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Fairness in Machine Learning @Codemotion

AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.

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Fairness in Machine Learning @Codemotion

  1. 1. Fairness in Machine Learning: are you sure there is no bias in your predictions? Azzurra Ragone - Innovation Manager @azzurraragone
  2. 2. Me… Innovation Manager Previous @Google DevRel team Before Research fellow: ➢ Univ. Milano Bicocca, ➢ University of Michigan ➢ Politecnico of Bari ➢ University of Trento
  3. 3. People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world The Master Algorithm Pedro Domingos, 2015
  4. 4. How to make my ML system fair? ...and why care?
  5. 5. Our success, happiness and wellbeing can be affected by other decisions
  6. 6. Life-changing decisions: ➔ Admission to schools ➔ Job offers ➔ Patients screenings ➔ Mortgage grant ➔ ...
  7. 7. Arbitrary, inconsistent, or faulty decision-making thus raises serious concerns because it risks limiting our ability to achieve the goals that we have set for ourselves and access the opportunities for which we are qualified. Fairness and Machine Learning S. Barocas, M. Hardt, A. Narayanan
  8. 8. How do we ensure that these decisions are made the right way and for the right reasons? Fairness and Machine Learning S. Barocas, M. Hardt, A. Narayanan
  9. 9. The ML promise: make decisions more consistent, accurate and rigorous.
  10. 10. B. C. Russell, A. Torralba, C. Liu, R. Fergus, W. T. Freeman. Object Recognition by Scene Alignment. Advances in Neural Information Processing Systems, 2007.
  11. 11. ...but there are serious risks in learning from examples.
  12. 12. Generalizing from examples Source: https://design.google/library/fair-not-default/ Quick, Draw!
  13. 13. Generalizing from examples Provide good examples: - a sufficiently large and diverse set - well annotated Quick, Draw! Source: https://design.google/library/fair-not-default/
  14. 14. Historical examples may reflect: - Prejudices against a social group - Cultural stereotypes - Demographic inequalities and finding patterns in these data means replicating these same dynamics
  15. 15. Source: https://gluon-cv.mxnet.io/build/examples_datasets/imagenet.html
  16. 16. 45% of ImageNet data comes from USA (4% of the world population) 3% of ImageNet data comes from China and India (36% of the world population) Ref: Nature 559 and Shankar, S. et al. (2017) Geo bias
  17. 17. Photo Credit: Left: iStock/Getty; Right: Prakash Singh/AFP/Getty (from Nature 559, 324-326 (2018)) Bride Dress Woman Wedding Performance art Costume
  18. 18. Word Embeddings
  19. 19. Debiasing Word Embeddings Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V. & Kalai, A. Adv. Neural Inf. Proc. Syst. 2016, 4349–4357 (2016). Credit: Pictures by Pixabay
  20. 20. State of the world Data Individuals Model Measurement Learning Action Feedback The Machine Learning Loop Source: Fairness and Machine Learning S. Barocas, M. Hardt, A. Narayanan
  21. 21. State of the world Data Measurement The Machine Learning Loop
  22. 22. Provenance of data is crucial. Data cleaning is mandatory. The world is “messy” Photo by pasja1000 on Pixabay
  23. 23. Measurement defines: - your variables of interest, - the process for turning your observations into numbers, - how you actually collect the data [Fairness and Machine Learning, 2018] Photo by Iker Urteaga on Unsplash
  24. 24. The target variable is the hardest to measure. It is made up for the purpose of the problem. It is not a property that people possess or lack Ex. “creditworthiness”, “good employee”, “attractiveness” [Fairness and Machine Learning, 2018] Photo by David Paschke on Unsplash
  25. 25. State of the world Data Individuals Model Measurement Learning Action Feedback The Machine Learning Loop
  26. 26. ML will extract stereotypes the same way that it extracts knowledge
  27. 27. labor statistics and the male-as-norm bias almost perfectly predict which pronoun will be returned [Caliskan et al., 2017]
  28. 28. ML works better with more data, so it will work less well for members of minority groups Sample size disparity Training set Training data
  29. 29. State of the world Data Individuals Model Measurement Learning Action Feedback The Machine Learning Loop
  30. 30. It’s not always about “Prediction” (“is this patient at high risk for cancer?”). It can be classification (determine whether a piece of email is spam), regression (assigning risk scores to defendants), or information retrieval (finding documents that best match a search query). Photo by Tobias Zils on Unsplash
  31. 31. Predictions - actions - outcome Photo by Pixabay
  32. 32. State of the world Data Individuals Model Measurement Learning Action Feedback The Machine Learning Loop
  33. 33. If you predict future prices (and publicizes them) you create a self-fulfilling feedback loop: houses with a lower sales prices predicted deter buyers, demand goes down and the final price is even lower House price prediction PhotobyDevaDarshanonUnsplash
  34. 34. Some communities may be disproportionately targeted, with people being arrested for crimes that might be ignored in other communities. Ref.: Saunders, J., Hunt, P. & Hollywood, J. S. J. Exp. Criminol. 12, 347–371 (2016). Self-fulfilling predictions PhotobyJacquesTiberionPixabay
  35. 35. “Feedback loops occur when data discovered on the basis of predictions are used to update the model.” Danielle Ensign et al., “Runaway Feedback Loops in Predictive Policing,” 2017
