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- Convenors:
-
Robert Strong
(Texas AM University)
Rafael Landaverde (Texas AM University)
Laura Palczynski (Harper Adams University)
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- Format:
- Paper panel
- Stream:
- Agriculture, rural livelihoods, food systems, and climate change
- Location:
- CB3.1, Chancellor's Building
- Sessions:
- Thursday 26 June, -, -
Time zone: Europe/London
Short Abstract
Our panel seeks investigations of the social implications of AI as a form of technology assessment that anticipates the unforeseen and unintended consequences and opportunities of technological innovation, including cultural, health, welfare, educational, ethical, food, and environmental impacts.
Description
We seek papers that advance understanding of Artificial Intelligence (AI) applications and transformative system approaches to enhance food system sustainability. By transformative systems we mean those that offer major and synergistic advances toward the multiple goals of sustainability; productivity, profitability, environmental, animal welfare, and social dimensions. Investigations of the social implications of AI as a form of technology assessment that anticipates the unforeseen and unintended consequences of technological innovation, including cultural, health, welfare, equity, ethical, and environmental impacts are welcome. Predictive AI includes machine learning, deep learning, and neural networks used to forecast trends to inform actors of solutions needed based on the data analyses. Generative AI models generate audio, text, video, and images from existing data to create new content. A critical lesson from past experiences with the application of scientific discoveries and technological innovations to food systems production is that public trust in science begins with and requires ongoing transparency and open deliberation. Submissions can involve a range of stakeholders to assess the AI’s merits and risks and/or examine issues and modes of communication that can result in open and participatory approaches to effectively involve the public and engage with communities potentially impacted by the technology in deliberations over these issues. Papers should assess the social, ethical, educational, cultural, legal, or other potential impacts that a broad range of emerging and disruptive AI technologies, including breakthrough scientific discoveries, may pose for society, food markets, communities and rural prosperity, food production, consumer preferences, and other contexts to model opportunities.
Accepted papers
Session 1 Thursday 26 June, 2025, -Paper short abstract
The collaborative trinity between AI industries, institutions and government agencies is a food systems frontier. We will highlight evidence-based strategic opportunities for industry leaders, physical and social scientists, and government administrators to elevate AI knowledge transfer and impact.
Paper long abstract
Stargate is a new project that intends to invest $500 billion over the next four years in building new AI infrastructure in the United States. AI allows computers and machines to carry out tasks without human cognition. Machine learning (ML), an AI tool, is a data analysis method that imitates human learning. The emergence of information technology has developed big data that can be analyzed utilizing AI to provide valuable decision support to producers. Knowledge transfer can be improved through the implementation and impact offered by the Agricultural Knowledge and Innovation System (AKIS), a multidimensional approach with food system actors. AI can provide predictive analytics for growers to better manage risks through improved preparation and response for unexpected events such as severe flooding and drought. Robotics and visual machine learning platforms enable the automation of critical labor activities such as field scouting and harvesting. ML’s advanced visual algorithms will enable stakeholders from fruits and vegetables to improve harvest efficiency using platforms such as flying robot pickers. Artificial intelligence is expected to greatly assist in reducing agriculture's environmental footprint and contributions to climate change while continuing to increase productivity and input efficiency. Our project will develop a suite of AI-enabled technologies using sensors, actuators, and robotic platforms to increase the precision and efficiency of farm and ranch management practices, reducing overall input use and GHG emissions. Collaborative synergy between academia and industries, like Stargate, will be essential for maximizing AI knowledge transfer and optimizing continued food security and land sustainability goals.
Paper short abstract
Adopting AI technologies is complex and requires careful consideration of positive and negative consequences. To better understand AI adoption, we present a framework that examines multiple nested factors, from the individual level to the enabling environment and technology characteristics.
Paper long abstract
Ataharul Chowdhury & Uduak Edet
The adoption of artificial intelligence (AI) in Ontario’s agri-food sector is inevitable and will enhance competitiveness, create market opportunities, and strengthen the industry. However, without responsible innovation that considers societal concerns, the sector may encounter challenges such as apathy toward adoption, job displacement, inequality, and ethical dilemmas. The extent of AI adoption varies across different sectors and value chains, requiring distinct skill sets for workers depending on the specific AI technologies utilized. This literature review examines various AI technologies and frameworks sourced from scholarly databases, reports, and government publications, focusing on their applications in the Canadian horticulture and livestock sectors. As part of an exploratory phase in a broader research project, this review establishes criteria for assessing the factors that influence the adoption of emerging AI technologies. It employs an analytical framework to evaluate AI tools based on their descriptive, diagnostic, predictive, and prescriptive features. Key AI technologies highlighted in this review include tools for crop and livestock disease detection, as well as technologies aimed at improving breeding and feeding efficiency. These technologies are classified into content layer categories, and potential barriers to adoption are identified, taking into account individual, social, environmental, and institutional factors. This review proposes a framework for analyzing the adoption of AI technologies, emphasizing the importance of responsible innovation, technological characteristics, and systemic change.
Paper short abstract
Farm data offers real-time insights into poultry health and welfare, but improvements rely on actions by farmers and farm staff. Using established tools and processes linking farm data to an AI engine, we explore the potential for language models to help farmers and workers act effectively on data.
Paper long abstract
Farm data offers real-time insights into poultry health and welfare, but improvements rely on actions by farmers and farm staff. Poor accessibility of data has been noted as a barrier to action. To overcome this data-action gap and effectively manage socio-cyber-physical systems, various costly and time-consuming interventions have been proposed, e.g. improving the advisory system and opportunities to upskill the workforce. A potentially more efficient solution could involve language models, or natural language programming. This would use generative AI trained on farm data, ultimately ending with a visual, text, or audio management instruction to the farmer/worker alerting them to an issue or suggesting corrective action.
