trustyai-explainability/trustyai-explainability-python-examples
Examples for the Python bindings for TrustyAI's explainability library
When you're working with complex AI models, especially those used for critical decisions like loan approvals or medical diagnoses, it can be hard to understand why a model made a specific prediction. This project helps you peek inside your AI model to see what factors influenced its output. It takes your model's predictions and shows you the key drivers, making AI more transparent for data scientists, machine learning engineers, and risk managers.
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Use this if you need to understand the reasoning behind your AI model's predictions, assess its fairness, or debug unexpected behaviors.
Not ideal if you are looking for a tool to build AI models from scratch, rather than explain existing ones.
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Apache-2.0
Last pushed
Oct 07, 2024
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