  36. 36. State of the world Data Individuals Model Measurement Learning Action Feedback The Machine Learning Loop
  37. 37. Training data encode the demographic disparities in our society and some stereotypes can be reinforced by ML (due to feedback loop) The state of society PhotobyCorySchadtonUnsplash
  38. 38. Solutions?
  39. 39. Bias may lurk in your data...
  40. 40. Analyze your data Source: Google Machine Learning Crash Course ★ Are there missing feature values for a large number of observations? ★ Are there features that are missing that might affect other features? ★ Are there any unexpected feature values? ★ What signs of data skew do you see?
  41. 41. Missing feature values Source: California Housing dataset, Google Machine Learning Crash Course
  42. 42. Skew data (geographical bias) Source: California Housing dataset, Google Machine Learning Crash Course
  43. 43. Facets Overview Source: Facet tool (https://pair-code.github.io/facets/) Facets Overview, an interactive visualization tool to explore datasets. Quickly analyze the distribution of values across the datasets.
  44. 44. Facets Overview Source: Facet tool (https://pair-code.github.io/facets/) ⅔ of examples represent males, while we would expect the breakdown between genders to be closer to 50/50
  45. 45. Facets Dive Source: Facet tool (https://pair-code.github.io/facets/) Data are faceted by marital-status feature. Male outnumbers female by more than 5:1. Married women are underrepresented in our data.
  46. 46. Evaluating for Bias Source: Google Machine Learning Crash Course Model to predict the presence of tumors evaluated against a validation set of 1,000 patients. 500 records from female patients 500 records from male patients.
  47. 47. Evaluating for Bias Source: Google Machine Learning Crash Course the model incorrectly predicts tumor in 9.1% the model misses a tumor diagnosis in 9.1% the model incorrectly predicts tumor in 33.3% the model misses a tumor diagnosis in 45.5%
  48. 48. “What-if” tool Analyze ML model without writing code. Given pointers to a TF model and a dataset, the What-If Tool offers an interactive visual interface for exploring model results.
  49. 49. Counterfactuals It is possible to compare a datapoint to the most similar point where your model predicts a different result.
  50. 50. Counterfactuals a minor difference in age and an occupation change flipped the model’s prediction (earning >50K)
  51. 51. Visualize inference results Compare the performance of two models, or inspect a single model's performance by organizing inference results into confusion matrices, scatterplots or histograms.
  52. 52. Edit a datapoint Edit a datapoint and see how your model performs. Edit, add or remove features or feature values for any selected datapoint and then run inference to test model performance.
  53. 53. Test algorithmic fairness Slice your dataset into subgroups and explore the effect of different algorithmic fairness constraints See: “Playing with fairness” by David Weinberger.
  54. 54. ★ Measurement is crucial ★ Know your data (and how data were collected and annotated) ★ Try to discover hidden biases (missing values, data skew, subgroups, etc.) ★ Ask questions. Don’t train the model and then walk away ★ Avoid feedback loop ★ Use tools that allow you to do such investigation Key Takeaways
  55. 55. AI is a cultural shift as much as a technical one. Autonomous systems are changing workplaces, streets and schools. We need to ensure that those changes are beneficial, before they are built further into the infrastructure of every­ day life. There is a blind spot in AI research Kate Crawford& Ryan Calo Nature 538, 311–313 (20 October 2016)
  56. 56. Thanks! @azzurraragone
  57. 57. ❏ AI can be sexist and racist — it’s time to make it fair James Zou & Londa Schiebinger - Nature 559, 324-326 (2018) ❏ The Master Algorithm Pedro Domingos, 2015 ❏ Fairness and Machine Learning S. Barocas, M. Hardt, A. Narayanan ❏ No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, D. Sculley ❏ Man is to computer programmer as woman is to homemaker? Debiasing word embeddings T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, A. T. Kalai,. Adv. Neural Inf. Process. Syst. 2016, 4349–4357 (2016) References
  58. 58. ❏ There is a blind spot in AI research, Kate Crawford & Ryan Calo, Nature 538, 311–313 (20 October 2016) ❏ Semantics Derived Automatically from Language Corpora Contain Human-Like Biases, Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, Science 356, no. 6334 (2017): 183–86 ❏ Predictions Put Into Practice: a Quasi-experimental Evaluation of Chicago's Predictive Policing Pilot Saunders, J., Hunt, P. & Hollywood, J. S. J. Exp. Criminol. 12, 347–371 (2016). ❏ Runaway Feedback Loops in Predictive Policing Danielle Ensign et al. arXiv:1706.09847 References
  59. 59. ❏ Object Recognition by Scene Alignment. B. C. Russell, A. Torralba, C. Liu, R. Fergus, W. T. Freeman. Advances in Neural Information Processing Systems, 2007. ❏ Fair Is Not the Default (https://design.google/library/fair-not-default/) ❏ “Playing with fairness” - David Weinberger. ❏ Google Machine Learning Crash Course ❏ What-if tool: https://pair-code.github.io/what-if-tool/ ❏ Facet tool https://pair-code.github.io/facets/ References

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AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.

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