716212451428.

[2] D. Murthy, et al., “Automation, algorithms, and politics| bots and political influence: a sociotechnical investigation of social network capital,” International Journal of Communication, vol. 10, 2016, pp. 20.

[3] A. Bessi and E. Ferrara, “Social bots distort the 2016 US Presidential election online discussion,” First Monday, vol. 21, no. 11, 2016; DOI 10.5210/fm.v21i11.7090

[4] P.N. Howard and B. Kollanyi, “Bots,# StrongerIn, and# Brexit: computational propaganda during the UK-EU Referendum,” Browser Download This Paper, 2016.

[5] S. Colombo, “The GCC and the Arab Spring: A tale of double standards,” The International Spectator, vol. 47, no. 4, 2012, pp. 110-126; DOI 10.1080/03932729.2012.733199.

[6] P.N. Howard, et al., “Opening closed regimes: what was the role of social media during the Arab Spring?,” Available at SSRN 2595096, 2011.

[7] C. Stott and S. Reicher, “Mad Mobs and Englishmen?: Myths and realities of the 2011 riots,” Book Mad Mobs and Englishmen?: Myths and realities of the 2011 riots, Series Mad Mobs and Englishmen?: Myths and realities of the 2011 riots, ed., Editor ed.^eds., Robinson, 2011, pp.

[8] K. Glasgow and C. Fink, “Hashtag lifespan and social networks during the London riots,” Social Computing, Behavioral-Cultural Modeling and Prediction, Springer, 2013, pp. 311-320.

[9] A. Følstad, et al., D2.2: Typology and method v2, 2016.

…The main objective of the HUMANE survey was to engage practitioners, such as policy makers, domain professionals, user groups, IT experts and researchers in the development of the roadmaps in the domains of sharing economy, eHealth and citizen participation, to collect their feedback and input, as well as to validate or challenge the work that has done for the HUMANE roadmaps so far…

In the course of the HUMANE project we examine important social domains, where human-machine interaction is expected to be significant in the future, and studied in more detail the type of interactions, the roles of humans and machines, and the challenges that must be addressed to ensure the successful integration of machines in a way that is beneficial for society. In order to overcome these challenges, there is a need for a concerted effort between different stakeholders. Building on this work, we have developed a preliminary version of roadmaps for future HMNs in different social domains focusing on the goals to reach in each domain and the steps needed by the different stakeholders to achieve these goals.

The focus of the roadmaps has been on three domains: the sharing economy, eHealth and citizen participation. These are domains embracing exciting technological applications, which promise to bring great benefits to the economy and society. The roadmaps are a systematic effort towards understanding the goals for HMNs in various domains, proposing concrete solutions, and aiding stakeholders in recognizing shared goals and their roles in reaching them.

A roadmap is a product of a collaborative process and it would be optimal to arrive at a consensus on all issues, or at least to represent and try to incorporate all different opinions. The stakeholders’ involvement helps to identify more detailed objectives, other appropriate design strategies or examples of design strategies that have already been applied in specific products or use cases.  As such the feedback of stakeholders was crucial, particularly in determining in more detail the tasks that need to be performed, the timeline and resources.

The inclusion of stakeholders was important in the work of the HUMANE project as they have a central role in the development of roadmaps for HMNs in these social domains. The main objective of the HUMANE survey was to engage practitioners, such as policy makers, domain professionals, user groups, IT experts and researchers in the development of the roadmaps in the domains of sharing economy, eHealth and citizen participation, to collect their feedback and input, as well as to validate or challenge the work that has done for the HUMANE roadmaps so far.

Overall 75 people have participated in our online survey. Below, we summarize the major conclusions for each of the examined social domains.

The sharing economy has emerged as a new way of accessing goods and services, and the respondents expect this trend to be strengthened in the next 12 months. Among identified motivational factors for consumers in sharing economy services, financial gains are considered to be the most important. This is in line with previous studies on sharing economy services (Hamari et al. , 2016).

Screen Shot 2017-06-12 at 11.30.12

The most interesting expectation of the respondents for the near future seems to be that sharing economy services will challenge and directly compete with traditional service providers. This may either lead to sharing economy service providers growing to dominate a sector (such as Uber seems to be doing in ride sharing in many locations) or existing players acquiring successful start-ups and then raising barriers to new entrants in the market so as to protect their own position and investments.

