considered promising for timely monitoring of market prices and to support food security assessment.

Generally, the data crowdsourcing approach leverages the so-called “citizen science” principles to engage volunteers (paid or unpaid) to submit data at intervals (defined or undefined) which can be aggregated within or across locations and time. The approach is premised on the notion that the diversity of the crowd volunteers will foster the collection of rich data with minimal or no bias because the data submission is typically decentralized and independent, representing a robust quasi-sampling across the population at scale (Arbia, et al., 2023; Solano-Hermosilla, et al., 2022). Beyond the core value proposition that crowdsourcing is a cost-effective and timely approach to collect price data, it also offers ancillary benefits. For instance, it fosters an inclusive digital and data ecosystem, where citizens are not merely data consumers or data subjects, but they are also empowered as data curators and duly recognized as active stakeholders in data-driven decision making for the food system. This approach was fully developed and tested under an initiative called Food Price Crowdsourcing in Africa (FPCA), which resulted in the submission of food price data by >1,000 volunteer citizens, over a 3-year period (2019 -2021) across a region in Nigeria where food insecurity is prevalent (Thomas, 2023; World Bank, 2023). The outputs provided one of the first near-real-time datasets that revealed the actual impact of the COVID-19 pandemic on food prices, and indirectly, food affordability at the sub-national level (Adewopo, et al., 2021; Adewopo, et al., 2022).

In parallel to data crowdsourcing, the recent leap in the application of AI has revealed new possibilities for food system intelligence, including back-casting, now-casting, and forecasting. By leveraging predictive algorithms, AI systems are now performing complex data operations to spurn new datasets from sparse data points (Nguyen, et al., 2023; Savage, 2023; Lu, et al., 2021; Gavin & Mandal, 2002; Aldoseri, et al., 2023). While AI applications for complex analysis are not new, the embedding of modern analytical models and real-time coupling with multi-channel data or information pipelines has unlocked advanced capabilities of AI algorithms. One of such revolutionary capability is the monthly imputation of price data, which was developed for 25 countries (Andrée, 2021a; Andrée & Pape, 2023) and is now deployed by the World Bank to cover over 2,100 markets across 36 countries and monitor food prices, energy prices, and unofficial parallel market exchange rates.<sup>1</sup> The RealTime-Prices (RTP) dataset is created through a resource-intensive Markov Chain Monte Carlo framework involving hundreds of thousands of models to address missing data points in underlying surveys gathered from country systems, the World Food Programme and the Food and Agriculture Organization. To ensure the rapid processing required for maintaining up-to-date outputs as new data arrives, the algorithm employs a swift tree-based approach known as Cubist. This method integrates local models that capture relationships within specific regions of the feature space, similar to the concept of local receptive fields in convolutional neural networks (CNNs).

> 1 Andrée, B. P. J. (2021). Monthly food price estimates by product and market (Version 2024-01-25). WLD_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. <u>https://microdata.worldbank.org/index.php/catalog/4483</u> Andrée, B. P. J. (2024). Monthly energy price estimates by product and market (Version 2024-01-25). WLD_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. <u>https://microdata.worldbank.org/index.php/catalog/6134</u>

Andrée, B. P. J. (2024). Monthly food price estimates by product and market (Version 2024-01-25). WLD_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. <u>https://microdata.worldbank.org/index.php/catalog/6160</u> 3