# Correlation-Aware Feature Attribution Based Explainable AI

arXiv: https://arxiv.org/abs/2511.16482 · HTML: https://mlsanity.com/paper/2511.16482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [On the Robustness of Interpretability Methods](https://mlsanity.com/paper/1806.08049/) (2018)
- [On the (In)fidelity and Sensitivity for Explanations](https://mlsanity.com/paper/1901.09392/) (2019)
- [Computing Extremely Accurate Quantiles Using t-Digests](https://mlsanity.com/paper/1902.04023/) (2019)

## Built on by (most cited first, 1 of 1)

- [MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees](https://mlsanity.com/paper/2610.01641/) (2026)

## Abstract (arXiv)

Explainable AI (XAI) is increasingly essential as modern models become more complex and high-stakes applications demand transparency, trust, and regulatory compliance. Existing global attribution methods often incur high computational costs, lack stability under correlated inputs, and fail to scale efficiently to large or heterogeneous datasets. We address these gaps with ExCIR (Explainability through Correlation Impact Ratio), a correlation-aware attribution score equipped with a lightweight transfer protocol that reproduces full-model rankings using only a fraction of the data. ExCIR quantifies sign-aligned co-movement between features and model outputs after robust centering (subtracting a robust location estimate, e.g., median or mid-mean, from features and outputs). We further introduce textscBlockCIR, a groupwise extension of ExCIR that scores sets of correlated features as a single unit. By aggregating the same signed-co-movement numerators and magnitudes over predefined or data-driven groups, textscBlockCIR mitigates double-counting in collinear clusters (e.g., synonyms or duplicated sensors) and yields smoother, more stable rankings when strong dependencies are present. Across diverse text, tabular, signal, and image datasets, ExCIR shows trustworthy agreement with established global baselines and the full model, delivers consistent top-k rankings across settings, and reduces runtime via lightweight evaluation on a subset of rows. Overall, ExCIR provides computationally efficient, consistent, and scalable explainability for real-world deployment.