Another interesting expectation which was considered important according to the respondents is the need for substantial changes in public policy and regulation to accommodate sharing economy services. This has also been acknowledged on European level and was addressed in the European Agenda for the collaborative economy.[1]

To change consumer behaviour patterns towards sharing and collaborative consumption was considered as a key challenge. This is due to the fact that it concerns the discontinuity of habit (such as moving from buying new to buying second-hand, moving from individual ownership to shared ownership). Another key challenge according to the respondents was to strengthen security and privacy in sharing economy services. Thus solutions in such services must guarantee the security and integrity of their users’ personal data.

Infrastructure and technology providers (providing the equipment for machine agency) as well as consumers (representing human agency) seem to be important groups in shaping sharing economy services in the future. This confirms the HUMANE’s objectives of analysing HMNs in terms of human agency and machine agency among the other identified layers. The importance attributed to regulation in order to ensure for that services are in line with consumer and employee rights is in the obvious sense of recognizing that emergent socio-technical systems inevitably require some sort of standards and responsible bodies if they are to function and grow in the long term.

In the domain of eHealth, the most interesting topic to the respondents who participated in the survey was medical devices, followed by mHealth apps. It is to be noted, however, that medical devices can have embedded mHealth apps, and in the future more and more of these devices will be devices connected to the Internet. Therefore we may consider that these topics form a wider topic. The importance attributed to such devices was expected, as they can be used by the patients themselves, and therefore have a larger chance to become part of everyday life for many people, and not just medical professionals.

Screen Shot 2017-06-12 at 11.30.29

The large importance attributed by the participants in the survey to such devices also confirms the HUMANE consortium’s decision to focus on such devices for the eHealth roadmap, rather than other eHealth topics. The importance attributed to privacy and confidentiality of medical information can also be attributed to the fact that this topic directly concerns individuals, who fear that their sensitive medical information can fall into the wrong hands. This is in line with the greater awareness that people now have about online privacy risks, following the publicity that the topic has recently acquired (allegations for mass surveillance programs, publication of cases of selling medical data, etc.) On the other hand, the fact that security is considered a less important issue can be due to the fact that it is often confused with privacy, although the terms have a separate meaning.


On the level of difficulty and expected duration of each proposed action, we get some counter-intuitive results. What we considered as one of the most difficult tasks, developing eHealth services with guaranteed QoS, is not shared by the majority of participants. On the other hand, they considered as demanding tasks to perform clinical validations and updating the regulatory framework. It is also worth noting that only very few of the actions were considered to require the highest implementation period (between one and two years). We will come back to this issue as we report the focus groups results in the next section.

The results also attributed the leading role for the implementation of most actions to researchers. The fact that the majority of the survey participants so far comes from the academia, may also bias these results. On the other hand, all the respondents recognised the collaborative work that must be done for all actions, since they considered all stakeholders necessary for all actions.

In the domain of citizen participation, public discussion is the most frequently reported form of citizen participation. Such discussion should take place at early stages of policy making, so that ideas can be formulated from the people themselves. The majority of the respondents reported to use social media channels to discuss issues of public participation with other citizens and mostly blogs and social networks (Twitter, Facebook, etc.).

Among identified barriers towards a citizen participation network, the lack of interest from both politicians and citizens about the process and the final results was considered to be the most important. It may be speculated that this, perhaps, is due to the fear of technology amongst some of the older generation of politicians and citizens.  Also, the kind of discussion that goes on at the level of social media is seen by many to be totally removed from the type of policy discussions that are essential at the level of governance. Thus citizen proposals may be viewed by policy makers as ‘superficial’ and not ‘adequately grounded’. Another reason for not taking into account citizen input can be the delays incurred, since citizen ideas and requirements may be hard to implement.

Screen Shot 2017-06-12 at 11.33.06

The openness and transparency was considered as the most important opportunity created in this domain. Among the challenges identified, trust was considered very important for an effective citizen participation network. This is also related to the lack of openness and transparency and confirms the large importance for this opportunity to be taken into account.

In regard to stakeholders, interestingly it is the citizens themselves who are regarded as most important: they and non-governmental agencies presumably provide the impetus for debate, of course. So their engagement is crucial. Equally, that policy makers are seen as the least important – even less than the technical system designers – suggest that the overall objective of such citizen participation networks is public debate, not shaping policy.

The document with all the detailed results will be made available on https://humane2020.eu/publications/  in July 2017.  Prior to this, please contact m.klitsi@atc.g  for an individual copy.


