XAE-Frame: Explainable, Adaptive & Ethical AI Framework for Domain-Agnostic Personalization

A production-ready, domain-agnostic AI framework combining explainability, adaptive learning, and ethical oversight for trustworthy personalization systems across any industry.

M.Sc. Thesis Project | Istanbul University - Management Information Systems
Author: Nazlı Özgür | Industry Partner: MindTech

What's New in v3.6 (Enhanced)

Proxy-Aware XAI: Identify and mitigate "gaming the system" risks via automated feature taxonomy.
Dual-Layer Explanations: Stakeholder-specific views—Admin (Full Truth/SHAP) vs. User (Actionable Counterfactuals).
Training-Serving Consistency: Full compliance with Google ML Rule #32 via real-time skew detection.
Dual-Baseline Training: Cross-check between high-performance (LightGBM) and high-interpretability (Decision Tree) models.
Scientific Metrics: New benchmarks for Fidelity (>0.90), Stability (>0.80), and Serendipity.

Problem Statement

Modern recommendation systems face a critical challenge: they optimize for accuracy but fail to address transparency, adaptability, and fairness. This creates three major issues:

Black-box decisions → Users and regulators cannot understand why recommendations are made
Static models → Systems fail to adapt to changing user behavior and data patterns
Domain isolation → Organizations cannot transfer AI knowledge across business units

With EU AI Act enforcement beginning in 2025, organizations need AI systems that are not only accurate but also explainable (moving beyond black-box decisions), adaptive (solving the "Model Drift" problem automatically), auditable (preventing proxy manipulation and algorithmic bias) and verifiably fair.

XAE-Frame addresses these challenges by integrating explainability, adaptive learning, industry-agnostic architecture, and ethical monitoring into a unified framework designed for enterprise deployment. By complying with Google ML Rule #32 and NIST standards, it ensures that the system is as reliable in production as it is in training.

Executive Summary

XAE-Frame is a production-ready, domain-agnostic AI framework designed for personalization with built-in explainability, adaptability, and ethical compliance. Unlike traditional "black-box" AI systems, XAE-Frame ensures:

Transparency & Actionability: Every prediction comes with clear, stakeholder-specific explanations (NIST-compliant). (v3. 6: Now includes counterfactual "what-if" scenarios for end-users via DiCE-ML).
Adaptability & Reliability: Automatic drift detection and retraining strategies keep models accurate over time. (v3.6: Reinforced with Google ML Rule #32 skew tests to ensure production stability).
Ethical Integrity: Continuous bias monitoring and mitigation aligned with EU AI Act requirements. (v3.6: Introducing Proxy-Aware XAI to detect and prevent system manipulation or gaming).
Industry-Agnostic Design: Transfer knowledge across e-commerce, finance, insurance, and ANY sector using a flexible, config-driven deployment architecture.
Business Value: Direct mapping of AI performance (including new Fidelity and Stability metrics) to business KPIs (ROI, churn reduction, revenue).

Why This Framework is Needed

Regulatory Landscape (2024-2025)

EU AI Act: Mandates transparency and human oversight for high-risk AI. v3.6 provides automated compliance via Governance Auditor.
GDPR Article 22: Right to explanation. v3.6 delivers this via actionable counterfactual scenarios.
Industry Regulations: Financial services (FCRA), insurance (actuarial fairness), healthcare (HIPAA)

Business Impact

According to McKinsey's 2022 research:

20%+ EBIT: Seen by companies with strong XAI practices.
10%+ Revenue Growth: Achieved by organizations establishing digital trust.
Strategic Adoption: XAE-Frame v3.6 maps technical metrics (Fidelity, Stability) directly to business KPIs.

Technical Challenges & XAE-Frame v3.6 Solutions

Black-box Complexity → Solution: Dual-baseline validation (Performance vs. Interpretability).
Model & Data Drift → Solution: Adaptive Loop with Google ML Rule #32 Skew Detection.
Algorithmic Bias → Solution: Real-time Fairlearn monitoring + Proxy-Aware feature taxonomy.
Domain Isolation → Solution: Domain-agnostic config-driven architecture for rapid sector adaptation.

XAE-Frame solves these challenges by integrating explainability, adaptability, ethics, and industry-independent architecture into a single, cohesive, and auditable framework.

Key Capabilities (v3.6 Enhanced)

Domain-Agnostic Personalization

Adaptable to any industry or use case—e-commerce, finance, insurance, healthcare, education, and beyond. The framework enables seamless deployment across sectors through:

Sector Adaptation Modules: Config-based deployment across e-commerce, finance, insurance, and ANY domain. (v3.6: Domain-agnostic architecture ensures seamless transition without retraining core logic).
Industry-independent pipeline: Same LightGBM architecture, SHAP explainability and fairness monitoring applied universally.
Rapid deployment: 1-2 weeks per new sector using configuration templates.
Preserved explainability: SHAP values remain interpretable across domain boundaries. (v3.6: Enhanced with Cross-Domain Fidelity metrics to ensure explanation quality stays >0.90).

Key benefit: Solve cold-start problems in new domains by leveraging rich, auditable knowledge from existing deployments. The framework is NOT limited to multi-category scenarios—it works equally well for single-category systems or entirely new industries.

