Lessons learned from the risk-informed urban development initiative in the SADC region.

Unlabelled: This study presents the systemisation of lessons learned from the urban sector in which the measures based on the guiding principle of risk-informed development (RID) have been implemented in the Southern African Development Community (SADC) region at the national and/or sub-national levels. Despite notable risks in the region, these are not adequately considered in urban development planning and programming. Aiming at strengthening RID in the SADC region, the objectives of this peer-to-peer exchange were achieved through virtual workshops, roundtables and briefings on a cloud-based and open-source BigBlueButton Web conferencing system.

The Ubuntu Way: Ensuring Ethical AI Integration in Health Research.

The integration of artificial intelligence (AI) in health research has grown rapidly, particularly in African nations, which have also been developing data protection laws and AI strategies. However, the ethical frameworks governing AI use in health research are often based on Western philosophies, focusing on individualism, and may not fully address the unique challenges and cultural contexts of African communities. This paper advocates for the incorporation of African philosophies, specifically into AI health research ethics frameworks to better align with African values and contexts.

Carbon Accounting in the Digital Industry: The Need to Move towards Decision Making in Uncertainty.

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the following research question: how do practitioners researching, working, or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that these calls for more data obscure the fact that there will always be uncertainty, and that we must learn to govern from within it.

Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.

Computationally expensive data processing in neuroimaging research places demands on energy consumption-and the resulting carbon emissions contribute to the climate crisis. We measured the carbon footprint of the functional magnetic resonance imaging (fMRI) preprocessing tool fMRIPrep, testing the effect of varying parameters on estimated carbon emissions and preprocessing performance. Performance was quantified using (a) statistical individual-level task activation in regions of interest and (b) mean smoothness of preprocessed data.