GiskardHow to test ML Models? (3/n): numerical data driftWasserstein metrics, Earthmover distance, Kolmogorov test… Drift for numerical features in Machine Learning can be tested using many…Apr 20, 2022Apr 20, 2022
GiskardHow to test ML Models? (2/n): categorical data driftKullback-Leibler (KL) divergence, Population stability index (PSI), Chi-square, etc. Drifts for categorical data in Machine Learning can be…Mar 31, 20221Mar 31, 20221
Giskard🧪 How to test ML models? (1/n)While regulators are asking for Quality management systems for AI (article 17 from the European AI Act), the capacity to create tests is…Mar 24, 2022Mar 24, 2022
Giskard🔍 Where do biases in ML come from? (7/N): 👀 PresentationIn this post, we focus on presentation bias, a negative effect present in almost all ML systems with User Interfaces (UI).Jan 14, 2022Jan 14, 2022
Giskard🔍 Where do biases in #AI / #ML come from? (6/N): Emergent biasIn this post, we focus on emergent bias among the most commonplace biases in AI.Jan 7, 2022Jan 7, 2022
GiskardWishing y’all a happy & healthy 2022! 🎊2021 has been an eventful & productive year 🚀Jan 3, 2022Jan 3, 2022
Giskard🔍 Where do biases in ML come from? (5/N) 📊 Structural biasIn this post, we focus on structural biases 🕵️♂️Dec 14, 2021Dec 14, 2021
Giskard🔍 Where do biases in ML come from? (4/N) 📊 SelectionIn this post, we focus on selection biases. 📊Dec 10, 2021Dec 10, 2021
Giskard🔍 Where do biases in ML come from? (3/N) 📏 MeasurementIn this post, we focus on one of the most important biases: measurement 📏Dec 10, 2021Dec 10, 2021
Giskard🔍 Where do biases in ML come from? (2/N) ❌ ExclusionIn this second post about the reasons for biases in ML, we focus on one of the most important biases in ML: exclusion biases. ⚠Dec 10, 2021Dec 10, 2021