Within almost every discipline related to the digital economy, there are critical and emerging issues around humans and the data they generate either directly, or as a byproduct of their endeavours. Equally, the data economy has stimulated a range of initiatives responses within each of the three sectors (public, private and third), as well as a broad portfolio of research across relevant disciplines. However, while such important work is ongoing, such these efforts are often disparate and tend not to feed directly back into the science of data-driven systems itself. There is an urgent need to guide the realisation of system design principles that are productive, and yet fit with the ethics and values acceptable to wider society. Those who are expert in development of the systems, algorithms and analytics that raise such issues face challenging culture gaps: firstly, with regard to those who are expert in areas such as the arts and humanities, and secondly with regard to those who are inexpert in technology but who are increasingly impacted by it in their everyday lives. Core to these divisions are issues such as a lack of social understanding of the technical capabilities of data-driven systems, inconsistency of research and development effort across sectors and disciplines, and tensions between industrial, societal and academic drivers, and human needs. Such tensions are visible in several domains, though few as pointedly critical as health. One need only look at NHS’ efforts to protect individuals’ medical records, in contrast to contrasted against the corporate monetization of DNA samples, as individuals take advantage of advances in low-cost mobile self-monitoring and diagnosiseek low cost solutions to their health-managements. Here, state, corporate and individual-level drivers create inconsistent approaches to the management and value of data.

It is time to draw together, consolidate and formalise our efforts across disciplines. We must now seek to structure further endeavour, while considering how new and emerging systems are realised, received and responded to-not just within the bounds of the DE but cross-sector, i.e. within the range of organisations and communities that reflect and support daily human activity and concern. At a sectoral level, industry has often focused narrowly on either corporate monetisation of data from individuals, or individuals’ efficiency and short-term optimisation of personal metrics (e.g. the ‘quantified self’). Market pressures mean that technical advances are increasingly implemented before social and cultural effects can be determined. This means, however, that data-intensive systems to support long term social, cultural and creative benefits are rare. At the same time, academic research has often focused on questions of interest more to itself than to other sectors. Academic work with public and third sector organisations has been fragmented, with interactions often weighted in favour of shorter term innovation cycles rather than longer term social needs. Such challenges, divergences and tensions lead to duplications, contradictions, and unproductive effort. This is the problem space within which we operate.

This network is a holistic and inclusive network approach, sensitive to the socially situated nature of such systems. To achieve this we will (a) develop and sustain a collaborative, cross-sectoral community under the banner of Human Data Interaction, (b) develop a portfolio of system design projects addressing underexplored aspects of the DE (c) create cross-sectoral interdisciplinary synthesis of research under the HDI banner (d) conceptually develop and flesh-out the HDI framework, (e) create a suite of policy and public-facing case studies, papers, prototypes and educational materials, and (f) develop a set of core guidelines intended to inform the design of human-facing data-driven systems.

This network seeks to respond to the challenges presented by human interaction with data-driven systems. At a high level, the network aims to establish the foundations for a new science of data-driven systems through collaborative development of the Human Data Interaction framework.


Photo c. Chris Scott