Digital Earth (DE) can be defined as a multidimensional representation of the planet capturing natural, social, and cultural phenomena (Goodchild et al., 2012
; ISDE, 2012
). Underpinned by both structured and unstructured, open or proprietary, spatial–temporal data, DE makes use of digital technologies to visualize, simulate, and model real-world situations. The concept of DE, initially popularized by Gore (1998)
, has long been loaded with the techno-optimism that often surrounds new technologies. DE has even been envisaged as a platform to support national and international cooperation for global sustainable development, economic growth, and well-being (ISDE, 2012
). The optimistic vision of DE from 10 years ago, however, could not fully anticipate the impact of big data, machine learning, and artificial intelligence (AI) systems on the field.1
These are crucial for DE’s development due to the high volumes of data coming from multiple sources—from remote sensing to crowdsourcing projects—and the need to integrate data to build complex multiscale and multisector models.
In this commentary, we argue that DE offers a fruitful lens through which to address current debates on data governance and the ethical use of AI. In our view, the many important issues being discussed about the ethics of AI are fundamentally about both the data and the algorithms used by AI. We propose that not only can current debates about data governance and ethical AI inform development in the field of DE, but also the distinct characteristics of DE (and of the geospatial information from which it is built) can also inform data governance and ethical AI debates more generally.
There are many reasons why DE and the geospatial domain provide valuable insights for discussions on data governance and ethical AI.
First, while the collection of geospatial data by private sector operators is becoming ubiquitous (Poom et al., 2020
), geospatial data about an individual’s location and proximity to other features on a map can reveal privacy-sensitive information. Thus, the ethical handling of geographic data, referred to as GeoEthics within the GIScience community (Goodchild, 2021
), has an important role to play in general discussions on the ethics of data and AI.
Second, DE involves both large volumes of spatial–temporal data from multiple sources—across different ownership and governance regimes, sectors, scales, and cultures—and complex interactive models, AI methods, and techniques.2
Instances of DE, like interactive digital twins of the Earth and its inhabitants, are capturing the interest of policy makers for their potential to provide tailored models for policy, monitoring, and simulating possible futures (Nativi et al., 2021
Third, the vision and role of DE as a global platform for collaboration across governments and cultures, by being multilayered and embedded in different local contexts, offers a nuanced case study to examine some of the global-local tensions that are arising in debates about AI ethics and data governance.
Current policy and academic discussions on AI ethics and data governance could also offer valuable insights for the future of DE. Nations across the world such as China, the USA, India, Canada, and member states of the European Union have developed AI strategic plans, ethical guidelines, and recommendations for responsible AI (e.g. European Commission, 2018
; UNESCO, 2021
; Wu et al., 2020
). Yet, there is little guidance on how to practically implement these guidelines in DE applications and how to develop data governance structures that support these values. Furthermore, within the data governance field, scholars and policy makers are problematizing the unequal distribution of power and investigating how individuals can be empowered in their relation with their data (Micheli et al., 2020
; Sadowski et al., 2021
). Citizens have a key role in DE, both as final users of applications and as co-generators of data that feed DE (Annoni et al., 2011
; Goodchild, 2007
). Issues at stake with citizens’ involvement (or lack thereof) in DE relate well to such debates on data governance (Brovelli et al., 2020
; Haklay, 2016
Expanding on the ideas in this introduction, in the second section we discuss the implications of AI ethics for DE, in the third section those of data governance, and in the fourth section we conclude.