ENTERPRISE AI ANALYSIS
Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity
By Ahmed M Salih
Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative fea-tures in the model. Current XAI approaches assume the features in the models are independent and calculate the effect of each feature toward model prediction independently from the rest of the features. However, such assumption is not realistic in real life applications. We propose an Additive Effects of Collinearity (AEC) as a novel XAI method that aim to considers the collinearity issue when it models the effect of each feature in the model on the outcome. AEC is based on the idea of dividing multivariate models into several univariate models in order to examine their impact on each other and con-sequently on the outcome. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method. The results indicate that AEC is more robust and stable against the impact of collinearity when it explains Al models compared with the state of arts XAI method.
Executive Impact: Addressing Critical AI Challenges
Our analysis of 'Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity' reveals key advancements for enterprise AI.
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Improved Explanation Robustness (Avg. NMR)
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Minutes to Process Large Dataset (Diabetes)
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Critical XAI Challenge Addressed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Collinearity
The leading cause of misleading XAI explanations, now effectively mitigated.
AEC Methodology for Dependent Features
Start with Multivariate Model
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Divide into Univariate Models
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Model Feature Dependencies (Xz ~ βXi)
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Model Feature Impact on Outcome (y ~ βXi)
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Combine Additive Effects (AEC Calculation)
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Generate Robust Explanations
| Dataset | AEC NMR | SHAP NMR | Robustness Factor (SHAP/AEC) |
|---|---|---|---|
| Simulated Classification | 0.225 | 0.361 | 1.60x |
| Simulated Regression | 0.052 | 0.325 | 6.25x |
| Diabetes Health Indicators | 0.107 | 0.179 | 1.67x |
| Wine Quality | 0.201 | 0.337 | 1.68x |
| Lower NMR indicates higher robustness against collinearity, meaning AEC provides more stable explanations. | |||
Real-world Application: Diabetes Health Indicators Dataset
The study applied AEC to the Diabetes Health Indicators Dataset (UCI Machine Learning Repository) with 21 features and 253,680 samples. A logistic regression model was used. AEC successfully processed this large dataset in 3.35 minutes on a standard PC, calculating feature effect sizes while accounting for collinearity. This demonstrates AEC's practical efficiency for enterprise-scale medical datasets where dependent features are common and accurate, robust explanations are critical for clinical decision support systems.
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Your Implementation Roadmap
A typical journey to integrate advanced explainable AI into your enterprise operations.
Phase 1: Discovery & Strategy Session
We begin with a deep dive into your current AI landscape, identifying key models, data dependencies, and specific business needs for enhanced explainability and robustness against collinearity.
Phase 2: Data & Model Assessment
Our experts analyze your datasets for inherent collinearity and evaluate existing AI models. This phase establishes a baseline for explanation quality and identifies prime candidates for AEC integration.
Phase 3: AEC Integration & Customization
Implementation of the Additive Effects of Collinearity framework, tailored to your models and data. This involves adapting AEC to ensure accurate and stable explanations for your unique enterprise challenges.
Phase 4: Validation & Deployment
Rigorous testing and validation of AEC-enhanced explanations. Once validated, the improved XAI capabilities are deployed, providing your teams with transparent and trustworthy AI insights.
Phase 5: Performance Monitoring & Iteration
Continuous monitoring of explanation stability and model performance. We provide ongoing support and iterative refinements to ensure long-term value and adaptation to evolving data landscapes.
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