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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
- 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:
A different perspective on methodological research in analysing food sovereignty and changing food ecology of the tribal India. Rather than conventional tools of sampling or participatory ethnography, how can AI not just aid in data collection but it can enable reimagining the movement itself?
Paper long abstract:
This paper is interested in exploring the social impact of AI in indigenous food systems in India. Though AI can predict trends and offer customised solutions to problems in a space struggling with shifting traditional hierarchies and evolving technology (such as Indigenous Kondh communities in India), can AI technologies exacerbate social inequalities, particularly in rural communities? The introduction of new stakeholders in this region who use remake and reimagine the spatial and geographical habitat of the Indigenous might disadvantage marginalised populations. However, it can also be a potential tool to build more resilient and sustainable communities. Hence, the paper discusses ways and policies in which ethical considerations can be implemented in the indigenous regions of India.
Exploring the ethical considerations involved in deploying AI in food production, such as data privacy concerns, surveillance, and the potential for bias in AI algorithms used for decision-making in food systems.
It can involve community consultations and the involvement of local stakeholders to ensure technology serves the common good. Through extensive data analysis and field research, our paper focuses on models or tools of AI which can resolve some questions about the food crisis. It discusses how disruptive usage can impact the indigenous food sovereignty movement. and the repercussions and tendencies of creating further hierarchies and how they can be used systematically and ethically to offer unique solutions; it can arise in indigenous regions as these regions are already dealing with the new models of economy and shifting local power dynamics in neo-liberal markets.
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:
The paper takes a multifaceted approach to addressing food insecurity, stressing local engagement and collaboration. It calls for AI solutions that are inclusive, culturally relevant, and supportive of community resilience.
Paper long abstract:
The discourse surrounding Artificial Intelligence (AI) 's role in transforming future food systems encompasses a broad spectrum of topics. These include the application of AI in food production, distribution, and consumption to mitigate food insecurity, as well as broader considerations regarding the potential disruption of existing local food systems by AI. There is a discernible consensus divide between those who advocate for detailed deliberation and those who emphasize the broader implications of AI on future food systems. While some stakeholders underscore the necessity of thorough discussions and meticulous planning, others are more concerned with the potential consequences and disruptions AI might introduce to existing food systems. This dichotomy underscores the need for a balanced perspective integrating detailed analysis with a comprehensive understanding of AI's impact on food systems.
This paper adopts a multifaceted approach to address the complex issue of food insecurity, emphasizing the importance of local engagement and collaboration, highlighting the necessity for AI solutions that are inclusive, culturally relevant, and supportive of community resilience. It underlines the importance of synthesizing existing evidence to understand how to foster collective environments that cultivate trust-building behaviors and attitudes. Additionally, the paper proposes a forward-looking research agenda to develop these collective settings further and enhance our understanding of the underlying dynamics. Investing in these areas can create more resilient and trustworthy food systems that effectively address food insecurity.
Paper short abstract:
Development planning is key to food security, yet policies often fail due to inadequate data and uncertainty. AI can enhance resilience by improving crisis response. This paper examines AI’s role in adaptive food systems, balancing its potential with limitations for equitable, sustainable outcomes.
Paper long abstract:
Development planning is crucial for ensuring food security and sustainable food systems, particularly in developing countries where governments lead policy formulation. However, food system policies often face challenges in achieving their intended outcomes due to factors such as inadequate data and a lack of consideration for uncertainty in planning. Global disruptions—including climate change, economic shocks, and conflicts—further strain food production and distribution, highlighting the limitations of rigid policy frameworks. This paper explores the potential of adaptive frameworks in food system policymaking, emphasising the importance of integrating uncertainty into planning. Emerging technologies, particularly Artificial Intelligence (AI) and citizen-generated data, provide new opportunities to enhance food system resilience. AI-driven analytics can improve agricultural forecasting, optimise supply chains, and support data-driven decision-making, helping policymakers anticipate risks and respond effectively to food security challenges. By incorporating adaptive systems that leverage real-time data and predictive modelling, food policies can become more responsive and better equipped to mitigate crises. This paper examines the extent to which AI can support food security, supply chain efficiency, and early warning systems for shortages, while also considering its limitations. It argues that embedding adaptive frameworks into food system policies can contribute to resilience, equity, and sustainability. As food systems face increasing uncertainty, integrating AI and data-driven approaches into policy planning offers a pathway to more agile governance structures that can help safeguard food security for vulnerable populations.
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.
Paper short abstract:
This study explores how GMO labeling impacts consumer behavior, pricing, and nutrition using retail data and machine learning. We identify key drivers of preferences, price sensitivities, and dietary shifts, offering insights for policymakers to refine strategies and promote sustainable food systems
Paper long abstract:
Genetically modified (GM) products offer immense potential to address global food security and sustainability by boosting agricultural productivity, reducing resource use, and mitigating environmental impacts. Research shows that GMOs can significantly increase crop yields, lower reliance on pesticides, and improve farmer incomes. With the introduction of mandatory GMO labeling in the U.S., understanding consumer responses to GM products has become critical for shaping effective policies and market strategies. This study investigates the economic and nutritional impacts of GMO labeling on consumer behavior, market trends, and dietary health. The retail and purchase datasets, such as Nielsen Retail Scanner Data and household purchase panel data, provide valuable insights into sales and pricing trends while capturing complex patterns of purchasing behavior. To analyze these datasets and better understand consumer behavior, we employ machine learning techniques, including predictive and generative models to identify key drivers of consumer preferences, estimate price sensitivities for GM and non-GM products, and simulate market responses to price fluctuations. Furthermore, we assess how shifts in purchasing behavior affect dietary quality by analyzing nutritional profiles and product substitutions using machine learning techniques. The findings offer actionable insights into the influence of GMO labeling on consumer decision-making. These insights help policymakers and the food industry optimize pricing, refine marketing strategies, and promote informed choices to support more sustainable food systems.
Paper short abstract:
This paper presents a Decision Support System that integrates multi-source data fusion and AI to forecast crop growth, yield, and profitability. Furthermore, the knowledge and experience gained during the development and implementation of this tool in collaboration with producers will be shared.
Paper long abstract:
This paper presents a Data-Driven Decision Support System that integrates remote sensing, multi-source data fusion, and Artificial Intelligence (AI) to forecast crop growth, yield, and profitability. Initiated in 2019, the project involved collecting data from 100-hectare fields using Unmanned Aerial Systems (UAS), weather stations, and satellite imagery. Through continuous collaboration with producers, we developed a decision support system capable of simulating future crop growth and development. These datasets were further utilized to inform crop management decisions and improve yield estimations. Our system can predict yield at least two months before harvest, enabling early profit calculations and informed marketing strategies. By incorporating "what-if" scenarios, the tool enhances risk mitigation and strengthens producers' trust in AI-generated insights. Over the past five years, we have actively engaged with local producers, refining the tool based on their feedback. This presentation will highlight the progress made in developing the decision support system and our efforts in implementing a User-Centered Design approach to improve adoption and build farmers' confidence in these AI-driven tools.