Roadmaps for risk mitigation
  • Risk mitigation roadmaps
  • Mitigation Roadmaps
    • Improving generalization through model validation
      • Step 1: Estimating generalization
      • Step 2: Model validation for hyperparameters tuning
      • Step 3: Performing algorithmic selection
      • Additional Material
    • Hyperparameter Optimisation
      • Step 1: Validation
      • Step 2: Hyperparameter Search
      • Additional Considerations
    • Handling dataset shift
      • Step 1: Understanding dataset shifts
      • Step 2: Detecting dataset shifts
      • Step 3: Handling dataset shifts
      • Additional Material
    • Adversarial training for robustness
      • Step 1: Understanding adversarial examples
      • Step 2: Finding adversarial examples
      • Step 3: Defending against adversarial examples
      • Additional Material
    • Data Minimization techniques
      • Step 1: Understanding the data minimization principle
      • Step 2: Data minimization techniques for Supervised Learning
        • Option 1: Reducing features
        • Option 2: Reducing data points
      • Step 3: Other privacy-preserving techniques
      • Additional Material
    • Measuring Bias and Discrimination
      • Step 1: Understanding bias
      • Step 2A: Measuring Bias for Classification tasks
        • Equality of Outcome metrics
        • Equality of Opportunity metrics
      • Step 2B: Measuring Bias in Regression tasks
        • Equality of Outcome metrics
        • Equality of Opportunity metrics
      • Additional Material
    • Mitigating Bias and Discrimination
      • Step 1: Understanding bias
      • Step 2: Mitigating Bias
        • Option 1: Pre-processing
        • Option 2: In-processing
        • Option 3: Post-Processing
      • Additional Material
    • Documentation for improved explainability of Machine Learning models
      • Step 1: Datasheets for Datasets
      • Step 2: Model Cards for Model Reporting
      • Additional Material
    • Extracting Explanations from Machine Learning Models
      • Step 1: Understanding algorithmic explainability
      • Step 2: In-processing methodologies for Explainability
      • Step 3: Post-processing methodologies for Explainability
      • Additional Material
Powered by GitBook
On this page
  • Reading
  • Tools
  1. Mitigation Roadmaps
  2. Documentation for improved explainability of Machine Learning models

Additional Material

PreviousStep 2: Model Cards for Model ReportingNextExtracting Explanations from Machine Learning Models

Last updated 3 years ago

Reading

Tools

Datasheet template Microsoft
Gebru et al. 2021
Mitchell et al. 2019
https://ai.googleblog.com/2020/07/introducing-model-card-toolkit-for.html
https://www.microsoft.com/en-us/research/project/datasheets-for-datasets/ https://github.com/tensorflow/model-card-toolkit https://github.com/tensorflow/model-card-toolkit/blob/master/model_card_toolkit/documentation/examples/MLMD_Model_Card_Toolkit_Demo.ipynb https://modelcards.withgoogle.com/about