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Trusted-AI / AIF360

Star 2.3k

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

python machine-learning r ai deep-learning artificial-intelligence bias fairness bias-finder ibm-research discrimination bias-correction ibm-research-ai bias-reduction fairness-testing bias-detection codait fairness-awareness-model fairness-ai trusted-ai
  • Updated Apr 20, 2024
  • Python

microsoft / responsible-ai-toolbox

Star 1.2k

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

visualization data-science machine-learning widget ui jupyter widgets ml data-visualization data-analysis machinelearning fairness interpretability error-analysis explainable-ai explainable-ml fairness-ai explainability fairness-ml responsible-ai
  • Updated Apr 29, 2024
  • TypeScript

tangxyw / RecSysPapers

Star 989

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

reinforcement-learning match rank calibration recommendation-system cold-start papers multi-modal causal-inference multi-task fairness debias multi-scenario distillation diverse re-rank look-alike contrastive-learning pre-rank feedback-delay
  • Updated Dec 30, 2023
  • Python

jphall663 / interpretable_machine_learning_with_python

Star 662

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability
  • Updated Feb 27, 2023
  • Jupyter Notebook
deep-explanation-penalization

laura-rieger / deep-explanation-penalization

Star 123

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

python data-science machine-learning ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-network fairness interpretability cdep feature-importance recurrent-neural-network interpretable-deep-learning explainable-ai explainability fairness-ml
  • Updated Mar 22, 2021
  • Jupyter Notebook

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