# Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design
## 4 Mitigation through Technological Developments
### FAIR Data Principles  
The findable, accessible, interoperable, and reusable (FAIR) data principles are a data management framework and open system design methodology with a particular emphasis on machine actionability in addition to supporting its reuse by individuals [25]. These principles not only apply to data in a more narrow sense but also include algorithms, tools, and workflows that have been part of the data generation and processing pipeline. In this context, Section 4.1 also integrates general data management design strategies to foster responsible use in AI environments. Machine actionability is a vital process to the data principle and is particularly important in automated data processing. As AI systems can combine and automatically process diverse data sources, this autonomous agent must have the capacity to carry out the following processes:  
1. Correctly identify the type of data;
2. Determine if it is worth using for the current task by analyzing the metadata or data elements;
3. Understand any licensing, legal, or accessibility constraints associated with the data;
4. Implements them correctly for the specified task [25]. By making the data **findable** through unique identifiers, diverse metadata, and common standards, the data provider ensures technical interoperability [56] for all geographically distributed AI service providers and their users but also enables clear visualization for transparent AI processes, as the context of the data and its limitations can be displayed (e.g., data generation method, experiment set-up, and data provider). This is also vital for analyzing bias in the data, as the enhanced capabilities for the machine to understand the context within which the data are generated may provide further information for identifying critical and sensitive areas in the data. Also, subsets of the data or changes in the data (dynamic data) should be uniquely identifiable and linked to the original data [57]. Changes to the data compared with its original source should be described in the metadata to enhance traceability, knowledge extraction, and the avoidance of potential biases. Providing **accessibility** to data via open and free protocols enables fair chances to find the necessary information for one's needs [58]. Accessibility does not necessarily imply open access to the data, as this could lead to other ethical concerns. However, it should clearly state under which modalities the data can be accessed. Also, the metadata should be openly visible even after the data are no longer available to enhance transparency and reproducibility [56]. In general, access to data should be as open as possible without introducing privacy concerns [59]. Data practices that enable privacy-aware access should be implemented in the AI processing pipeline to leverage the learning capabilities of the algorithms and better scalability and interoperability for the system. This includes practices such as anonymization of the data, synthetization, and federated machine learning algorithms. Anonymization describes the process of transforming the data in a way that sensitive information is hidden [60]. Synthetization of data is a range of methods to create an artificial data set that inherits the characteristics and structure of the original data [61]. Federated learning, on the other hand, is a machine learning approach that builds its model on decentralized and isolated data, thereby providing a security- and privacy-aware model training process [62; 63; 26]. All three methodological examples can be used to enhance data availability for a diverse set of stakeholders with limited open data access or without enough data to train a model for their needs. Furthermore, providing AI services incorporating federated learning may positively affect technology adoption for a broader set of stakeholders, as existing privacy concerns are mitigated [15]. **Interoperable** standards enhance the usability of the data [25] and leverage competencies to develop accurate models and foster farm system understanding.