The 5 Best Explainable AI (XAI) Tools in 2024

Explainable AI (XAI) helps us understand how AI makes decisions. This is really important because it lets us trust AI systems more and know they're working properly. 

Many AI models are like black boxes — we put data in and get results out, but we don't know what happens inside. This can be a problem, especially when AI is used for important things.

In this article, we'll help you find the best XAI tool for your project by comparing different options. Here's a quick look at how popular XAI tools stack up:

To find the right XAI tool for your use cases, look for the following features:

Interpretability tells how well the tool explains the model’s predictions or decision-making process in a way that humans can understand. It is categorized in the following ways:

• Human-readable explanations: The tool should explain things in a way that makes sense, even if you're not an expert.

• Local interpretability: It should be able to explain why the AI made a specific decision.

• Global interpretability: A broad understanding of how the model generally works across all scenarios to provide an overview of its decision-making process.

Traceability ensures that data flow and decisions through the AI model can be tracked and documented. Its main components are:

• Data lineage: Tracks the entire flow of data through the AI system, from its origin to how it is processed and makes predictions.

• Model documentation: Detailed records of the AI model’s development which includes information about data sources, preprocessing steps, and any changes or updates to the model.

• Audit trails: Keeps logs of the model’s decisions and the input data to promote accountability and review of past predictions.

Visualization provides a greater understanding of the AI model by presenting its decision-making process in visual formats. For proper visualization, you need the following features in an XAI tool: 

• Graphical explanations: Uses visual tools like graphs, charts, or plots to explain how the model processes data and reaches decisions.

• Interactive dashboards: Allows users to explore and interact with visualizations so they can look deeper into the model's behavior and explanations.

• Model behavior analysis: Visualizes how changes in input data affect the model’s predictions.

These features help make AI more understandable and trustworthy. When picking a tool, think about which matters most for your project.

Transparency is the ability to provide clear insights into how the AI model operates. This includes details of how an AI model makes predictions or decisions. Some key aspects of transparency are:

• Model transparency: Ability to show how the model makes decisions and specific predictions.

• Feature importance: Defines those input features or variables that can influence the model’s predictions. 

• Decision rules: Develops explicit conditions or guidelines that the model follows to make decisions—so we can comprehend the logic behind the AI’s outcomes.
  

Open-source XAI tools are freely available software, which means anyone can use them and even help improve them. They're great because you can see how they work, and there's often a community of people available to help you use them.

Here are the most popular open-source XAI tools:

SHAP (SHapley additive exPlanations) explains how machine learning models make predictions by monitoring the way each feature (like age, income, etc.) impacts the model’s output. It uses an idea from game theory to fairly assign credit to each feature and assess whether it pushed the prediction higher or lower. 

Key features

• Model-agnostic: Works with any machine learning model, including complex models like deep learning or tree-based models.

• Global and local explanations: Provides both an overall view of how features contribute across all predictions (global) and detailed insights for specific predictions (local).

• Fairness and consistency: Fairly distributes contributions among features using Shapley values from game theory.

• Visual interpretability: Provides clear visualizations like bar charts, force plots, and dependence plots to make the explanations more intuitive.

LIME (Local interpretable model-agnostic explanations) explains individual predictions by approximating the model's behavior locally around a particular instance. It generates perturbations of the input data and learns a simpler interpretable model (like linear regression) to mimic the behavior of the complex model. This flexible and modular approach allows LIME to be applied to text, tabular, and image data.

Key features

• Local explanations: Provides explanations for individual predictions by approximating the model behavior locally.

• Perturbation-based analysis: Explains predictions by creating variations (perturbations) of the input and observing the output.

• Surrogate models: Uses simple interpretable models like linear regression to mimic the black-box model's behavior around a specific instance.

• Instance-level interpretability: Explains the decision for a specific data point rather than the overall model. 

ELI5 (Explain like I'm 5) simplifies model interpretation by providing feature importance scores and debugging support for various models, such as tree-based, linear models, and deep learning. It gives easy-to-understand, human-readable explanations just like its name suggests. ELI5 also works with LIME and permutation importance techniques for feature interpretation.

Key features

• Feature importance: Highlights the importance of features in the model’s decision-making process.

• Text data explanation: Works well with text data and provides explanations for classifiers and regressors.

• Visualization tools: Offers clear visualizations for understanding model predictions and debugging.

• API integration: Allows integration with other apps or systems for broader use.

InterpretML is developed by Microsoft as a complete toolkit that provides both glass-box (inherently interpretable models) and black-box explainers (LIME and SHAP). It provides both global and local explanations to easily interpret the overall model and individual predictions.

Key features

• Glass-box and black-box models: Supports both inherently interpretable models (e.g., Generalized Additive Models) and explanations for complex models (e.g., using SHAP or LIME).

• What-if Analysis: Allows users to explore how changes in inputs affect predictions.

• Visualizations: Creates clear and easily readable visualizations for model interpretability, feature importance, and errors.

• Interactivity: Users can interact with explanations to explore and compare model behaviors across different data subsets.

