Roadmaps for risk mitigation
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  • Risk mitigation roadmaps
  • Mitigation Roadmaps
    • Improving generalization through model validationchevron-right
    • Hyperparameter Optimisationchevron-right
    • Handling dataset shiftchevron-right
    • Adversarial training for robustnesschevron-right
    • Data Minimization techniqueschevron-right
      • Step 1: Understanding the data minimization principle
      • Step 2: Data minimization techniques for Supervised Learningchevron-right
      • Step 3: Other privacy-preserving techniques
      • Additional Material
    • Measuring Bias and Discriminationchevron-right
    • Mitigating Bias and Discriminationchevron-right
    • Documentation for improved explainability of Machine Learning modelschevron-right
    • Extracting Explanations from Machine Learning Modelschevron-right
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  1. Mitigation Roadmapschevron-right
  2. Data Minimization techniques

Additional Material

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Reading

De Cristofaro, Emiliano. "A critical overview of privacy in machine learning". IEEE Security and Privacy, 2020.arrow-up-right

Olvera-López, J. Arturo, J. Ariel Carrasco-Ochoa, J. Martínez-Trinidad, and Josef Kittler. "A review of instance selection methods." Artificial Intelligence Review, 2010.arrow-up-right

Guidance on the AI auditing framework, Information Commissioner's Officearrow-up-right

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Tools

IBM AI privacy toolkit arrow-up-right

sklearn arrow-up-right

PreviousStep 3: Other privacy-preserving techniqueschevron-leftNextMeasuring Bias and Discriminationchevron-right

Last updated 3 years ago

  • Reading
  • Tools