# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
#### 4.2.6 Aviation  
In the aviation sector, where safety, efficiency, and reliability are paramount, the selection of appropriate XAI frameworks is crucial. Among the standout frameworks, Captum, SHAPs, GraphLIMEs, Integrated Gradients, and ELI5 offer tailored solutions to address the unique challenges and requirements of aviation operations. Captum's primary attribution and neuron attribution methods provide granular insights into the behavior of the AI models used in aviation systems, aiding in understanding flight safety, route optimization, and aircraft maintenance. SHAPs' ability to elucidate the contribution of individual features to predictions is valuable for understanding complex aviation systems, such as weather conditions, aircraft performance, and air traffic patterns. GraphLIMEs' extension to graph neural networks enables the interpretable analysis of air traffic networks, flight trajectories, and airport operations, facilitating predictions related to flight delays, route planning, and airspace management. The Integrated Gradients approach to attributing the model's output to each input feature aids in understanding the critical parameters affecting flight safety, efficiency, and performance. Finally, ELI5's user-friendly interface and simplicity facilitate collaboration and decision-making among aviation stakeholders, providing clear and understandable explanations of AI-driven processes and predictions in aviation systems. Leveraging these XAI frameworks empowers aviation professionals to enhance the safety, efficiency, and reliability in aviation operations, ultimately ensuring the smooth and secure functioning of the aviation industry.  
In aviation lies its ability to provide transparent and interpretable insights into complex AI models, particularly in critical decision-making scenarios. XAI ensures that human users, including pilots, air traffic controllers, maintainers, and aircraft manufacturers, can understand and trust the decisions made by AI systems. By employing a comprehensive XAI taxonomy and explanation framework, ref. [87] aims to enhance the decision-making processes in aerospace by integrating human-centric and scientific explanations. The related work discussed in this paper focuses on the implementation of XAI in aerospace, emphasizing its application in supporting operators such as pilots and air traffic controllers in making informed decisions based on meaningful information within time constraints. The increasing utilization of intelligent Decision Support Tools in Air Traffic Management (ATM) necessitates the assurance of their trustworthiness and safety, particularly with the integration of non-traditional entities like AI and ML. Despite the extensive research on these technologies, a gap exists between the research outcomes and practical implementation due to regulatory and practical challenges, including the need for transparency and explainability. To address these challenges, a novel framework is proposed to instill trust in AI-based automated solutions, drawing from the current guidelines and end-user feedback [88]. Recommendations are provided to facilitate the adoption of AI- and ML-based solutions in ATM, leveraging the framework to bridge the gap between research and implementation.  
Figure 3 illustrates the popular application domains and the respective XAI framework that is currently being utilized.