Towards Informing Human-centric ICT Standardization for Data-driven Societies

Information and communication technology (ICT) standards play a crucial role towards harnessing technological developments and shaping the technology landscape. ICT standards development is largely driven by standards developing organizations or standards setting organizations that represent and are informed by the perspectives and interests of the ICT private sector and the public sector. Thus the shaping of the standards is mostly driven by the business, technical, and legal impetus, towards facilitating greater market influence, coordination, integration, interoperability, and legal conformity. The technocentric, business-focused perspective to ICT developments, and corollary to ICT standards development, is distinct from and can be orthogonal to the human-centric perspective that elevates the role and centrality of the human concerns over the technology and business concerns. Notwithstanding the crucial role of business and technology, it remains that at the centre of the 21st century data-driven societies are individuals and end-users who are the primary actants and agents within the technology and data ecosystems. This gives motivation for infusing the human perspectives and values into technology development as well as into ICT standards development. This is more pronounced for cases where the business and technocentric interests are at odds and opposed to the human interests, such as, the need for increased datafication to support Big Data developments versus the need for individuals’privacy preservation. In this research, which is framed through a case study of personal health informatics in the context of sustainable development (i.e., sustainable development goal on “health and wellbeing” – SDG3) indicators monitoring, we have undertaken a survey to investigate the human-centric values and attitudes associated with the collection and use of personal data. From this inquiry, the paper highlights and surfaces: individuals attitudes and perceptions around monitoring of social indicators; key considerations associated with data ownership, privacy and confidentiality of data, as well as sharing of personal data within the data ecosystem. The paper then discusses how these findings could inform and be infused into the development of technology artefacts and standards, towards a realization of more human-centric data-informed societies.

[1]  Mamello Thinyane,et al.  Small data and sustainable development — Individuals at the center of data-driven societies , 2017, 2017 ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K).

[2]  Rowanne Fleck,et al.  Shared PI: Sharing Personal Data to Support Reflection and Behaviour Change , 2015 .

[3]  Nuno Teles Motivations and Incentives : from the “ crowding-out effect ” to “ peer-production ” . , 2007 .

[4]  Harri Oinas-Kukkonen,et al.  Social interaction and reflection for behaviour change , 2014, Personal and Ubiquitous Computing.

[5]  J. Dewey Experience and Education , 1938 .

[6]  Un Desa Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .

[7]  K. Schwab The Fourth Industrial Revolution , 2013 .

[8]  Silvana Trimi,et al.  Big-data applications in the government sector , 2014, Commun. ACM.

[9]  T. Tirpak Small Data: The Tiny Clues That Uncover Huge Trends , 2017 .

[10]  Matthew Chalmers,et al.  Personal tracking as lived informatics , 2014, CHI.

[11]  Cihan Cobanoglu,et al.  The Effect of Incentives in Web Surveys: Application and Ethical Considerations , 2003 .

[12]  Rae Woong Park,et al.  Big data management , 2015 .

[13]  Deborah Estrin,et al.  Small data, where n = me , 2014, Commun. ACM.

[14]  Andres Agostini THE FOURTH REVOLUTION: HOW THE INFOSPHERE IS RESHAPING HUMAN REALITY! , 2015 .

[15]  Alan F. Smeaton,et al.  LifeLogging: Personal Big Data , 2014, Found. Trends Inf. Retr..

[16]  David M. Kreps Intrinsic Motivation and Extrinsic Incentives , 1997 .

[17]  Hamed Haddadi,et al.  Human-Data Interaction: The Human Face of the Data-Driven Society , 2014, ArXiv.

[18]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[19]  Jens-Erik Mai,et al.  Big data privacy: The datafication of personal information , 2016, Inf. Soc..

[20]  Peter Parycek,et al.  Big data in the policy cycle: Policy decision making in the digital era , 2016, J. Organ. Comput. Electron. Commer..

[21]  Solava Ibrahim,et al.  From Individual to Collective Capabilities: The Capability Approach as a Conceptual Framework for Self‐help , 2006 .

[22]  Mamello Thinyane,et al.  Investigating an Architectural Framework for Small Data Platforms , 2017 .

[23]  J. Clapp,et al.  Development as freedom , 1999 .

[24]  Arnold Picot,et al.  Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda , 2015, J. Strateg. Inf. Syst..

[25]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[26]  Jodi Forlizzi,et al.  A stage-based model of personal informatics systems , 2010, CHI.

[27]  J. Borges,et al.  A TAXONOMY OF PRIVACY , 2006 .

[28]  Johan Redström,et al.  Slow Technology – Designing for Reflection , 2001, Personal and Ubiquitous Computing.