Which factor relates to the explainability of the AI solution's decisions?
A. Model complexity
B. Training time
C. Number of hyperparameters
D. Deployment time
Answer (2)
The factor that relates to the explainability of AI solution's decisions is model complexity (Option A). More complex models can obscure how decisions are made, while simpler models are easier to interpret. Thus, balancing model complexity is crucial for enhancing AI explainability.
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The factor that relates to the explainability of an AI solution's decisions is A. Model complexity .
Explainability in AI refers to the ability to understand and interpret how an AI model makes its decisions or predictions. Here is how model complexity is connected to explainability:
Model Complexity : This refers to how intricate and sophisticated an AI model is. Complex models, such as deep neural networks, often consist of many layers and parameters, making them difficult to interpret. On the other hand, simpler models like decision trees or linear regression are easier to understand because their decision-making processes can be traced and visualized.
Why Explainability Matters : Explainable AI is important for trust and accountability. When users or stakeholders can understand how decisions are made, they are more likely to trust the AI system. In sensitive applications like healthcare or finance, understanding the model's decision-making process is critical.
Balancing Complexity and Explainability : While more complex models might offer higher accuracy and performance, they often sacrifice explainability. It is crucial to find a balance that meets the needs of the application. For applications where transparency is important, simpler models might be preferred.
Techniques for Explainability : There are methods designed to make AI models more explainable, such as feature importance analysis, LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations), which help in providing insights into how features influence the final output.
Therefore, in the context of explainability, understanding and potentially reducing model complexity can help stakeholders interpret the decisions made by AI systems more effectively.