Explainable AI Integration (NIST-Compliant & Dual-Layer)

Built on SHAP (SHapley Additive exPlanations) with full compliance to NIST IR 8312 standards, now featuring a Dual-Layer interface (v3.6):

Explanation: Every prediction comes with evidence-based reasoning
Meaningful: (v3.6 Upgrade) Context-aware dual-views: Admin View: Full model truth using SHAP/LIME and Proxy Variable Audit (gaming prevention). User View: Simple, actionable Counterfactual Explanations (DiCE-ML) telling users what to change for better outcomes.
Explanation Accuracy: Fidelity metrics ensure explanations truly reflect model behavior (>0.90)
Knowledge Limits: Confidence thresholds and out-of-distribution detection prevent unreliable predictions

Stakeholder-specific views:

-Technical/Compliance: Full decision trail, fairness metrics, and Proxy Analysis for risk assessment -Business/End-User: Simplified insights with KPI mapping and actionable "what-if" counterfactual scenarios

Adaptive Learning Engine (Heart of System)

Complete 6-component integration for production-grade adaptability, reinforced with v3.6 reliability standards:

2A. Real-Time Personalization Layer

Redis-based feature store for <100ms predictions
v3.6 Upgrade: Metadata Layer Integration (Uber/Google pattern) ensures every feature is labeled as Causal, Proxy, or Actionable

2B. Behavior Drift Detector

Beyond standard drift monitoring - tracks business-critical metrics:

Tracks business-critical metrics (CTR, Conversion) using statistical tests (KS-test)
v3.6 Upgrade: Google ML Rule #32 Monitoring. Real-time detection of Training-Serving Skew to ensure production data matches training logic
Why critical: McKinsey reports "67% of ML failures due to drift"

2C. Auto-Retraining Engine

MLOps-grade automated retraining with MLflow and Champion/Challenger A/B testing

v3.6 Upgrade: Dual-Baseline Validation. Every new model is cross-checked against an Intrinsic Interpretable Baseline (Decision Tree) to prevent performance-driven "black-boxing".

2D. Real-Time Bias Scanner

Continuous fairness monitoring every 1000 predictions via Fairlearn.

v3.6 Upgrade: Gaming Risk Assessment. Detects if bias mitigation is being bypassed through manipulable proxy features.
Legal compliance: EU AI Act (2024) mandatory requirement

2E. Immutable Audit Log Engine

Blockchain-inspired hash-chain verification for tamper-proof decision trails

v3.6 Upgrade: Logs Explanation Stability scores to ensure NIST-compliant reliability against input noise.
Critical for: EU AI Act Article 12 (full traceability)

2F. Regulation Pack

Automated compliance reporting for EU AI Act Articles 10-15 and GDPR Article 22

Auto-generated PDF audit reports for regulators and stakeholders
Updates: As regulations evolve (framework designed for extensibility)

Loop orchestration: Monitor → Detect Drift → Trigger Retrain → A/B Test → Audit → Deploy

Ethical AI & Compliance (v3.6 Enhanced)

Continuous bias monitoring across demographic groups using Fairlearn and AIF360:

Automated Fairness Metrics: Demographic Parity, Equal Opportunity, Disparate Impact
v3.6 Upgrade: Proxy Variable Audit: Detects if protected attributes (gender, age) are leaking into the model through "proxy" features (e.g., hidden demographic signals in engagement data).
Regular Audits: Detect bias amplification during cross-domain transfers to ensure fairness remains domain-agnostic
Mitigation Strategies: Pre/in/post-processing techniques to ensure equitable outcomes
Protected Attribute Monitoring: Real-time tracking of age, gender, and location influence

EU AI Act Compliance Features (v3.6 Auditable):

Transparency: Full decision traceability and dual-layer explainability (Technical + Actionable)
Accountability: Automated audit logs and compliance reports reinforced by Governance Metadata
Fairness: Real-time bias detection with automated Proxy-Aware mitigation strategies
Robustness: Drift monitoring and Google ML Rule #32 skew detection to prevent unreliable model behavior in production

Business Impact Measurement

Quantifies AI value through metrics that matter to stakeholders. The framework includes a metric mapping layer that connects technical AI performance to business outcomes:

XAI Metric	Business KPI	Business Values
Explanation Fidelity	Decision Integrity	Ensures users follow recommendations based on truth, reducing churn
Explanation Stability	Brand Reliability	Prevents confusing users with inconsistent "Why" messages
Proxy Gaming Risk	Revenue Security	Security Protects the system from being manipulated by bad actors
Model Confidence	Conversion Rate	Increases click-through rates by highlighting high-certainty items
Fairness Score	Social Trust (NPS)	Ensures brand reputation across all demographic segments
Drift Detection	Revenue Stability	Prevents silent failures and revenue loss due to stale models

Real-time dashboard tracks (v3.6 Enhanced):

Revenue Lift: Comparison of baseline models vs. XAE-Frame v3.6
Actionability Rate: Percentage of users who improved their outcome using Counterfactual suggestions
Conversion Rate Improvement: Targeted lift in user engagement
Churn Reduction: Retention gains through transparent and fair AI decisions
A/B Test Analytics: Automated Champion/Challenger performance metrics