[1] European Commission (2016). A European agenda for the collaborative economy. Available at: http://ec.europa.eu/DocsRoom/documents/16881


Hamari, J., Sjøklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 67(9), 2047–2059. http://doi.org/10.1002/asi.23552

The advancements in micro/nano-, bio-technology, data management and telecommunications are revolutionizing the provision of healthcare services worldwide.

Healthcare services assisted by telecommunications and electronic equipment, also known as eHealth services, include Electronic Health Records (EHRs), telemedicine networks and applications (including telesurgery) and networks for physiological monitoring of patients with smart mobile or wearable devices. Assisted by technological developments, the healthcare community is moving toward early  detection  of  diseases,  health  status monitoring,  healthy  lifestyle,  and  overall  quality  of  life.

Today, there are devices and applications for the management of chronic diseases, back problems, biochemical indices, heart problems, and many other medical conditions. Such personalized eHealth services can benefit the entire community by improving access to care, quality of care and by making the health sector more efficient.

The interaction of humans and machines for the provision of healthcare services poses several challenges: quality provisioning, the efficient management and protection of personal medical data, and the economic and social sustainability of the services. Higher machine agency in a domain were human relationships were traditionally predominant necessitates the establishment of human trust in machine operation and capabilities. Moreover, for a massive uptake of such services it is essential to motivate people for behavioural changes, and make the services affordable at low cost.

In a short paper, we provide an overview of the challenges and envisaged actions at European level for the efficient integration of personalized eHealth systems, devices and applications in human life and societies. The study is based on the Roadmap for eHealth Human-Machine Networks (HMNs), which was developed in the course of the HUMANE project.

Read the paper here.

Post updated August 3, 2017, with link to revised version of roadmap.

Image based on artwork from MaxPixel (CC0 Public Domain)

The sharing economy has seen much interest as an alternative to traditional models for ownership and consumption of products and services. From the perspective of HUMANE, the sharing economy is of particular interest as a phenomenon enabled by networking of humans and intelligent digital machines.

Botsman defines sharing economy as an economic system based on sharing underused assets or services, for free or for a fee, directly from individuals. As pointed out by Belk, sharing behaviour is as old as humankind itself. However, in a networked society sharing supported by digital services becomes a whole new phenomenon with disruptive potential. While the value of the sharing economy today is relatively modest, it is expected to grow exponentially in the coming 10 years.

As part of the HUMANE work to support future thinking, we are in the process of developing a roadmap for human-machine networks supporting sharing economy services. To do this, we have applied the HUMANE typology dimensions to analyse aspects of such networks pertaining to its actors and their relations, as well as network structure and extent. The analysis is based on interviews with sharing economy service owners, policy maker representatives, and independent researchers. Here, we provide some example insights from this work.

The actor perspective: Human and machine actors in sharing economy services hold highly different roles. The human actors, typically unskilled or unprofessional, are providers and seekers in the two-sided market of the sharing economy. The machine actors are the matchmakers, predicting who is in need of the provided goods or services, and supporting the sharing process. Interestingly, as service owners want to provide efficient and easy sharing processes , the tasks and activities of the human actors are typically restricted or streamlined. In contract, the machine actors take on an increasing range of tasks requiring machine intelligence. For example, owners of online redistribution markets seek to reduce the work needed to upload ads and communicate with potential buyers by leveraging artificial intelligence for image recognition and text prediction; improving quality through reducing human agency and increasing machine agency.

The relation perspective: Whereas sharing behaviour requires trust between the actors in the sharing economy, sharing economy services do not seem to particularly encourage human actors to establish social ties. In the pre-internet era, sharing behaviour would typically require strong social ties between those that share goods or services. Not so much so in todays networked world. Botsman has discussed this phenomenon as layered trust, where the trust among individuals in the sharing economy depend on trust in the sharing platform. In our interviews, we find that the service owners have similar insights and seek to strengthen the relation between the individuals and the services, rather than to strengthen the relation between individuals. For sure, the positive experience of a sharing encounter with another person is seen as valuable; as is the users’ ratings and feedback to each other. Nevertheless, group formation is typically not strongly encouraged and the aim of the service owners is to support matching of strangers.

The future of sharing economy services will likely see the strengthening of the trends of increased machine agency and strengthening of the relationship between users and the platforms. An implication of this is the benefit of size and market share. Larger service owners will have better access to user data, enabling stronger AI-support in the sharing process. Larger service owners may also be better equipped to serve as a trusted platform for sharing. For newcomer platform providers, it will be critical to identify alternative means of supporting intelligent matching and predictions, as well as smart ways of building trust without the benefit of a household name.

