Reports
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Model Risk Management with W&B
ML algorithms depend on Data and algorithms can amplify bias or inaccuracies in data creating disparate impact. This report highlights model development, assessment, and mitigation via Fairlearn, while using W&B for experiment tracking and reporting / documentation
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tim-w2023-12-05
Data Science Workflows and Fairness
ML Algorithms depend on Data and algorithms can amplify bias or inaccuracies in data creating disparate impact. This report highlights model development, assessment, and mitigation via Fairlearn, while using W&B for experiment tracking and reporting / documentation
0
2022-11-13
Copy of tim-w's Fairness
Attaching weights and biases to explore fairness of algorithms. Fairness is evaluated via Fairlearn and Weights and Biases is used to track everythging done. Borrows heavily (lots of copy and paste) from the intro example available at fairlearn.org. The original notebook can be found.
https://github.com/fairlearn/fairlearn/blob/main/notebooks/Binary%20Classification%20with%20the%20UCI%20Credit-card%20Default%20Dataset.ipynb
0
2022-10-17
Fairness
Attaching weights and biases to explore fairness of algorithms. Fairness is evaluated via Fairlearn and Weights and Biases is used to track everythging done. Borrows heavily (lots of copy and paste) from the intro example available at fairlearn.org. The original notebook can be found.
https://github.com/fairlearn/fairlearn/blob/main/notebooks/Binary%20Classification%20with%20the%20UCI%20Credit-card%20Default%20Dataset.ipynb
0
2022-05-18