Enterprise AI Analysis
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
This paper introduces a novel approach to incorporate Random Forest (RF) proximities into the eXplainable Case-Based Reasoning (XCBR) framework for identifying prototypes, critics, semi-factuals, and counter-factuals. These play critical roles in explaining RF outcomes, especially in financial and regulated industries. The analysis across various datasets and evaluation metrics highlights the method's effectiveness and accuracy.
Executive Impact & Strategic Value
Integrating advanced XAI techniques like XCBR with Random Forests significantly enhances interpretability in financial models. This leads to more robust decision-making, better regulatory compliance, and increased stakeholder trust. By understanding why a model makes certain predictions, financial institutions can mitigate risks, optimize strategies, and provide clear justifications for AI-driven decisions. The ability to identify prototypes, critics, and counter-factuals provides invaluable insights into model behavior and data distribution, leading to more transparent and reliable AI systems.
0%
Reduction in Audit Time
0x
Increase in Model Trust
0%
Improved Regulatory Compliance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Prototypes
Critics
Counter-factuals
Semi-factuals
Understanding Prototypes
Prototypes are representative data points that provide an overview of the dataset or a specific class. In loan approvals, they show typical approved and not-approved individuals. Our approach uses High Density Points (HDP) and a K-medoids variation to select prototypes, maximizing proximities to nearest neighbors of the same class. This ensures prototypes accurately represent class distributions, aiding comprehensive understanding of the population.
The study highlights that Gap Proximity, a refined RF distance metric, consistently outperforms traditional Euclidean methods in identifying high-quality prototypes, leading to better F1 scores for prototype-based predictors. These prototypes are shown to be excellent representations, with low compactness values and robust performance.
Identifying Critics
Critics are data points poorly represented by prototypes, highlighting areas where the model's generalization might be inadequate. They identify data points that deviate from typical model predictions. The Maximum Mean Discrepancy (MMD) critic method is adapted to derive critics using a witness function, which estimates how much two distributions differ. Points with higher witness function values are selected as critics, indicating poor representation by prototypes.
Evaluation shows critics consistently have significantly higher outlier and OOD (Out-Of-Distribution) scores compared to prototypes. This indicates they are indeed farther from typical data distributions and help differentiate unusual cases that challenge the model. Higher diversity among critics ensures a broad representation of unusual scenarios, crucial for understanding model limitations and edge cases.
Exploring Counter-factuals
Counter-factuals illustrate 'if only' scenarios, showing what minor modifications could alter a model's outcome. They are the closest points to a query that yield a different label. For example, if a loan applicant was rejected, a counter-factual might suggest specific changes (e.g., increased income or credit score) that would lead to approval. Our method utilizes RF-GAP proximities to find these points, effectively approximating the decision boundary.
The analysis confirms that counter-factuals exhibit lower robustness than semi-factuals, meaning the model's predictions change more rapidly with small feature alterations. They also show high diversity, revealing various paths in the feature space that lead to different outcomes, which is beneficial for understanding model sensitivity and decision boundaries.
Understanding Semi-factuals
Semi-factuals represent 'even if' scenarios, demonstrating how outcomes remain consistent despite potential changes. They are the points furthest from the query while maintaining the same label. For instance, in a loan application, a semi-factual might indicate that even if the applicant improved their credit score slightly, they would still not be approved. These methods, like counter-factuals, effectively approximate decision boundaries using RF-GAP proximities.
Evaluation reveals that semi-factuals generally have higher distance values from the query compared to counter-factuals, aligning with their definition. They show high sparsity, suggesting minimal feature changes are needed to maintain predictions. Higher Out-Of-Distribution (OOD) distances for semi-factuals indicate their position near decision boundaries, reflecting the model's sensitivity to feature changes.
0.780
Average F1 Score (GAP Proximity Prototypes)
Enterprise Process Flow
Input Query (q)
→
Compute RF-GAP Proximities
→
Identify Prototypes, Critics, Counter-factuals, Semi-factuals
→
Generate Explanations
→
Stakeholder Understanding & Trust
| Metric | RF-GAP Proximity | L2 Distance |
|---|---|---|
| Prototype F1 Score |
|
|
| Critic OOD Distance |
|
|
| Sparsity (Counter-factuals) |
|
|
Financial Fund Classification with XAI
In a fund classification problem using Morningstar Categories (e.g., 'Large Blend', 'Large Value', 'Large Growth'), RF-GAP proximities significantly improved the interpretability and accuracy of predictions.
Challenge: A major financial institution needed to accurately classify investment funds into Morningstar Categories while providing transparent explanations for these classifications, crucial for investor trust and regulatory compliance.
Solution: Implemented the RF-GAP proximity-based XCBR framework to identify prototypes representing typical funds within each category, critics highlighting unusual or misclassified funds, and counter-factuals showing minimal changes needed to reclassify a fund. This allowed for detailed explanations of fund predictions.
Outcome: Achieved a 0.7477 F1 score for prototypes in the funds dataset, indicating highly representative examples. The use of critics successfully identified outlier funds, and counter-factuals provided clear actionable insights for reclassification. Overall, transparency and trust in the AI-driven fund classification system significantly improved.
Quantify Your AI Impact
Use our interactive calculator to estimate the potential time and cost savings AI can bring to your enterprise.
Estimate Your Potential Savings
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Enterprise AI Roadmap
A phased approach to integrate advanced AI explainability into your operations, ensuring smooth adoption and maximum impact.
Phase 1: RF-GAP Integration
Integrate RF-GAP proximities into existing XCBR frameworks for initial testing and validation.
Phase 2: Prototype & Critic Identification
Develop and refine algorithms for identifying prototypes and critics across diverse financial datasets.
Phase 3: Factuals Generation & Evaluation
Implement counter-factual and semi-factual generation, rigorously evaluating their explanatory power.
Phase 4: Stakeholder Workshop & Feedback
Conduct workshops with financial analysts and regulators to gather feedback and refine explanations for real-world applicability.
Phase 5: Production Deployment & Monitoring
Deploy the XAI solution in production environments, continuously monitoring its effectiveness and user adoption.
Ready to Empower Your Enterprise with XAI?
Unlock transparent, trustworthy, and impactful AI. Our experts are ready to guide your strategy.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat