Use of AI in the UK Food System

In recent years, advancements in Artificial Intelligence (AI) – or the use of machines to undertake tasks that would otherwise require human intelligence to perform (Future Risks of Frontier AI, 2023) – have seen multiple sectors increasingly incorporating AI into their work. This includes the food sector, which is using technologies like deep learning, robotics, and natural language processing to sort and classify foods, predict yields, and control for food safety, among many other functions (Di Vaio et al., 2020). AI has the potential to have large impacts on both daily work within the food sector, and to be a potential solution to longer term and more existential challenges, like the rising global population or climate crisis (Di Vaio et al., 2020; Marvin et al., 2022; Mavani et al., 2022; Short et al., 2022; Strauss et al., 2023). However, the field of AI is a very broad and an uncertain one, which is both changing rapidly, and largely represented by private companies with a vested interest in preserving confidentiality (Future Risks of Frontier AI, 2023).

Given the increasing trends of AI use in the food sector both within the UK and globally, it is important to stay abreast of the ways in which AI is being utilised, where technology can be further optimised and what barriers and challenges may be encountered or may emerge as a result of using AI. There are already many concerns around current use of AI in the sector, including potential failures in transparency around the algorithms in use, in how they make decisions (also known as the AI 'black box'), and around data access and use infringing privacy or exposing trade secrets (Jouanjean, 2019). This lack of transparency adds to the significant levels of uncertainty which exist around the progress and capabilities of any kind of AI.

This research sets out to establish an initial understanding of the current use of AI in the UK food system, where research is concentrated, and where activity may be likely to occur in the near future. To do this, it combines a rapid evidence assessment of academic and grey literature into the use of AI, with a scientometric analysis of global academic publication and patent data. This more historical data is supplemented by a high level horizon scan of emergent topics of discussion in the food sector based on scraping of major websites, blogs, journals and news outlets.