ML Model Vulnerabilities

Before testing Machine Learning models, it’s crucial to have a comprehensive understanding of the critical vulnerabilities that can impact your model. Giskard provides an automatic scan functionality that is designed to automatically detect a variety of risks associated with your ML model. You can learn more about the different vulnerabilities it can detect here:

• Performance Bias
• Unrobustness
• Overconfidence
• Underconfidence
• Unethical behaviour
• Data Leakage
• Stochasticity
• Spurious correlation

By conducting a Giskard Scan, you can proactively identify and address these vulnerabilities to ensure the reliability, fairness, and robustness of your Machine Learning models.