Ethical and legal challenges of AI in agriculture
The use of artificial intelligence (AI) in autonomous harvesting machines offers opportunities for more efficient and sustainable harvesting processes, but also raises ethical, legal, and social questions. Oxbo is investigating how AI can be applied responsibly, focusing on transparency, data ownership, liability, and the impact on employment in agriculture.
Innovation package, use case, and type of trial
Open Teelten
Handsfree autonoom oogsten vollegrondgroente
Status: evaluation report
Stakeholder impact
Broad research question
What responsibilities does AI in harvesting machines entail?
AI increasingly makes decisions in automated agricultural processes. This study focuses on how AI can be deployed transparently, who is responsible for the decisions made by the system, and the impact of automation on the sector.
Approach
Interviews and impact analysis
To gain insight into the impact of AI on harvest quality, autonomy, and regulation, interviews were conducted with experts from Oxbo and Wageningen University & Research (WUR). The following topics were evaluated:
How AI makes decisions in the harvesting process.
The role of users in overseeing AI outcomes.
Privacy, data ownership, and liability.
The economic and social consequences of automation.
This analysis is part of a broader ELSA scan, which maps ethical, legal, and social aspects.
Goal
Responsible AI integration in agriculture
The goal of this analysis is to identify potential bottlenecks and risks and to provide recommendations for responsible AI use in autonomous harvesting machines. This report serves as the foundation for further discussions and adjustments in the development of this technology.
Results and reflection
AI offers opportunities but requires clear guidelines
The ELSA scan confirms that AI can contribute to more efficient harvesting processes, but there are still important legal and ethical considerations.
Successes:
AI supports more efficient harvesting and can contribute to more sustainable agriculture.
The analysis provided insights into how AI can be deployed more transparently and with better controllability.
Lessons learned:
Transparency about AI decisions and data ownership is essential for user acceptance.
Collaboration with legal experts and industry partners is needed to develop guidelines on responsibility, data ownership, and social impact.