xai and AIX360
Both provide overlapping XAI method implementations (SHAP, LIME, counterfactuals, etc.) with similar scope and positioning, making them **competitors** offering alternative interpretability frameworks rather than tools designed to work together.
About xai
EthicalML/xai
XAI - An eXplainability toolbox for machine learning
This tool helps data scientists and machine learning engineers analyze and evaluate their machine learning models to ensure fairness and transparency. It takes in your dataset and trained model, then outputs visualizations and metrics that highlight data imbalances, feature importance, and model performance across different groups. This is for anyone building or deploying machine learning models who needs to understand why their model makes certain decisions and identify potential biases.
About AIX360
Trusted-AI/AIX360
Interpretability and explainability of data and machine learning models
This toolkit helps data scientists, machine learning engineers, and researchers understand why their AI models make specific predictions. It takes your existing tabular, text, image, or time-series data and machine learning models, and outputs explanations showing the factors influencing the model's decisions or highlighting important aspects of the data itself. This allows you to build trust in AI systems and debug potential issues.
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