Baseline Effects on Explainability Metrics: A Critical Re-examination
Published:Dec 12, 2025 10:13
•1 min read
•ArXiv
Analysis
The study's focus on baseline effects is crucial for understanding the reliability of explainability methods. This research likely challenges the common assumptions used in evaluating the effectiveness of these methods.
Key Takeaways
- •Highlights potential biases in explainability metrics due to baseline choices.
- •Suggests a need for more rigorous evaluation methodologies for explainable AI.
- •Focuses on the importance of robust baselines in assessing model interpretability.
Reference
“The article is sourced from ArXiv, indicating a peer-reviewed or pre-print research paper.”