AI innovations and ELSA Challenges
Autonomous weeding robots use AI and cameras to recognise crops and weeds and remove weeds automatically. This can greatly reduce the need for chemical pesticides, which is better for the environment and biodiversity. At the same time, robots like these raise important questions. Who is responsible if something goes wrong? Can farmers understand and trust the decisions made by the AI? ELSA research helps address these issues by promoting clear responsibility rules, transparent AI systems and farmer involvement in the design and use of the technology. This supports both sustainable farming and responsible innovation.
AI is also used to analyse consumer buying behaviour, for example to reduce food waste or promote more sustainable food choices. These systems can help make food chains more efficient and environmentally friendly. However, the use of consumer data also raises concerns about privacy, fairness and transparency. People may not always know how their data is used or how AI influences their choices. ELSA research promoted the protection of privacy, ensuring clear communication and giving users more control over their data. This creates opportunities to use AI for sustainability while respecting consumer rights.
Read more about the AI innovations involved in WUR's ELSA research under Innovations.
ELSA Lab in Wageningen
The ELSA lab at Wageningen University & Research develops, tests, and applies a methodology for (re)designing AI making it responsible and trustworthy. We work closely together with various commercial and non-commercial stakeholders and societal representatives. The ELSA research provides insights into which ELSA Aspects are most applicable in agri-food and areas for improvement (see Publications). For example, there is often uncertainty about who owns data, algorithms, and technologies: the user or the developer. Clear data governance agreements are needed to define who has decision-making authority and who provides consent for data use. Although companies may legally own or use data for model training, there is a strong emphasis on transparency, informed consent, and secure data handling. At the same time, the value of data and the rights associated with it are continuously evolving, with future challenges expected due to increasing complexity in data management and growing awareness among stakeholders. Scaling to multiple locations or applications therefore requires robust, secure, and practical data management solutions.
ELSA research about aspects like these has become well known and aims to enhance responsible and trustworthy AI and digitalisation in agri-food through various national and EU projects (link).
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