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fairlearn / fairlearn

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jphall663 / interpretable_machine_learning_with_python

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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 10, 2021
  • Jupyter Notebook

microsoft / responsible-ai-widgets

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[Error Analysis] Support for bringing in features that are not being used by the model but useful for error analysis

nushib
nushib commented Jun 2, 2021

Suggested by Melanie Fernandez Pradier:

“Given a model trained on certain features, is there any way I can include additional features (not used in training) but that I want to monitor in the error analysis?"

This is currently possible by enriching the set of input features to the dashboard after the inference step. However, will need further support on the UI side to clearly mark features t

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Design Error Analysis documentation enhancement
deep-explanation-penalization

laura-rieger / deep-explanation-penalization

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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

EducationalTestingService / rsmtool

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Streamline the code for flagging warnings in the reports

aloukina
aloukina commented Jul 19, 2021

When tests are run in STRICT mode, we check for any errors or warnings in the report.

We currently have two functions:

test_utils.check_report https://github.com/EducationalTestingService/rsmtool/blob/main/rsmtool/test_utils.py#L767 checks for both errors and warnings

collect_warning_messages_from_report https://github.com/EducationalTestingService/rsmtool/blob/main/rsmtool/test_u

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