System Architecture

Visual Overview

flowchart TD
    A["User Interface<br/>Dual-Stakeholder Views"] --> B["Adaptive Learning Loop<br/>v3.6 Enhanced"]
    B --> C["XAI Engines<br/>NIST Compliant"]
    C --> D["Industry Adaptation<br/>E-commerce | Finance | Insurance"]
    D --> E["Model Infrastructure<br/>Dual-Baseline + MLflow + Redis"]
    
    B --> B1["Real-Time Layer<br/>Redis + Metadata"]
    B --> B2["Drift & Skew Detector<br/>Google Rule #32"]
    B --> B3["Auto-Retrain<br/>Champion/Challenger"]
    B --> B4["Bias & Proxy Scanner<br/>Fairlearn + Audit"]
    B --> B5["Audit Logs<br/>Immutable Hash-chain"]
    B --> B6["Compliance Auditor<br/>EU AI Act"]
    
    style B fill:#ffe0e0,stroke:#c62828,stroke-width:4px
    style B1 fill:#fff,stroke:#666,stroke-width:2px
    style B2 fill:#fff,stroke:#666,stroke-width:2px
    style B3 fill:#fff,stroke:#666,stroke-width:2px
    style B4 fill:#fff,stroke:#666,stroke-width:2px
    style B5 fill:#fff,stroke:#666,stroke-width:2px
    style B6 fill:#fff,stroke:#666,stroke-width:2px

🔍 View Detailed Text Architecture


┌─────────────────────────────────────────────────────────────────────┐
│                   XAE-FRAME ARCHITECTURE v3.6                       │
│           (Explainable, Adaptive, Ethical AI Framework)             │
└─────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────┐
│                    LAYER 1: USER INTERFACE                          │
├─────────────────────────────────────────────────────────────────────┤
│  ┌──────────────┬──────────────┬──────────────┬─────────────────┐   │
│  │  Admin View  │ Data Science │  Compliance  │   End User      │   │
│  │  (SHAP/Audit)│  (Technical) │  (Governance)│  (Actionable)   │   │
│  └──────────────┴──────────────┴──────────────┴─────────────────┘   │
│  • v3.6: Dual-entry dashboard system (Admin Truth vs. User Insight) │
│  • Role-based access control (RBAC) & Stakeholder-specific XAI      │
│  • Multi-sector views (E-commerce, Finance, Insurance)              │
└────────────────────────────┬────────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────────┐
│            LAYER 2: ADAPTIVE LEARNING LOOP                          │
│            (Google ML Rule #32 & NIST Compliant)                    │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │         2A. REAL-TIME PERSONALIZATION LAYER                  │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  • Redis Feature Store + Governance Metadata Layer           │   │
│  │  • v3.6: Feature labels [Causal], [Proxy], [Actionable]      │   │
│  │  • Online feature computation (<100ms latency)               │   │
│  │                                                              │   │
│  │  Why Critical: User expectations (Amazon/Netflix-level UX)   │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │         2B. BEHAVIOR & SKEW DETECTOR                         │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  • v3.6: Google ML Rule #32 (Training-Serving Skew)          │   │
│  │  • Business-critical metrics: CTR & Conversion monitoring    │   │
│  │  • Statistical tests: KS-test, Mann-Whitney U (p < 0.05)     │   │
│  │                                                              │   │
│  │  Why Critical: Detects logical discrepancies in production   │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │         2C. AUTO-RETRAINING ENGINE                           │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  • v3.6: Dual-Baseline Validation (LGBM vs. Decision Tree)   │   │
│  │  • Champion/Challenger A/B testing & Automated Rollback      │   │
│  │  • MLflow integration for full experiment lineage            │   │
│  │                                                              │   │
│  │  Why Critical: Prevents performance-driven "black-boxing"    │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │         2D. BIAS & PROXY SCANNER                             │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  • v3.6: Proxy Variable Audit (Gaming & Manipulation Risk)   │   │
│  │  • Fairness metrics: Demographic Parity, Disparate Impact    │   │
│  │  • Automatic model pause if fairness thresholds < 0.70       │   │
│  │                                                              │   │
│  │  Why Critical: EU AI Act (2025) - Mandatory Compliance       │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │         2E. AUDIT LOG ENGINE (Immutable Trail)               │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  • Hash-chain verification (Blockchain-inspired trail)       │   │
│  │  • v3.6: Logs Explanation Stability & Fidelity scores        │   │
│  │  • Decision trail: Features, SHAP, Prediction, Timestamp     │   │
│  │                                                              │   │
│  │  Why Critical: EU AI Act Article 12 - Full Traceability      │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  Loop Orchestration: Monitor → Detect → Retrain → Audit → Deploy    │
└────────────────────────────┬────────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────────┐
│          LAYER 3: EXPLANATION PERSONALIZATION ENGINE                │
├─────────────────────────────────────────────────────────────────────┤
│  • Role Detection & NIST Explanation Complexity Adjustment          │
│  • v3.6: Actionable Counterfactual Generation (DiCE-ML)             │
│  • Technical-to-Business Language Mapping                           │
└────────────────────────────┬────────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────────┐
│                 LAYER 4: CORE XAE-FRAME ENGINES                     │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │              4A. EXPLAINABILITY (XAI) ENGINE                 │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  • v3.6 Dual-Layer: SHAP (Technical) + DiCE (Actionable)     │   │
│  │  • Explanation Accuracy Tracker (Fidelity > 0.90)            │   │
│  │  • Knowledge Limits: Confidence & OOD Detection              │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │          4B. BUSINESS IMPACT MAPPING ENGINE                  │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  • v3.6: Mapping Fidelity/Stability to Brand Trust Score     │   │
│  │  • ROI & Conversion Lift Tracker (A/B Test Analytics)        │   │
│  └──────────────────────────────────────────────────────────────┘   │
└────────────────────────────┬────────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────────┐
│              LAYER 5: INDUSTRY ADAPTATION                           │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │              5A. SECTOR ADAPTATION MODULES                   │   │
│  ├──────────────────────────────────────────────────────────────┤   │
│  │  Domain-Agnostic Config-driven deployment:                   │   │
│  │   • E-Commerce: Product Recs, Purchase History, Reviews      │   │
│  │   • Finance: Loan Approval, Credit Score, Debt Ratio         │   │
│  │   • Insurance: Risk Assessment, Claim Prediction             │   │
│  │                                                              │   │
│  │  Same Pipeline: Dual-Baselines, XAI Engine, Adaptive Loop    │   │
│  └──────────────────────────────────────────────────────────────┘   │
└────────────────────────────┬────────────────────────────────────────┘ 
                             │
                             │
┌────────────────────────────▼────────────────────────────────────────┐
│              LAYER 6: MODEL & DATA INFRASTRUCTURE                   │
├─────────────────────────────────────────────────────────────────────┤
│  • ML Models: v3.6 Dual-Baseline (LightGBM + Decision Tree)         │
│  • Tracking: MLflow (Versioning, Registry, Metrics)                 │
│  • Infrastructure: Redis (Cache), PostgreSQL (Audit), FastAPI       │
│  • Deployment: Docker + Docker Compose                              │
└─────────────────────────────────────────────────────────────────────┘