Putting People Centre Stage

What do networks of humans and machines actually do?

We expend a lot of time and energy, especially in a project like HUMANE, trying to understand the ‘what’ and the ‘how’ of human-machine networks, but it is during a workshop such as the recent, excellent, discussions in Oxford that bring to the fore the question of ‘why’.

We are apt to think of the machines in the network as the important feature – after all, the humans have been there all the time, it is the machines that are the innovation. Aren’t they? Maybe not. As Eric Meyer reminded us at the start of his talk, people have been building machines ever since they climbed out of the trees and started banging rocks together. We may not think of a piece of bent stick as a machine, but the use of a tool to dig furrows and plant seeds heralded a major social shift from nomadic to agricultural lifestyles.

Dave De Roure furnished us with more examples, citing the printing press and its social impact in 15th Century Europe leading to the libraries and social records we have today. So does this make the printing press a social machine, in line with the definitions coming from Tim Berners-Lee et al., where he defines social machines as abstract entities living on the web that do the ‘heavy lifting’ of administration, leaving the people to be creative? Or is the concept more abstract still? Whereas the plough-share enabled the people using it to be more productive by making a task more manageable, it also permitted a social change as a consequence of the introduction of a different way of life that was not possible before the machine arrived.

Similarly, but perhaps not so obviously, the printing press caused social change. People could write and distribute their ideas before the printing press arrived, but if they wanted to distribute their ideas widely they were reliant on monastic scribes to create copies. With the arrival of the printing press it became very easy to replicate and distribute ideas in print without involving the monks. This is very much in line with the new forms of social process that Berners-Lee also associates with social machines, and has obvious corollary with the social changes brought about by the rapid expansion of social media at the start of this century.

Of course, we have to ask whether all such changes are beneficial, and who defines what ‘beneficial’ is. Each new technology-led innovation ushers in a Utopian ideal in which the human beneficiaries are enabled to achieve idealised goals – or at least that is what the technologists behind the innovation would have them believe. What we see in reality, whilst not necessarily dystopian, is nonetheless very far from this idealised world. There is, and always will be, a huge difference between the way the humans behave and the way that machines behave. No matter how complex the machine, and how closely it appears to mimic human thought, a machine will never be human, it will always be a machine.

The dystopic view of our developing relationship with machines comes not from machines developing some kind of emergent consciousness and taking over the world, but from the behaviour of the people who exploit them or rely on them. Machines are a product of their design and programming – they have limitations. People, on the other hand, are driven by their very nature to explore outside the boundaries of experience. They don’t ask ‘what does this machine do’, they ask ‘what can I do with this machine’.

Vegard Engen introduced the concept of ‘intentionality’ as a distinction between the ‘agency’ exhibited by machines and the ‘agency’ exhibited by the humans in a network. Humans will intentionally set out to get the machine to do what they want it to do, whereas the machine will only do those things that are within its design parameters.

In the descriptive model presented by Brian Pickering ‘Human Behaviour’ takes centre stage, usurping the earlier focus of such models on the technical capability within networks. This is an important shift of emphasis taking place in the study and understanding of human machine networks, including as it does the social science and humanities component as an intrinsic part of network functionality.

In her review of the roadmaps being developed by the HUMANE project, Eva Jaho talked about policy and regulation as well as technological development – reflecting the need to manage the behaviour and activity of the people in a network whilst recognising that evolving technology allows for emergent beneficial behaviour that could be supressed by over-enthusiastic regulators. We should remember that machines operate on the principle of prescription – they do what they are designed to do – whilst people operate on the principle of proscription – they will do anything they can get away with unless they are prevented from doing it.

Dave DeRoure reminded us that people are subversive – they will be inventive to get the machines to do what they want to do, not what the designers expected the machines to do. The best networks are the ones that celebrate and encourage the inventive ability of humans – Grant Miller provided the example of Zooniverse and its ability to satisfy the higher human ideals of curiosity, satisfaction and achievement whilst eschewing any financial reward.

So, I will return to my original question of what human machine networks, or social machines, actually do. Gina Neff talked about symbiotic agency, reflecting the developing understanding of networks coming out of HUMANE.

Humans and machines work together to achieve a human-defined goal. Different humans within the network may have different goals, leading to conflicts and battles such as those described by Taha Yasseri in his studies of Wikipedia, but this is a result of human nature, not machine intervention. Human machine networks and social machines allow people to do what people do best – communicate, explore, discover, invent, manipulate, subvert and revolutionise.