AIX360 (AI Explainability 360) is an open-source XAI toolkit from IBM that includes a collection of algorithms to improve the interpretability and explainability of ML models. 

Key features

• Multiple explainability algorithms: Provides several algorithms for different types of models and explanations, such as feature attribution and contrastive explanations.

• Fairness and bias detection: Gives tools for reducing bias in AI models to ensure fairness and transparency.

• Tutorials and resources: Provides detailed guides and tutorials for using the toolkit in different use cases, such as credit scoring and healthcare.

• Domain-specific use cases: Tailored for industry-specific applications, so it is easier to implement explainability in compliance-driven fields like finance and healthcare.

XAI tools build trust in the outcomes of AI as we move toward a future with generative AI. However, there are even more reasons to use XAI tools. They are:

XAI tools help people trust AI decisions more by explaining how the AI reached its conclusion. This is especially important in high-stakes industries, where users must trust and verify the model’s reasoning before depending on its outcomes.

Examples

• Healthcare: Patients are more confident in AI-powered diagnoses or treatment recommendations when they can see which medical factors (e.g., symptoms, history) the model considered.

• Finance: Customers can better understand why their loan was rejected or how AI identified potential fraud by seeing the key factors that the model used, like credit score or unusual spending patterns.

Explainable AI tools are like enterprise AI agents for humans because they give us insights into how AI models think. This helps us make smarter decisions as we become more aware of why AI thinks a certain decision is better overall. 

Examples

• Customer service: AI may analyze a customer's complaint before a human representative talks to them. The XAI tool could explain that the AI detected frustration in the customer's language. This will help the representative know to be extra patient and understanding.

• Legal affairs: For legal professionals, XAI tools can break down complex predictions about case outcomes. If an AI predicts a high chance of winning a lawsuit, the XAI tool might explain which facts of the case were most influential. Lawyers can use this information to strengthen their arguments.

With XAI tools, developers can understand the logic behind a model’s recommendations and identify areas where the model may be unfairly favoring or disadvantaging certain groups. This transparency allows them to fine-tune the model for more accurate and fair outcomes.

Examples

• Bias in demographics: An XAI tool may reveal that an AI hiring system is unfairly favoring male applicants for tech jobs. Once developers know this, they can adjust the AI's training data or algorithms to fix the bias.

• Model weaknesses: These tools can also show which parts of an AI model aren't working well. If an image recognition AI is great at identifying dogs but terrible with cats, the XAI tool can help pinpoint why. This makes it easier to improve the AI's performance.

Many industries have rules about using AI, especially when it affects people's lives. XAI tools help companies follow these rules by making AI decisions responsible and explainable. 

Examples

• Finance: XAI tools can show exactly why an AI system denied a loan, helping banks meet regulatory requirements for transparency.

• Healthcare: Some laws may require doctors to explain treatment decisions. If they're using AI to make those decisions, XAI tools ensure they can provide clear explanations to patients.

Data catalogs help organizations organize, manage, and understand their data through automation. They provide centralized repositories to document metadata, track data lineage, make AI-ready data accessible, and govern data workflows. Here’s how:

• Improved data quality and lineage: Data catalogs document data’s entire journey to confirm that the data used in training AI models is accurate and well-tracked. This traceability generates reliable explanations from XAI tools to ensure the model's inputs are trustworthy and well-understood.

• Data discovery and accessibility: Data scientists and developers can easily find relevant datasets through data catalogs like data.world, using natural language commands. This simplifies finding high-quality data to train and explain AI models.

• Collaboration and knowledge sharing: Data catalogs give everyone a central hub to document data insights. This reduces the communication gap and helps everyone work together and share knowledge about where data comes from and how it affects AI decisions.

XAI tools work best when they are integrated with data catalogs. Here’s why:

• Clearer explanations: Data catalogs provide rich context and detailed information about the training data. This added context helps users understand why the AI is making certain decisions.

• Less bias: Data catalogs can spot potential biases in training data to help create fairer AI models and more balanced explanations.

• Better model governance: Data catalogs act as a single source of truth for all the data used in AI models. This makes it easier to govern data by tracking its lineage and complying with regulations that require AI explainability.

data.world is a leading data catalog platform that's built for the future of AI. It has all the tools for data management that give you 3x more data accuracy.

Here's how it works:

Enterprise-grade data catalog: offers a central place to manage and organize all of an organization's data. This makes it easy for teams to find and use the necessary information.

Knowledge graph foundation: Backed by a knowledge graph that connects data with its business context—definitions, metrics, and processes. This gives you all the details to interpret data assets and make searching through them easy.

AI Context Engine: data.world’s proprietary AI context engine technology integrates XAI capabilities by connecting large language models (LLMs) with the knowledge graph. Here's what it provides:

• Accurate and explainable insights: Allows LLMs to comprehend the business contexts so they can improve their outcomes and insights.

• Streamline data workflows: Enables automation by integrating XAI agents with LLMs for data catalog lookups and query explanations.

Trustworthy AI applications: Provides context and clear explanations to help people build AI systems they can trust.

If you'd like to see how data.world's XAI works in action, schedule a demo to get a firsthand look.