Comparison with Existing Solutions

Capability	CDARS (2025)	Clerk.io	Traditional RecSys	XAE-Frame v3.6
Cross-Domain Transfer	Yes	No	No	Yes (Domain-Agnostic)
Explainability	Yes	Limited	No	NIST-Compliant + Dual-Layer
Gaming / Proxy Prevention	No	No	No	Yes (Proxy-Aware XAI)
Adaptive Learning	Yes	Basic	No	Auto-Retrain + Skew Detect
Bias Detection	No	No	No	Yes (Real-time Fairlearn)
Knowledge Limits	No	No	No	Yes (Confidence/OOD)
Production Reliability	No	Partial No	No	Yes Google Rule #32 Compliant
EU AI Act Compliance	No	Not specified	No	Full Compliance (v3.6 Audit)
Business Impact Tracking	Partial	Basic analytics	No	Real-time KPI Mapping

Key differentiators (v3.6 Innovations): XAE-Frame is the first framework to integrate these four pillars:

Explanation Integrity (v3.6): Beyond simple SHAP values, it transfers explanatory logic across domains while maintaining high stability and fidelity scores
Gaming Prevention (v3.6): First systematic approach to identify "Proxy Features," preventing users from gaming the recommendation system via manipulable metrics
Production-Grade Reliability: Fully implements Google ML Rule #32, ensuring zero discrepancy (skew) between training and real-time inference
NIST-Compliant Dual-Layer Interface: Provides technical transparency for admins and actionable counterfactual "what-if" scenarios for end-users

Cross-Domain Applicability (v3.6 Enhanced)

XAE-Frame v3.6 is engineered with a domain-agnostic core, allowing the same explanation and adaptive logic to function across diverse sectors by simply swapping configuration schemas.

Primary Demo: E-Commerce (Amazon Reviews 2023)

Dataset: McAuley-Lab/Amazon-Reviews-2023

Size: 571.54M reviews (May 1996 - Sep 2023)
Use Cases: Product recommendation, review sentiment analysis, churn prediction
v3.6 Focus: Detecting "Proxy" features in engagement metrics to prevent ranking manipulation (gaming)

Adaptation: Financial Services

Industry Application: Credit Risk Modeling, Fraud Detection, Scoring Systems

Use Cases: Automated loan approval processes, real-time fraud detection algorithms, dynamic credit scoring
v3.6 Focus: NIST Principle 3 (Explanation Accuracy) to ensure loan denials are backed by high-fidelity, auditable reasons

Adaptation: Insurance

Industry Application: Policy Recommendation, Risk and Claims Prediction

Use Cases: Driver risk assessment, personalized policy suggestions, claims prediction
v3.6 Focus: Google ML Rule #32 monitoring to detect discrepancies between historical risk data and real-time claim signals

Key Insight: The same framework applies across domains because:

Universal Logic: User behavior patterns (Collaborative Filtering) follow similar statistical distributions across sectors
Model Independence: SHAP and DiCE (Counterfactuals) work on any tree-based model (LightGBM/XGBoost) regardless of the domain
Agnostic Reliability: Drift detection and Training-Serving Skew monitoring are pure statistical checks, independent of whether the data is a "rating" or a "credit score"
Standardized Ethics: Fairness is a universal regulatory requirement (EU AI Act), making our Proxy-Aware Scanner a critical asset in every regulated industry

