As artificial intelligence increasingly shapes decisions in everything from finance and healthcare to hiring and security, understanding how AI makes its choices is no longer a luxury — it’s a necessity.
Enter explainable AI (XAI) tools: powerful, open-source frameworks built to help developers, regulators, researchers, and curious users peek under the hood of modern machine learning models.
Each of the tools below offers a unique lens into the decision-making process of AI, enabling everything from feature attribution to visual debugging, counterfactual reasoning, and fairness evaluation.
1. BAST AI (Behavioral Artificial System of Truth)
Creator: Beth Rudden
Best for: Explainable AI with full traceability and domain-specific understanding
BAST AI is a powerful Artificial Intelligence Engine featuring a robust data pipeline and pre-built Application Processing Interfaces (APIs). It creates a verifiable system of record while enabling transparent and explainable AI. Its APIs function through intuitive chat interfaces or behind-the-scenes integrations to support any digital outcome.
Key modules include:
Analysis: Complex assessments and workflow execution
Method: Ontology-driven and context-aware processing
Search: Semantic retrieval with OCR-enhanced capabilities
Chat: A secure, personalized interaction companion
BAST AI empowers businesses to build reliable, transparent, and fully auditable AI solutions grounded in trusted data.
2. LIME (Local Interpretable Model-Agnostic Explanations)
Creators: Marco Ribeiro, Sameer Singh, Carlos Guestrin
Best for: Local explanations of individual predictions
LIME creates slightly altered versions of an input (e.g., removing words from a sentence or changing pixels in an image) and observes how the model’s output changes. This process helps determine which parts of the input were most influential in the prediction, making it ideal for understanding isolated decisions and debugging unpredictable model behavior.
3. ELI5 (Explain Like I’m 5)
Best for: Simplified, beginner-friendly explanations
Inspired by the popular Reddit thread, ELI5 offers intuitive explanations for complex machine learning models in plain language. It supports several popular libraries like scikit-learn, XGBoost, and LightGBM, providing text-based breakdowns and visualizations that are especially useful for non-technical stakeholders.
4. What-If Tool
Developed by: Google’s PAIR (People + AI Research) team
Best for: Visual, interactive exploration of model behavior
The What-If Tool is a TensorBoard plugin that allows users to manipulate input variables and immediately observe how predictions change. It supports slicing datasets, visualizing decision boundaries, testing counterfactuals, and performing fairness audits — all without writing code. A powerful option for developers and analysts alike.
5. AIX360 (AI Explainability 360 Toolkit)
Developed by: IBM Research
Best for: A comprehensive suite of explainability algorithms
This Python toolkit includes a wide range of algorithms tailored for different audiences (developers, business leaders, regulators). It’s designed to help assess interpretability from multiple angles — local vs. global, intrinsic vs. post-hoc, and more. AIX360 also supports fairness checks and model transparency benchmarks.
6. Skater
Best for: Model-agnostic interpretation and visualization
Skater is a versatile library for interpreting complex models like random forests, XGBoost, or deep neural networks. It provides both global (dataset-level) and local (single prediction) interpretability, using feature importance plots, partial dependence plots, and surrogate models to uncover what the AI “learned.”
7. InterpretML
Developed by: Microsoft
Best for: Combining explainability with performance tracking
InterpretML features both glass-box models (like Explainable Boosting Machines) and post-hoc tools like SHAP and LIME. It integrates seamlessly with scikit-learn pipelines, and its interactive dashboard lets users explore explanations in-depth — from model accuracy to the impact of individual features.
8. XAITK (eXplainable AI Toolkit)
Developed by: U.S. Department of Defense / Kitware
Best for: Defense, surveillance, and mission-critical applications
Designed for high-stakes environments, XAITK provides modular components for evaluating, visualizing, and validating the reasoning of AI systems. It supports explainability for computer vision tasks and has been applied in defense and security use cases where interpret ability is vital for accountability and trust.
9. SHAP (SHapley Additive exPlanations)
Creators: Scott Lundberg and Su-In Lee
Best for: Feature attribution with strong mathematical rigor
SHAP assigns an importance value to each input feature (like age, salary, or education) by using Shapley values from cooperative game theory. It quantifies how much each feature contributes to the model’s final decision — offering one of the most trustworthy and consistent explanations. Its visualizations make it easier to spot patterns, biases, and anomalies in predictions.
Why These Tools Matter
Together, these tools form a robust ecosystem to help demystify AI systems. From healthcare to hiring, these explainability frameworks empower developers to build more ethical, accountable, and transparent models — and help users trust the technology that’s shaping their lives.
