XAI is an innovative Machine Learning library crafted with the core principle of AI explainability. It is designed to equip data scientists and machine learning engineers with various tools for analyzing and evaluating both data and models. Maintained by The Institute for Ethical AI & ML, this library is rooted in the 8 principles for Responsible Machine Learning.

Why XAI Matters?

Explainable AI (XAI) is about more than just algorithms. It’s an interconnected puzzle that combines data science practices with specialized domain knowledge. By employing XAI, users can not only pinpoint discrepancies but also enhance the overall effectiveness of their machine learning models. The framework of XAI consists of three essential steps:

• Data Analysis
• Model Evaluation
• Production Monitoring

Installation of XAI

Getting started with XAI is simple! You can install it via PyPI by running the following command:

pip install xai

If you prefer to install from the source, simply clone the repository and execute:

python setup.py install

Usage Guidelines

The XAI library is designed for straightforward usage. Let’s walk through different functionalities using the census dataset.

Data Analysis

Firstly, you can load data to analyze imbalances:

import xai
df = xai.data.load_census()
df.head()

Imagine a chocolate factory. If you find that your chocolate batches mostly contain dark chocolate but almost no milk chocolate, you recognize an imbalance. Similarly, you can utilize the methods in XAI to identify and rectify data imbalances.

Example: Viewing Class Imbalances

ims = xai.imbalance_plot(df, 'gender')

In this step, you are visualizing the gender distribution in the dataset. Think of it like checking the types of chocolates in the factory—knowing what you have helps in planning what you need.

Balancing Classes

To further balance these classes in your dataset, you can use:

bal_df = xai.balance(df, 'gender', 'loan', upsample=0.8)

This is akin to creating new recipes that mix both dark and milk chocolate to satisfy a broader audience. The goal is to blend your dataset for better model training.

Model Evaluation

Next, let’s evaluate the model’s performance:

model = build_model(proc_df.drop('loan', axis=1))
model.fit(f_in(x_train), y_train, epochs=50, batch_size=512)

Here, you construct your machine-learning model, treating it like a new chocolate recipe. You need to carefully measure the ingredients (input features) to produce the desired outcome (predictions).

Metrics Visualization

To visualize how well your model performs based on these inputs, use:

xai.metrics_plot(y_test, probabilities)

Think of this as tasting your chocolate to see if it meets quality standards!

Troubleshooting

If you encounter any issues while using the XAI library, here are a few troubleshooting ideas:

• Ensure that you have the correct version of Python installed (3.5, 3.6, or 3.7).
• Double-check that all required packages dependencies are installed correctly.
• Refer to the documentation provided at ethicalml.github.io/xai/index.html for common errors and solutions.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Further Learning Resources

Check out our video that demonstrates how to mitigate biases using the XAI library, or explore the community-built Awesome Machine Learning Operations list for more tools.