Over the past two decades, there has been increasing research on the use of artificial intelligence (AI) and geographic information technologies for monitoring and mapping varying phenomena on the Earth’s surface. At the same time, there has been growing attention given to the ethical challenges that these technologies present (both individually and collectively in fields such as critical cartography, ethics of AI and GeoAI). This attention has produced a growing number of critical commentaries and articles as well as guidelines (by academic, governmental, and private institutions) that have been drafted to raise these ethical challenges and suggest potential solutions. This paper presents a review of 16 ethical guidelines of AI and 8 guidelines of geographic information technologies, analysing how these guidelines define and employ a number of ethical values and principles (e.g., autonomy, bias, privacy, and consent). One of the key findings from this review is the asymmetrical mentioning of certain values and principles within the guidelines. The AI guidelines make very clear the potential of AI to negatively impact social and environmental justice, autonomy, fairness and dignity, while far less attention is given to these impacts in the geographic information guidelines. This points to a need for the geo-information guidelines to be more attentive to the role geographic information can play in disempowering individuals and groups.

The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.

Cartography has been, in its pre-modern and modern production of maps, influential in determining how space and territory is experienced and defined. But advancements in telecommunications and geovisualization software, along with geoinformation systems and geoinformation science (GIS), have transformed cartographic practice from a tool of dominantly state apparatus to a scientific, commercial, and humanitarian enterprise. This is exemplified in the use of remote sensing (RS) techniques to acquire, process, and visualize images of the Earth. In the last decade, RS techniques have increasingly incorporated Artificial Intelligence (e.g., Convolutional Neural Networks) to improve the speed and accuracy of feature extraction and classification in remotely sensed images. This paper will investigate the use of CNNs in the classification of deprived urban areas referred to as “slums” and “informal settlements” in the Global South. Using a postphenomenological methodology, this paper shall analyze the role of classification and use of geoinformation in shaping how deprived urban areas are algorithmically classified. This analysis will reveal that besides the technical opportunities and challenges, attention needs to be given to three ethical areas of concern: how deprived area mapping using AI impacts the agency of communities, how there is a potential lack in the democratization of these RS technologies, and how the privacy and data protection of communities being mapped is endangered.

In the wake of the COVID‐19 pandemic, a range of technological as well as legislative measures were introduced to monitor, track and prevent the spread of the COVID‐19 virus across the world. The measures taken by governments across the world have relied upon the use of geoinformation from satellites, drones, online dashboards and contact tracing apps to render the virus more visible, which has been instrumental in two ways. First, geoinformation has been helpful in organizing efforts for capacity building, in mapping communities living in deprived urban areas (referred to commonly as ‘slums’) and their response to COVID‐19 measures. These efforts have been part of initiatives by the United Nations as well as NGOs, using geoinformation to inform urban policymaking by representing the social, political and environmental issues facing those living in deprived urban areas. And secondly, geoinformation has also been used to control the spread of the pandemic by monitoring and limiting the behaviour of citizens through various technologies. This form of geoinformation‐driven governmentality, I will contend from critical geography and surveillance studies perspective endangers ethical values such as trust and solidarity, agency, transparency along with the rights and values of citizens.

Geographic information has become increasingly relied upon in supporting decision-making in public administration as well as humanitarian intervention. One domain where such information has been important is waste management, notably improving the mapping of areas with the highest waste accumulation, finding suitable locations for landfills as well as planning transport routes for collection services. The utility of geographic information here reflects the capacity of visual information to mediate between human actors (e.g. municipal authorities) and the world in which they wish to make changes . In this paper, we shall be presenting insights from five focus groups that were conducted to assess the impact of the visualisation of waste accumulation in the ‘slums’ and informal settlements in Nairobi, Kenya, along with factors that affected the readability and understanding of the visualisations presented in the focus groups. In this paper, we will be considering: i) the impact of design elements (e.g. level of detail and symbology) on raising problem awareness of the participants regarding the waste accumulation; and ii) the ethical and political concerns that arise from the way communities living in the DUAs are perceived from the visualisations.