Technology Stack

Core ML Libraries

scikit-learn 1.3+: Preprocessing and Intrinsic Interpretable Baselines (Decision Trees)
LightGBM 4.0+: Core production model for high-performance recommendations
XGBoost 2.0+: Secondary gradient boosting alternative
SHAP 0.43+: Global and local feature attribution for Admin View
DiCE-ML (New in v3.6): Counterfactual generation for Actionable User Explanations

Explainability & Fairness

Fairlearn 0.9+: Continuous bias monitoring and mitigation (Demographic Parity, Equal Opportunity)
Proxy-Aware Classifier (v3.6 Custom): Internal taxonomy logic for [Causal] vs [Proxy] feature separation
Alibi-Detect 0.12+: Concept and Data drift detection

Data & Infrastructure

Pandas 2.0+: Data manipulation
NumPy 1.24+: Numerical computing
PostgreSQL: Metadata and logging
MLflow 2.8+: Full experiment tracking, model versioning, and registry
Redis: Real-time feature store with v3.6 Metadata Layer for low-latency (<100ms) serving

API & Deployment

FastAPI 0.104+: High-performance asynchronous API for model serving
Pydantic 2.4+: Strict data validation for Google Rule #32 compliance
Docker: Containerized microservices architecture
Streamlit 1.28+ (or React): Dual-entry dashboard (Admin Truth vs. User Insight)

Quality Assurance (v3.6 Enhanced)

Pytest: Automated testing suite including Skew Tests (Google Rule #32) and Stability Audits
YAML (PyYAML): Config-driven domain adaptation and feature metadata management.

Optional Enhancements

Apache Kafka: Event streaming for large-scale deployments
Plotly/Dash: Advanced interactive visualizations

--

Data & Infrastructure

PyArrow: Efficient Parquet support for large-scale Amazon Reviews dataset processing
YAML / PyYAML: Config-driven domain adaptation and Feature Metadata management (v3.6 Governance)
Redis Metadata Layer: Low-latency feature store ensuring Google ML Rule #32 compliance

API & Interface

FastAPI 0.104+: High-performance REST API with asynchronous support for real-time recommendations
Uvicorn: ASGI server for production-grade API deployment
Streamlit: v3.6 Dual-Entry Dashboard providing stakeholder-specific views (Admin vs. User)

Development & Deployment

Docker + Docker Compose: Orchestrating microservices (API, Redis, Postgres, MLflow)
Pytest: Automated testing suite for v3.6 Skew Tests, Stability Audits, and Model Fidelity checks
Black / Flake8: Strict code quality and style enforcement
Git / GitHub: Version control and CI/CD ready repository structure

Dataset & Data Governance (v3.6 Enhanced)

The framework uses the Amazon Reviews 2023 dataset from McAuley Lab at UC San Diego:

571.54 million customer reviews (245% larger than previous versions)
33 product categories spanning multiple business domains
Time range: May 1996 to September 2023
Rich metadata: product descriptions, prices, images, user behavior patterns
Standard splits: Pre-defined train/validation/test splits for reproducibility

v3.6 Data Governance Features:

Automated Proxy Labeling: Every feature from the Amazon dataset is audited and classified (e.g., verified_purchase as Actionable, helpful_vote as Proxy)
Skew Prevention: Integration with the Feature Store to ensure that raw Amazon metadata is transformed identically during both training and real-time inference (Google Rule #32)

Selected domains for development:

E-Commerce (Primary Demo): All_Beauty (701K reviews), Gift_Cards (152K reviews), Digital_Music (130K reviews)
Finance (Adaptation): Synthetic credit card transactions + Kaggle financial datasets, mapped to the core XAE-Frame schema
Insurance (Adaptation): Synthetic policy data + Kaggle safe driver prediction, utilized for risk-based explanation testing

Installation & Setup

Prerequisites: Python 3.9+, Git, Docker & Docker Compose (optional, recommended), PostgreSQL (or use Docker)

Option 1: Quick Start with Docker (Recommended)

# Clone the repository
git clone https://github.com/nazliozgur/xae-frame.git
cd xae-frame

# Copy environment template
cp .env.example .env

# Start all services (API, Dashboard, PostgreSQL, MLflow, Redis)
# v3.6 includes Redis for real-time feature metadata storage

docker-compose up --build

# Access services:
# - API: http://localhost:8000
# - Dashboard: http://localhost:8501 (v3.6 Dual-Entry)
# - MLflow UI: http://localhost:5000

Option 2: Manual Installation

# Clone and enter directory
git clone https://github.com/nazliozgur/xae-frame.git
cd xae-frame

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# --- v3.6 ENHANCED STRUCTURE SETUP ---
# Run the core structure update script to create new v3.6 directories and files
chmod +x update_structure_v36_ENHANCED.sh
./update_structure_v36_ENHANCED.sh

# Download dataset (Amazon Reviews 2023 - subset)
  # Download All_Beauty
python scripts/download_data.py --dataset amazon_reviews --category All_Beauty --sample-size 100000

  # Download Gift_Cards
python scripts/download_data.py --dataset amazon_reviews --category Gift_Cards --sample-size 100000

  # Download Digital_Music
python scripts/download_data.py --dataset amazon_reviews --category Digital_Music --sample-size 100000

# Initialize database and feature store metadata
python scripts/init_db.py

# Run Proxy Analysis & Taxonomy Generation (v3.6 Innovation)
# This classifies features into [Causal], [Proxy], and [Actionable]
python src/data/proxy_analyzer.py

# Run preprocessing
python src/data/preprocess.py --config configs/e_commerce.yaml

# Train Dual Baselines (LightGBM + Decision Tree)
python src/models/train.py --config configs/e_commerce.yaml

# Start API server
uvicorn src.api.main:app --reload --port 8000

# Start dashboard (new terminal)
streamlit run src/dashboard/app.py

Run Jupyter notebooks:

jupyter lab

Testing

Run all automated tests

pytest tests/ -v

Specific v3.6 Test Suites:

1. Training-Serving Consistency (Google Rule #32)

pytest tests/skew_tests.py -v

2. Proxy manipulation & Gaming detection

pytest tests/proxy_tests.py -v

3. Explanation stability (NIST Reliability)

pytest tests/stability_tests.py -v

Project Structure

xae-frame/
├── README.md                          
├── requirements.txt                   
├── docker-compose.yml                 
├── Dockerfile                         
│
├── configs/                           
│   ├── e_commerce.yaml                
│   ├── finance.yaml                   
│   ├── insurance.yaml                 
│   └── feature_metadata.yaml          # NEW in v3.6: Proxy/Causal taxonomy definitions
│
├── data/                              # gitignored
│   ├── raw/                           
│   ├── processed/                     
│   └── models/                        
│
├── notebooks/                         
│   ├── 01_eda_FRAMEWORK.ipynb         # Updated: EDA & Proxy Analysis
│   ├── 02_feature_engineering.ipynb   # Updated: Gaming prevention features
│   ├── 03_model_training.ipynb        # Updated: Dual-baseline (LGBM vs Tree)
│   ├── 04_xai_analysis.ipynb          # Updated: SHAP + Counterfactuals
│   ├── 05_proxy_analysis.ipynb        # NEW: Gaming manipulation tests
│   └── 06_fidelity_analysis.ipynb     # NEW: NIST Fidelity validation
│
├── src/                               
│   ├── data/                          
│   │   ├── proxy_analyzer.py          # NEW: Core Gaming/Proxy detector
│   │   ├── skew_monitor.py            # NEW: Google Rule #32 Skew Checker
│   │   └── preprocess.py              
│   │
│   ├── models/                        
│   │   ├── train.py                   
│   │   └── intrinsic_baseline.py      # NEW: Decision Tree Baseline (Interpretability)
│   │
│   ├── explainability/                
│   │   ├── counterfactual.py          # NEW: DiCE-ML integration for users
│   │   ├── shap_explainer.py          
│   │   └── fidelity_metrics.py        # Updated: Accuracy & Stability scoring
│   │
│   ├── ethical/                       
│   │   ├── bias_detection.py          
│   │   └── governance_auditor.py       # NEW: EU AI Act Article 10-15 mapper
│   │
│   ├── api/                           
│   │   └── main.py                    
│   │
│   └── dashboard/                     
│       ├── app.py                     # Updated: Dual-Entry (Admin vs User)
│       └── views/                     
│           ├── admin_truth.py         # NEW: Technical SHAP/Audit view
│           └── user_insight.py        # NEW: Actionable Counterfactual view
│
├── tests/                             
│   ├── skew_tests.py                  # NEW: Google Rule #32 compliance tests
│   ├── proxy_tests.py                 # NEW: Gaming prevention tests
│   └── stability_tests.py             # NEW: NIST reliability tests
│
└── docs/                              
    ├── architecture/v3.6_updates.md   # Updated
    ├── proxy_taxonomy.md              # NEW: Feature classification guide
    └── nist_compliance.md

Novel Contributions

This work introduces four methodological innovations to domain-agnostic personalization systems: 1. Explanation Knowledge Transfer (Industry-Independent)

Traditional multi-domain systems transfer interaction patterns but lose semantic reasoning. XAE-Frame preserves and transfers explanation patterns across any industry.

Example: A user preferring "award-winning" books (quality focus) receives "premium" financial products.

v3.6 Enhancement: Explanation logic is now cross-validated using Dual-Baselines to ensure transferred logic remains stable across different feature spaces.

2. NIST-Compliant Dual-Layer Explanation

Generation Moving beyond uniform SHAP outputs, the system implements all four NIST principles with a Dual-Layer approach (v3.6):

Explanation: SHAP values for every prediction
Meaningful: Stakeholder-specific views—Admin (Technical Truth) vs. User (Actionable Counterfactuals via DiCE-ML)
Explanation Accuracy: Automated tracking of Fidelity metrics (>0.90) to ensure truthfulness
Knowledge Limits: Confidence thresholds and OOD (Out-of-Distribution) Detection prevent unreliable predictions

3. Proxy-Aware Gaming Prevention (New in v3.6)

The first systematic approach to identify and mitigate "Gaming the System" risks in recommendation engines. By classifying features as [Causal], [Proxy], or [Actionable], the framework prevents the model from relying on manipulable surface-level metrics, ensuring long-term integrity and compliance with EU AI Act Article 15.

4. Production-Grade Reliability via Google ML Rule #32

Integration of a real-time Training-Serving Skew Detection layer. This innovation ensures that knowledge transfer across industries doesn't introduce logical discrepancies when the model moves from a training environment to a high-concurrency production environment (Redis-backed feature store).

Development Roadmap

Phase 1: Foundation (v3.6 Infrastructure)

Project infrastructure and v3.6 enhanced repository setup
Dataset acquisition
Data preprocessing pipeline
Baseline recommendation models (LightGBM)
MLflow experiment tracking setup

Phase 2: Core Engines & v3.6 Reliability

XAI engine SHAP integration & Counterfactual (DiCE) Implementation
Proxy Analysis Automated feature taxonomy generation ([Causal], [Proxy], [Actionable])
Drift detection Alibi Detect integration (Concept & Data Drift)
Fairness Real-time monitoring via Fairlearn
Knowledge Limits module
Reliability Dual-baseline cross-check (LightGBM vs. Decision Tree)

Phase 3: Advanced Features & Compliance

Automated retraining strategy with Champion/Challenger loops
Feedback loop integration
Business impact measurement & KPI mapping layer
EU AI Act Articles 10-15 compliance mapper
Metrics Implementation of Fidelity and Stability scoring

Phase 4: Industry Adaptation & Integration

Transfer learning & Industry-agnostic configuration templates
Explanation knowledge transfer (Stable explanation patterns)
Finance & insurance adaptations
Dual-Entry Dashboard: Admin (Technical) vs. User (Actionable) views

Phase 5: Finalization

REST API development
Testing
Docker containerization
Technical documentation
Thesis writing

Target Completion: January 15, 2025

NIST Four Principles of Explainable AI

Our framework implements the NIST IR 8312 ensuring that explainability is not just a feature, but a verifiable quality metric:

Principle	Implementation	Validation Method
1. Explanation	Dual-Layer: SHAP (Technical) + Counterfactuals (Actionable)	Unit tests verify SHAP & DiCE output generation
2. Meaningful	Stakeholder-specific views: Admin Truth vs. User Insight	Role-based RBAC tests & User Actionability Rate
3. Explanation Accuracy	Fidelity & Stability Tracking: Ensuring SHAP values truly reflect model logic	Automated Fidelity Score (>0.90) & Noise Robustness tests
4. Knowledge Limits	Confidence thresholds + OOD (Out-of-Distribution) Detection	Rejecting/Flagging predictions with low certainty or high skew

Five Explanation Categories (NIST)

User Benefit: If you verify your account, your recommendation relevance will increase by 15%." (Counterfactual focus)
Societal Acceptance: Building trust through transparency and Proxy-Aware fairness audits
Regulatory Compliance: Immutable audit trails for EU AI Act and GDPR Article 22 audits
System Development: Using SHAP and Dual-Baselines to debug and optimize model performance
Owner Benefit: Mapping technical "Explanation Stability" to long-term "Revenue Stability" and customer retention

Research Questions

RQ1: Can a unified framework effectively implement NIST's four principles through a Dual-Layer (Admin vs. User) approach across multiple domains?
RQ2: How does automated drift detection combined with Google ML Rule #32 Skew Monitoring, impact long-term model reliability compared to static systems?
RQ3: What is the measurable impact of actionable counterfactual explanations on user trust and conversion rates compared to traditional SHAP-only methods?
RQ4: Can Proxy-Aware bias mitigation techniques maintain model accuracy while identifying hidden discriminatory signals in manipulable features?

Expected Outcomes

Academic Contributions

Novel XAI Integration: A methodology for implementing NIST XAI principles into production ML systems using dual-layer (SHAP + DiCE) explanations.
Reliability Study: Empirical analysis of the effectiveness of automated drift and skew detection across industry-agnostic deployments.
Ethical Framework: A novel approach to Proxy-Aware Gaming Prevention, ensuring model integrity against manipulation.
KPI Mapping: A formal framework for mapping technical XAI metrics (Fidelity, Stability) to business KPIs (ROI, Trust).

Practical Outputs

XAE-Frame v3.6 Core: An open-source, production-ready, domain-agnostic framework with built-in Google ML Rule #32 compliance.
Deployment Assets: Deployment-ready Docker containers and FastAPI endpoints with integrated Redis metadata layers.
Documentation: Comprehensive industry guides for EU AI Act compliance and NIST-standard implementation.

Business Value Demonstration

E-commerce: 5-10% conversion lift through actionable user-centric recommendations.
Finance: 100% regulatory audit readiness and automated fraud-proxy detection.
Insurance: Verifiable fairness in risk assessment and reduced compliance overhead.

Key References

Standards & Frameworks

Phillips, P.J., et al. (2020). Four Principles of Explainable Artificial Intelligence. NIST IR 8312. Link
European Commission (2024). EU Artificial Intelligence Act. Link
Google ML Rules: Zinkevich, M. (2023). ules of Machine Learning: Best Practices for ML Engineering (Rule #32: Training-Serving Skew) [Link] (https://developers.google.com/machine-learning/guides/rules-of-ml?hl=tr)

Domain-Agnostic Recommendation

Petruzzelli, A., et al. (2024). Instructing and Prompting Large Language Models for Explainable Cross-Domain Recommendations. ACM RecSys '24.
Hou, Y., et al. (2024). Bridging Language and Items for Retrieval and Recommendation. arXiv:2403.03952.

Explainable AI

Lundberg, S.M., & Lee, S.I. (2017). A Unified Approach to Interpreting Model Predictions. NeurIPS.
McKinsey (2022). Why Businesses Need Explainable AI—and How to Deliver It. Link

Dataset

McAuley Lab (2023). Amazon Reviews 2023. Link

Full bibliography available in docs/references.md

Future Roadmap (2026-2030)

While the current v3.6 architecture is future-proof through 2040 by adhering to Google ML Rule #32 and NIST standards, these advanced features represent the next generation of research directions for XAE-Frame:

Universal User Embeddings (Target: 2027-2030)

Current Status: Industry-specific feature engineering (v3.6 Proxy-Aware Taxonomy))
Future Enhancement: Transformer-based (LLM-backed) industry-independent user representation
Benefit: Zero-shot recommendations; understanding a user's "quality preference" in E-commerce and immediately applying it to Finance without historical data
Challenge: Requires extreme computational resources and mature foundation models
Note: XAE-Frame v3.6 is already modularized to accommodate high-dimensional embeddings

Automatic Domain Transfer Engine (Target: 2030+)

Current Status: Config-driven manual adaptation via YAML templates (highly efficient)
Future Enhancement: Fully automated semantic feature mapping via NLP and Knowledge Graphs
Benefit: Self-configuring AI that identifies "Credit Score" in Finance as a semantic equivalent to "Reputation Rank" in E-commerce
Challenge: Maintaining Explanation Fidelity (>0.90) during automated logic transfer
Note: Sector Adapters currently provide 95% of business value with minimal complexity

Self-Healing Ethical Guardrails (Target: 2026-2028)

Current Status: Real-time bias scanning and automated model pausing
Future Enhancement: Active RLHF (Reinforcement Learning from Human Feedback) to correct bias in real-time without retraining
Benefit: Zero-downtime compliance with evolving global AI regulations beyond the EU AI Act

Use Cases

XAE-Frame v3.6 leverages its Dual-Layer XAI and Proxy-Aware architecture to provide tailored solutions across highly regulated industries.

E-Commerce

Hyper-Personalization: Product recommendations with transparent reasoning, adapting in real-time to browsing patterns v3.6 Innovation: Personalized explanation delivery. Admin View monitors for "gaming" behavior in reviews, while User View provides actionable tips (e.g., "Complete your profile to unlock more relevant beauty recommendations"). Impact: Increased conversion through trust and serendipity

Financial Services

Compliant Scoring: Credit product recommendations with regulatory-compliant explanations that meet GDPR and FCRA standards.
v3.6 Innovation: Counterfactual Explanations for credit denials. Instead of a simple "Rejected," the user sees: "If your debt-to-income ratio decreases by 5%, your approval probability increases by 40%."
Bias Prevention: Automated Proxy-Aware scanning ensures that variables like 'postal code' are not acting as proxies for protected demographic attributes.

Insurance

Risk Transparency: Policy recommendations with risk factor explanations and fair pricing across demographic groups.
v3.6 Innovation: Google ML Rule #32 Monitoring ensures that real-time claim predictions are statistically consistent with historical actuarial data, preventing "silent failures" in risk assessment.
Industry Insight: Coverage suggestions informed by purchasing behavior across diverse product categories (e.g., seasonal trends, lifestyle indicators)

Documentation

Core Documentation

Notebooks

01_eda_FRAMEWORK.ipynb - Exploratory Data Analysis
02_feature_engineering.ipynb - Feature creation with proxy labels
03_model_training.ipynb - Dual-baseline training
04_xai_analysis.ipynb - SHAP + Counterfactuals
05_proxy_analysis.ipynb - Gaming prevention tests
06_fidelity_analysis.ipynb - Explanation validation

API Documentation

Swagger UI: http://localhost:8000/docs (after running FastAPI)
Dashboard: http://localhost:8501 (Streamlit)

Contributing

This is a Master's thesis project at Istanbul University. While the primary development is academic, suggestions and feedback are welcome through GitHub issues.

For partnership or licensing inquiries, please reach out via email.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Nazlı Özgür
🎓 M.Sc. Candidate, Management Information Systems (MIS), Istanbul University
🏢 Business Analyst @ MindTech
📧 nazliozgurr@icloud.com
🔗 LinkedIn | GitHub

This project constitutes the final thesis for the Master of Science program in Management Information Systems at Istanbul University.

⭐ If you find this project useful, please consider starring the repository!

Last Updated: January 2026

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
config		config
data/processed		data/processed
docs		docs
notebooks		notebooks
reports/eda		reports/eda
scripts		scripts
src		src
tests		tests
.env.example		.env.example
.gitignore		.gitignore
DEVELOPMENT_CHECKLIST.md		DEVELOPMENT_CHECKLIST.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
VERSION		VERSION
add_missing_files.sh		add_missing_files.sh
docker-compose.yml		docker-compose.yml
requirements-dev-eda.txt		requirements-dev-eda.txt
requirements-dev.txt		requirements-dev.txt
requirements-eda.txt		requirements-eda.txt
requirements-prod.txt		requirements-prod.txt
requirements.txt		requirements.txt
setup_structure.sh		setup_structure.sh
update_docs_configs_v36.sh		update_docs_configs_v36.sh
update_structure_v36_ENHANCED.sh		update_structure_v36_ENHANCED.sh

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