# 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
### Technical Aspects  
The data-driven modeling aspect of AI provides a variety of technological factors that create uncertainties in farmers' decision-making processes. Along the AI development pipeline, this already starts with creating biases during data generation or its processing. During collection, a bias in data can be introduced by sampling solely in specific regions or types of farms, therefore expressing the ecological environment and social practices of a limited area (sampling bias). Due to the limitation to certain regions, the data might exclude different demographic properties (exclusion bias) or focus too much on crops with a high economic value (selection bias) [42]. Such biases limit the ability of farmers to infer actions based on generalized suggestions by the AI [43] and may produce a disadvantage for areas or crops of less economic interest (e.g., central African countries) [26]. The diversity of measurement tools and practices may also lead to bias during data generation (measurement bias) or limited accuracy of the models in a different data environment (interoperability issue). Depending on the instruments, data may be in a different format, aggregated at different levels, or produced in a different environment (e.g., soil depth, water content, and time of day).  
Further bias can be introduced in the data processing phase of the model [44] through the selection of the model or its parameters (e.g., number of cross-validations, size of the train-test split, regularization functions, or data selection) as well as by making wrong assumptions about the modeling subject [42]. Other algorithmic biases may be created by the optimization function used by the model, the user interface, and the feedback loop between the system and its users [45]. Such algorithmic biases can significantly impact the model's efficiency, trustworthiness, and deployability. This situation is aggravated if several models are incorporated into the system that exchange data between them or build on the decisions of each other [7]. This complex data flow creates an additional challenge for the already limited transparency and explainability of deployed models [32]. Based on this data complexity, the ability of algorithms to continuously learn by interacting with their technical, social, and environmental environment (e.g., reinforcement learning) further aggravates direct and hidden model dependencies. Such dependencies lower the transparency of the system, leading to higher costs and decreased maintainability of products based on AI [46]. These hidden dependencies potentially initiate a cascade of unwanted feedback loops, unknown emergent effects of the models, or large-scale fragility of the system. If those issues are not addressed in the design phase, then those design weaknesses may further increase the economic dependency of farmers on the vendors, the uncertainty in the decision-making process, and trust issues toward the technology.  
Another important aspect is the amount of data available. In most cases, data will be sparse and scarce, and then deep learning techniques cannot be used [47,48]. A similar problem happens in health applications, and thus we can borrow techniques from that field to handle this issue [49].