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Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 1Copyright © 2019 ML6. All rights reserved. ML6 Confidential Information | 1
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 2
Matthias Feys
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 3
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 4
■Unawareness
■Group fairness
■Individual fairness
→ No “best” definition of fairness
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 5
Don’t include sensitive variable as a feature in the
training data
+ simple to implement
+ doesn’t require knowledge of the sensitive
variable
- proxies of the sensitive variable are not
excluded
Image source: https://research.google.com/bigpicture/attacking-discrimination-in-ml/
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 6
Sensitive variable divides individuals into two groups,
each group should be treated “equal”
+ excludes potential proxy features
- definition of “equal” is contradictory
- prone to positive discrimination (lazy
implementation)
Image source: https://research.google.com/bigpicture/attacking-discrimination-in-ml/
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 7
Similar individuals should be treated similarly,
implemented by analysing pairs of individuals
+ most fine-grained fairness
- ambiguous definition of similarity
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 8
■Optimizing for any definition improves the status quo
■Impossible to optimize for all, possible to calculate all
→ multiple tools exist to help you with this (Google What-if, IBM AI Fairness 360, ...)
■Monitoring fairness is the first step
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 9
■ sample bias
■ prejudicial bias
■ exclusion bias
■ measurement bias
■ algorithmic bias
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 10
■ sample bias
■ prejudicial bias
■ exclusion bias
■ measurement bias
■ algorithmic bias
introduced by the
dataset
introduced by the
model
→
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 11
■ Reducing bias in the data (pre-processing)
■ Reducing bias in the classifier (training)
■ Reducing bias in the predictions (post-processing)
training data model training inference
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 12
■ Sampling or re-weighting the data
■ Modify features or labels (obfuscate
information about protected attributes) training data model training inference
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 13
■ Add a discrimination-aware regularization
term to the learning objective
■Adversarial debiasing: maximize accuracy
while reducing evidence of sensitive
features in the predictions
training data model training inference
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 14
■ Changes predictions from a classifier to
make them more fair
■ Works with black box models and unknown
datasets
training data model training inference
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 15
■3 main types of solutions
■Trade-off between accuracy and fairness
■Typically need sensitive features to achieve fairness
Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 16
■ Fairness/bias is on the map
‐ New papers
‐ New tools
‐ Work on legal context
■ “If you can measure it, you can improve it”
vs.
“You get what you measure”
Image source: https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb

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Introduction to Bias in Machine Learning, presented by Matthias Feys, CTO @ ML6 at the Trustworthy & Ethical AI conference on Feb. 13th

  • 1. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 1Copyright © 2019 ML6. All rights reserved. ML6 Confidential Information | 1
  • 2. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 2 Matthias Feys
  • 3. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 3
  • 4. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 4 ■Unawareness ■Group fairness ■Individual fairness → No “best” definition of fairness
  • 5. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 5 Don’t include sensitive variable as a feature in the training data + simple to implement + doesn’t require knowledge of the sensitive variable - proxies of the sensitive variable are not excluded Image source: https://research.google.com/bigpicture/attacking-discrimination-in-ml/
  • 6. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 6 Sensitive variable divides individuals into two groups, each group should be treated “equal” + excludes potential proxy features - definition of “equal” is contradictory - prone to positive discrimination (lazy implementation) Image source: https://research.google.com/bigpicture/attacking-discrimination-in-ml/
  • 7. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 7 Similar individuals should be treated similarly, implemented by analysing pairs of individuals + most fine-grained fairness - ambiguous definition of similarity
  • 8. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 8 ■Optimizing for any definition improves the status quo ■Impossible to optimize for all, possible to calculate all → multiple tools exist to help you with this (Google What-if, IBM AI Fairness 360, ...) ■Monitoring fairness is the first step
  • 9. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 9 ■ sample bias ■ prejudicial bias ■ exclusion bias ■ measurement bias ■ algorithmic bias
  • 10. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 10 ■ sample bias ■ prejudicial bias ■ exclusion bias ■ measurement bias ■ algorithmic bias introduced by the dataset introduced by the model →
  • 11. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 11 ■ Reducing bias in the data (pre-processing) ■ Reducing bias in the classifier (training) ■ Reducing bias in the predictions (post-processing) training data model training inference
  • 12. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 12 ■ Sampling or re-weighting the data ■ Modify features or labels (obfuscate information about protected attributes) training data model training inference
  • 13. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 13 ■ Add a discrimination-aware regularization term to the learning objective ■Adversarial debiasing: maximize accuracy while reducing evidence of sensitive features in the predictions training data model training inference
  • 14. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 14 ■ Changes predictions from a classifier to make them more fair ■ Works with black box models and unknown datasets training data model training inference
  • 15. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 15 ■3 main types of solutions ■Trade-off between accuracy and fairness ■Typically need sensitive features to achieve fairness
  • 16. Copyright © 2020 ML6. All rights reserved. ML6 Confidential Information | 16 ■ Fairness/bias is on the map ‐ New papers ‐ New tools ‐ Work on legal context ■ “If you can measure it, you can improve it” vs. “You get what you measure” Image source: https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb