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Machine Learning Bias

Machine Learning Bias

We have been constantly told this statement “Computers don’t lie”. Yes in fact Computers don’t lie, but neither does it speak the truth. A computer does what its Master programs it to do. Similarly, A model wouldn’t lie unless the Machine Learning Engineer want it to lie. Humans are filled with unconscious biases and when these are fed into Machine to Learn in the form of Data, the resulting AI model wouldn't be `fair` enough without Biases. This deck tries to introduce you to the world of Machine Learning Bias.

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AbdulMajedRaja RS

April 02, 2019
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Transcript

  1. Machine Learning Bias AbdulMajedRaja RS

  2. Outline • Recognizing the Problem • What’s Machine Learning Bias?

    • Definition of “Fairness” • Interpretable Machine Learning
  3. Thoughts? What if I told you Computers can lie? Would

    you believe me?
  4. Biased-Google Translation at Work

  5. The Problem - Samples

  6. But Wait, Why is this concerning? After all, This is

    just Google Translate
  7. Biased-Google Photos App at Work

  8. Perhaps, That’s just Google. Two instances can account for the

    entire industry, Huh?
  9. Microsoft’s super-cool Teen Tweeting Bot Tay

  10. Much more!

  11. Oops, Got it! There, definitely, is Bias! What’s next?

  12. ML Bias - What

  13. What’s Machine Learning Bias? A Machine Learning Algorithm being “unfair”

    with its Predictions A Machine Learning Algorithm missing “Fairness”
  14. ML Bias - (un)Fairness

  15. Disclaimer No Common Consensus / Standard definition of Fairness

  16. ML Bias - un(Fairness) • Group Fairness • Individual Fairness

  17. ML Bias - Causes

  18. ML Bias - Causes • Skewed sample • Tainted examples

    • Limited features • Sample size disparity • Proxies
  19. ML Bias - Mitigate

  20. Mitigation Also means, Improving Fairness

  21. ML Bias - Improving Fairness Pre-Processing Training (Optimization) Post-Processing Learn

    a New Representation - Free from Sensitive Variable Yet, preserving the Information Add a constraint or a regularization term Find a proper threshold using the original score function
  22. ML Bias - Happening

  23. Mention of ML Fairness in Research Papers

  24. Difficulties in ensuring ML Algorithm is unbiased

  25. Interpretable Machine Learning

  26. Today - Modelling Architecture

  27. IML - Definition Interpretable Machine Learning refers to methods and

    models that make the behavior and predictions of machine learning systems understandable to humans.
  28. IML - Benefits • Fairness: Ensuring that predictions are unbiased

    and do not implicitly or explicitly discriminate against protected groups. An interpretable model can tell you why it has decided that a certain person should not get a loan, and it becomes easier for a human to judge whether the decision is based on a learned demographic (e.g. racial) bias. • Privacy: Ensuring that sensitive information in the data is protected. • Reliability or Robustness: Ensuring that small changes in the input do not lead to large changes in the prediction. • Causality: Check that only causal relationships are picked up. • Trust: It is easier for humans to trust a system that explains its decisions compared to a black box.
  29. Modelling Architecture - with IML

  30. Preferred Explaining - Model Interpretation

  31. References • https://developers.google.com/machine-learning/fairness-overview/ • https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb • https://www.youtube.com/watch?v=fMym_BKWQzk • https://www.kaggle.com/nulldata/ml-bias-iml-perspective-recommendation#media-coverage-about-bias-i ml

    • Doshi-Velez, Finale, and Been Kim. “Towards a rigorous science of interpretable machine learning,” no. Ml: 1–13. http://arxiv.org/abs/1702.08608 ( 2017) • https://christophm.github.io/interpretable-ml-book/ • https://github.com/adebayoj/fairml/
  32. Thank you!