What are the Challenges in Achieving Explainability?
While the benefits are clear, achieving explainability in AI for toxicology comes with challenges:
Complexity vs. Interpretability: Often, the most accurate models are complex (e.g., deep neural networks), making them harder to interpret. Data Limitations: Limited or biased data can lead to inaccurate models, and explaining such models might reveal these flaws rather than solve them. Trade-offs: There is often a trade-off between model performance and explainability, where increasing one may decrease the other.
