# Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design
## 4 Mitigation through Technological Developments
### Transparent AI  
Transparent AI is defined in this context as a system that provides information about its decision-making process and constitutes a high-level concept that incorporates the approaches of interpretable AI or explainable AI [24, 66]. The first uses models that are inherently interpretable to humans [67], whereas the second approach provides explanations for its predictions to the user [68], primarily for models that are too complex to understand directly. In combination, the transparent AI approach not only focuses on explaining the model outputs but also provides information about the model pipeline. This creates novel potentials for human-in-the-loop design after model and product deployment [69].  
Trusting AI decision processes too much is a serious concern in most professions. Understanding the underlying criteria and attributes of an AI through interpretable or explained models can facilitate trust in one's own and the models' decisions and enhance collaboration by combining the farmers' knowledge with the data provided by the AI. A farmer may confirm his or her management hypothesis or the AI-based solution by analyzing the contributing factors directly in the model (interpretable AI) or through visualized explanations (explainable AI). Visualizations such as bar charts are often used for identifying the importance of attributes [70], heatmaps can show what parts of pictures have been used by the model [71], partial dependence plots enable an overview of how different values of attributes influence decision outcomes [72], or scatter plot diagrams can help users to see relationships or similarities between model outputs [73]. Interpretable models, on the other hand, can give direct access to the probability distribution of features (naive Bayes) and the importance of individual criteria (linear or logistic regression models and decision trees), provide a logic-based structure that allows following the decision process of the model (decision trees and covering algorithms) [43, 67, 74], or enable one to visualize the distribution of possible decision lists (Bayesian rule list) [75]. It is important to mention that interpretable models often come at the price of limited scalability to large data sets (i.e., many attributes) and complex decision processes (i.e., nonlinear relationships between attributes) and should therefore be handled with care [67, 74].  
Both explainable and interpretable models can help detect biases (see Figure 2), errors, or hidden dependencies in the decision process [24, 43, 68, 71], such as by identifying sensitive attributes and their importance to the decision outcome (e.g., gender, race, height, and postal code). By understanding the underlying decision process and the importance of individual attributes, farmers can increase their decision-making competence, farm understanding, and privacy assessment [24]. Developing a system that is also able to learn from farmers' feedback (e.g., reinforcement learning) could further help to strengthen collaboration by aligning the outputs and decision processes with unique farm manage ment practices [76] or local biophysical differences (e.g., calibrating the model for a priori unknown biophysical feedbacks). This provides customizable management of AI systems for individual farm use cases [21]. As users are often inconsistent in their feedback or may inflict errors and biases [45; 77], particular guidance and merge criteria must be developed to ensure correct model development (e.g., an error message if an input conflicts with the expected values or has a negative effect on the accuracy).