Balancing the trade-off between diffusion distance and directional coherence to generate a diverse set of high-quality counterfactual explanations requires careful optimization and consideration of the specific context and objectives. One approach to achieve this balance is through multi-objective optimization techniques, where the weights assigned to diffusion distance and directional coherence are dynamically adjusted based on the desired outcomes. By incorporating user preferences or constraints into the optimization process, the framework can generate a diverse range of counterfactual explanations that align with both the underlying data structure and human intuition.
Another strategy to optimize the trade-off is through iterative refinement and feedback loops. By allowing users to interact with the generated counterfactual explanations and provide feedback on their relevance and coherence, the framework can adapt and improve over time. This iterative process can help identify the optimal balance between diffusion distance and directional coherence for generating high-quality explanations that are both feasible and aligned with human reasoning patterns.