This pilot research, working with a company that has established tools and processes for sending data it has captured to an AI language model, explores preferences for how data is presented. This work also explores the potential of language models to help farmers/workers act effectively on data – an area which has so far received little to no empirical on-farm evaluation. We also critically explore the social and ethical implications of using AI language models on farms.
Interviews with poultry farm staff explore how they make decisions from data and technology and the challenges they face, if any; and their preferences and views about the potential for language models to overcome the data-action gap, as well as how they might affect the nature of work and human-animal relationships. These insights will improve understanding of the presentation of data insights in ways aligned to individual preferences.
Paper short abstract
This paper aims to identify structural gender challenges that the deployment and use of Artificial Intelligence (AI) in agri-food raises. The divergence in the use and benefits of AI in agri-food raises many concerns about disadvantage, unfair distribution of resources and benefits, and inequality.
Paper long abstract
Artificial Intelligence (AI) and AI-powered robots offer real potential to help alleviate and reduce many of the dull, dirty, and dangerous jobs done by, particularly, women in the Global South. At the same time the agri-food domain and computer science have traditionally been male-dominated. Research on the ethical, legal, and social aspects (ELSA) of digital technologies in agri-food, has shown that digitalisation will most likely exacerbate existing divides, including the gender divide. The impact of AI on gender dimensions in agri-food, however, has hardly been researched.
This paper aims to identify structural gender challenges that the deployment and use of AI in agri-food raises. Initial literature review results show that the use of AI could harm diversity and inclusion in the sector in multiple ways. First, digitalisation of agri-food may cause further disenfranchisement and push women out of the industry. This is particularly noticeable in the Global South where the agri-food sector comprises up to 80% of women. Second, the use and deployment of AI on farms are further expected to mainly be deployed on wealthy, large, monocultural farms. This may result in an increased digital divide between farms in the Global North and farms in the Global South. Third, it could also harm the industry because it needs to attract more young farmers to replace an ageing, declining demographic of farmers. The divergence in the use and benefit of AI in the agri-food sector thus raises many concerns about disadvantage, unfair distribution of resources and benefits, and inequality.
Paper short abstract
Adaptive planning supported by AI can improve food system resilience by enhancing forecasting, land use, and decision-making. Using diverse data and models, this study identifies key success factors and calls for richer data sources to address gaps in uncertainty and public sentiment.
Paper long abstract
Development planning plays a key role in supporting food security and sustainable food systems, particularly in developing countries where governments lead policy efforts. However, such policies often struggle to meet their objectives due to limited data and insufficient consideration of uncertainty. External pressures—such as climate change, economic shocks, and conflict—add further strain, highlighting the rigidity of traditional planning methods. This study explores the potential of adaptive policy frameworks, supported by artificial intelligence, to enhance the resilience of food systems. AI-based tools can support more informed decision-making by improving agricultural forecasting, mitigating soil sealing, and optimising land use and land cover management. An adaptive system using real-time data and predictive modelling is proposed to help policymakers respond more effectively to emerging risks. A combination of analytical methods—text mining, regression analysis, Random Forest, and artificial neural networks—is used to develop the system. Data sources include the International Aid Transparency Initiative, Global Food Security Index, World Uncertainty Index, world news APIs, and National Development Plans. Results suggest that AI-supported approaches can help identify key project factors—such as budgets, timelines, and national priorities—associated with more successful outcomes. The AI-driven adaptive system also shows strong potential to support evidence-based development planning and decision-making. Nonetheless, challenges remain. Data gaps and the limited availability of structured information on public sentiment and uncertainty can affect model performance. Future work should explore alternative data sources—such as social media and additional development indices—test other algorithms to improve predictive accuracy, and conduct thematic deep dives to generate more targeted policy insights.
Paper short abstract
We conducted a landscape analysis of Artificial Intelligence in agriculture focusing on smallholder farming contexts in low- and middle-income countries. Findings suggest that academic research on AI effectiveness lags behind technological developments and practitioner studies.
Paper long abstract
Introduction:
Use of Artificial Intelligence in smallholder agriculture has exponentially grown, but the effectiveness and implications for productivity, incomes, or livelihoods is poorly understood. We assessed the technical effectiveness of AI innovations and the socio-economic, ethical, and governance dimensions critical for AI implementation and scaling.
Methods:
- Rapid Review: Follow Cochrane Collaboration and Campbell Collaboration recommended methods. Qualitative evidence was critically appraised using the NICE framework; quantitative studies underwent evaluation using PROBAST, Cochrane ROB2, and ROBINS-I tools.
- Narrative Review: Capture emerging trends and implementation realities from developer and implementer perspectives, sources like blogs and editorials reviewed.
- Regional Case Studies: Reflect diverse applications of AI in agriculture with concrete regional examples of AI implementation and impact on local communities.
- Deep Dives: Examine socio-political and governance aspects of AI implementation, focusing on ethics: e.g. digital divide, digital inclusion.
- Stakeholder Consultations: Validate findings from reviews and case studies with agricultural sector and AI experts and stakeholders. Generate practical insights regarding the real-world application of AI innovations and implementation challenges.
Results: The rapid review synthesized 66 peer-reviewed studies: 42 quantitative, 20 qualitative, and 2 employing mixed methods. The narrative review includes 26 qualitative studies. Findings suggest (1) a mismatch between how AI is studied academically versus real-life implementation; (2) limited evidence for the effectiveness of AI in smallholder agriculture; (3) lacking attention for agricultural contexts in existing national and regional policies and regulations; (4) risks for maintaining or exacerbating the social, ethical, and sustainability challenges previously documented for digital agriculture.