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Rabah Abdul Khalek
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L'Oréal leverages Giskard for advanced Facial Landmark Detection
L'Oréal has partnered with Giskard to enhance its AI models for Facial Landmark Detection. The collaboration focuses on evaluating and comparing various AI models using metrics such as Normalized Mean Error, prediction time, and robustness against image perturbations. It aims to improve the accuracy and reliability of L'Oréal's online services, ensuring superior performance across diverse facial regions and head poses. Co-authors: Alexandre Bouchez (L'Oréal), and Mathieu Martial (Giskard).
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How to test the fairness of ML models? The 80% rule to measure the disparate impact
This article provides a step-by-step guide to detecting ethical bias in AI models, using a customer churn model as an example, using the LightGBM ML library. We show how to calculate the disparate impact metric with respect to gender and age, and demonstrate how to implement this metric as a fairness test within Giskard's open-source ML testing framework.
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