Noah Shumba

Isabel Agadagba, Triphonia Kilasara, Takudzwa Tarutira et al.

Digital payment systems have become a cornerstone of consumer finance in Africa. Prominent payment categories include money transfer applications, mobile money, cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs). While there are studies exploring how and why people use individual digital payment systems (both in Africa and beyond), we lack a good understanding of why people choose between different categories of payment systems, and how they view the tradeoffs between different categories. We conducted qualitative interviews in three African countries -- Nigeria, Tanzania, and Zimbabwe -- to understand how and why people use various payment systems, and what influenced them to start using these systems. Our study highlights several notable findings regarding tradeoffs between perceived utility, privacy, and security. For example, many users trust government issuers to protect them from scams, but they do not trust those same institutions to build reliable systems and products or prioritize customer satisfaction. We also find that most users have accounts on multiple payment systems, and conduct a complex selection process using different platforms for different types of payments. This selection process is driven in part by financial considerations, but also by security, privacy, and trust preferences. Our findings suggest compelling directions for regulators and the research community to design systems that balance users' trust and utility needs.

Noah Shumba, Opelo Tshekiso, Pengfei Li et al.

AI computing and data centers consume a large amount of freshwater, both directly for cooling and indirectly for electricity generation. While most attention has been paid to developed countries such as the U.S., this paper presents the first-of-its-kind dataset that combines nation-level weather and electricity generation data to estimate water usage efficiency for data centers in 41 African countries across five different climate regions. We also use our dataset to evaluate and estimate the water consumption of inference on two large language models (i.e., Llama-3-70B and GPT-4) in 11 selected African countries. Our findings show that writing a 10-page report using Llama-3-70B could consume about \textbf{0.7 liters} of water, while the water consumption by GPT-4 for the same task may go up to about 60 liters. For writing a medium-length email of 120-200 words, Llama-3-70B and GPT-4 could consume about \textbf{0.13 liters} and 3 liters of water, respectively. Interestingly, given the same AI model, 8 out of the 11 selected African countries consume less water than the global average, mainly because of lower water intensities for electricity generation. However, water consumption can be substantially higher in some African countries with a steppe climate than the U.S. and global averages, prompting more attention when deploying AI computing in these countries. Our dataset is publicly available on \href{https://huggingface.co/datasets/masterlion/WaterEfficientDatasetForAfricanCountries/tree/main}{Hugging Face}.