# Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design
## 3 Current Challenges of AI in the Autonomy of Farmers from an STES Perspective
### Social Aspects  
The deployment of AI technologies is set within a web of social entities that are driven by economic incentives to a considerable degree. Technology providers, farmers, third-party entities, and the farming community are interested in maximizing their individual objectives, thereby providing a variety of requirements for the technologies that sometimes compete against each other (e.g., economic scalability vs. crop diversity). As the providers drive the design of the technology, the risk for the users and broader farming communities is more prominent. Vendors tend to centralize data storage to continuously develop their algorithms (vendor lock-in) over data management platforms [28]. The economic gains of those data are on the side of the technology provider, while the farmers are promised better products, which in turn are sold at higher prices [28]. This threatens the long-term economic stability of farmers. The implications of rights management on trust in the context of data storage were also highlighted as a prominent factor in the survey conducted by [PERSON] et al. [33].  
Another factor is the distribution and access to knowledge of the systems [34]. Centralizing specialized knowledge at the vendor site enables a continuous business model based on maintaining and upgrading the deployed systems. Conversely, this increases farmers' dependency on AI businesses as the sole knowledge providers. Limited access to knowledge for farmers results in low maintainability and adaptation of novel technologies and ultimately in reduced capabilities to judge the AI decision process. This in turn either lowers trust or results in trusting the decision output too much, with both limiting eco  
Figure 1: STES approach for analyzing the impact of AI on the autonomy of farmers based on the framework introduced by [PERSON] et al. [29].  
nomic exploitation of the technology. Furthermore, working with AI-based technologies creates the need for new skills in the farm workforce (for programming digital systems and software, data analytics, or machine and hardware maintenance) [28]. At the same time, switching to automated technologies based on complex decision processes allows a shift to a more steady workforce. As repetitive and simple tasks may be annihilated, fewer migrant and part-time workers are needed, as they are potentially replaced with fewer high-skilled, full-time work personnel [35; 36]. This creates novel requirements for technology design in terms of the user experience, as the deployed models must be designed for the specific user group or should at least be adaptable to different groups (e.g., language and visualization). Depending on the geographical area, different work requirements will lead to a non-homogeneous target audience. If the design of the AI is not adaptable to different skill levels of users, then it systematically widens the gap between demographic groups. Furthermore, various geographical and demographic differences must be accounted for to avoid social disparities, such as long-standing racial inequities [28], gender differences [37; 38], and also disparities in farm size [33]. The respective models and tools are often designed for large-scale producers [26; 39] as those are the farms that adopt digital technologies at higher rates [40] and may further widen an already existing digital gap between farming populations [41].