Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

Abstract

Global agriculture is poised to benefit from the rapid advance and diffusion of artificial intelligence (AI) technologies. AI in agriculture could improve crop management and agricultural productivity through plant phenotyping, rapid diagnosis of plant disease, efficient application of agrochemicals and assistance for growers with location-relevant agronomic advice. However, the ramifications of machine learning (ML) models, expert systems and autonomous machines for farms, farmers and food security are poorly understood and under-appreciated. Here, we consider systemic risk factors of AI in agriculture. Namely, we review risks relating to interoperability, reliability and relevance of agricultural data, unintended socio-ecological consequences resulting from ML models optimized for yields, and safety and security concerns associated with deployment of ML platforms at scale. As a response, we suggest risk-mitigation measures, including inviting rural anthropologists and applied ecologists into the technology design process, applying frameworks for responsible and human-centred innovation, setting data cooperatives for improved data transparency and ownership rights, and initial deployment of agricultural AI in digital sandboxes.

Leverage zones in Responsible AI: towards a systems thinking conceptualization

“…For example, how to effectively apply systems thinking methodologies to the problem of Responsible AI without the need for intensive formal training. Although there are growing studies on adopting frameworks such as Responsible Innovation in which inclusivity, reflexivity, responsiveness and anticipation are considered (e.g., Tzachor et al, 2022 ), the research that explicitly focuses on a systems thinking understanding of Responsible AI are scarce and scattered. The literature also lacks a conceptual framework, or theoretical foundation, that allows to conceptualize, identify and evaluate the ‘effectiveness’ of interventions for Responsible AI in a structured way.…”

Achieving the nutrient reduction objective of the Farm to Fork Strategy. An assessment of CAP subsidies for precision fertilization and sustainable agricultural practices in Germany

“…For example, algorithms can be guided by different objectives such as reducing fertilizer input, minimizing environmental impacts or improving fertilization profitability. The latter objective demands maximum yields in relation to financial input which is likely to induce negative environmental impacts (see e.g., Saikai et al, 2020;Tzachor et al, 2022). Therefore, to contribute to the nutrient objective of the Farm to Fork Strategy, digital precision fertilization must clearly aim at fertilizer reduction and nutrient loss minimization.…”

Transforming agrifood production systems and supply chains with digital twins

“…Third, modelling flaws may be introduced in design, through human error in coding or merging error-free but discordant algorithms or data. A small notational error in the code of a computational model used for predictive maintenance of an irrigation system, for instance, could result in ill-informed decisions leading to crop yield failures and produce loss 38 .…”