SDGs, Ethics and the Geospatial Domain: Critical insights and ways forward for the geospatial curriculum

Given the disruptive impact that data-driven technologies can have on society, ethics has been used to identify what is deemed as collectively acceptable when it comes to the development, implementation, and use of these technologies.

This lecture provides an overview on data ethics in connection with geospatial data and geographic information systems (GISs). It does so by focusing on the Sustainable Development Goals (SDGs) outlined in the 2030 Agenda of the United Nations. This is a particularly fruitful angle because 1) the 2030 Agenda was a collective agreement among all UN member states; 2) geospatial data and GISs have been repeatedly identified as key to achieving the SDGs. To this end, the Integrated Geospatial Information Framework (IGIF) of the UN aims to provide guidance on the management of geoinformation and related tools for countries to achieve the SDGs.

So far, technical, economic, political and organizational factors have hindered the realization of the 2030 Agenda and the implementation of the IGIF. More fundamentally, the epistemological rationale at the basis of both documents is inconsistent: they rest upon a top-down, linear understanding of development coupled with a ‘good vs bad’ conception of data-driven technologies. These inconsistencies risk preventing the achievements of SDGs because they overlook the ecosystemic, collective-level, sociotechnical dimension of any geoinformation initiative.

The lecture will follow three steps. It will:

1)     critically rethink what we mean by ethics, data, and technology, with a specific focus on the geospatial domain and SDGs

2)     advance an alternative model to the IGIF: the Ethics Assessment List for Geoinformation Ecosystems – to enable a critical use of geospatial data and GISs for SDGs

3)     discuss a “data ethics” course developed at TU Delft which compels students to consider the value-laden entanglements connected with the implementation of data-driven technologies, as well as the emergence of both positive and negative consequences deriving from their use.