People have a symbiotic relationship with the machines they invent – but they always have done. Where machines come to dominate or control lives it is only because we have allowed them to do so. We lay ourselves open to Perrow’s ‘Normal Accidents’ but, as Perrow describes, they do not arise because of the technology but because of human reliance and organisational failure. Our understanding and appreciation of the value and benefits of human machine networks must be based on their social context and on the resultant behaviour of the people forming part of the network, we can no longer study networks as purely technological artefacts.

There is no other ghost in the machine than the people who live within it, who seek to achieve their goals and ambitions, their wants and needs in symbiosis with machine capabilities. And this is what human machine networks do – they give us the power to be more human and to do better what we, as humans, have always strived to achieve.

From Actors to Interactions

Image provided by PresenterMedia.com

Over the course of the HUMANE project, we have developed a typology based on individual dimensions across four analytical layers including networks, behaviours, actors and interactions. Further, we have identified a number of key challenges for human-machine networks (HMNs) – including motivation, collaboration, innovation and issues relating to trust – and explored design options to support them [1], as well as developing roadmaps across different domains [2,3]. But as we move towards the conclusion of the project, we need to take stock of what the dimensions of the typology, or some of them at least, really tells us about HMNs and what they might mean for the future of such networks.

In the recent workshop, some of our research outcomes were helpfully bracketed by our keynote speakers, who highlighted two major themes for HMNs. In the morning, David De Roure reminded us that collaboration between humans and technology has a long and distinguished history remembering that well before the launch of Wikipedia at the beginning of the noughties, the co-creation of knowledge and content goes back a very long way revealed in part by prosopographic investigation of personal narratives. Developing a contemporary metaphor, the concept of SOCIAM GO! underlines the fact that human actors in the network will adapt as they move offline activities online to exploit the greater reach and efficiencies enabled by increasing machine capabilities in the virtual world. Those engaging with technologies over the ages have therefore developed strategies together or independently to achieve their own goals. More recently, emergent behaviours have begun to signal that there is more to come.

Exploring the logical possibilities of Moore’s law as well as increasing machine agency [4] and the power of automation [5], Gina Neff set out a number of thought provoking propositions. The interplay of human and machine agency [6] may be usefully summarised as symbiotic agency: ignoring what might go wrong and the as-yet unresolved regulation of bots in political life [7], human-machine interaction is now about collaboratively exploring possibilities constrained only by our imagination. One consequence of this, though, is that instead of looking at the legal demands of privacy regulation  with its misdirected focus on data subject empowerment [8], we need to appreciate that it’s not so much personal data which may need protection but rather the derived data, the notional offspring from a human-machine coupling (see also [9,10]). Agency is therefore coming of age and is no longer concerned solely with the fine-grained distinction between human and machine actors of intentionality.

Elsewhere, we have begun to explore the potential afforded by increasing machine agency [5] as well as the relationship between agency on the one hand and regulation as well as self-efficacy on the other [6]. But other dimensions of the HUMANE typology now deserve additional attention. Interactions between human actors (Social Tie Strength), as well as Human-to-Machine Interaction, may well provide the key to taking our understanding of the dynamics of HMNs to the next level. Social psychology has already provided some insight into the migration of human relationships to the virtual world [11,12], the potential for robot exploitation in healthcare, interventions for developmental disorders, and trust as an organising principle [13] leading to trust transfer from human interactions to ecommerce [14]. However, if the co-creation of personal data is really the result of the intimate union of human and technology [15], then this will have both societal as well as economic implications. Value and rights management are not only about the service provider controlling access to their services against the reuse of such data perhaps for customised marketing purposes. Instead, with advanced machine learning techniques unleashing unexpected complexities via data analytics, the advent of blockchain [16] provides a basis for innovative economic models to ensure that both human participant and technology providers can cooperate on an equal footing and most importantly assume joint and equal responsibilities for the accuracy and curation of those data.

Today’s HMNs already exploit workflow interdependence and network organisation in ensuring increasing geographic reach supporting ever greater network size. The HUMANE profile identifies such networks, while related work shows both the cultural diversity of common network interaction [17] and the dissolution of previous spatio-temporal barriers to network efficiency [18]. Tomorrow’s HMNs will need to understand the agency dimensions and how they affect each other to facilitate network complexity and sophistication [4], [5]. Agency opens up the possibilities for emergent network-level behaviours. Future HMNs, though, will also need to explore and respond to the interaction dimensions of the network to ensure the selection of appropriate economic models and the fair use of network outcomes.

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