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Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects.
Code for the definition and testing of three new fairness-aware algorithms: Fair Decision Tree, Fair Genetic Pruning, and Fair LightGBM (FDT, FGP, FLGBM), completed for my Master's thesis.
Repository of the paper "Towards a Human-Centred Fairness Analysis: From Binary to Multiclass and Multigroup Assessment in Graph Neural Network-Based Models for User Profiling Tasks"
WEFE: The Word Embeddings Fairness Evaluation Framework. WEFE is a framework that standardizes the bias measurement and mitigation in Word Embeddings models. Please feel welcome to open an issue in case you have any questions or a pull request if you want to contribute to the project!