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Ethics for Data Scientists The Limits of ML Munich DataGeeks Gerrit Gruben 31. January 2018

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about.me 2 •Freelance DS, before worked as DS/SWE. •Training people in a 3-month boot camp to be DS → •Org. of Kaggle Berlin meetup •ML PhD Dropout @ Potsdam •Degrees in Math. & CS, going for Laws (sic!) datascienceretreat.com

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Goals 3

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Main points •No data positivism in ML • inductive bias always there • IID assumption is idealistic. •Can't predict everything •ML systems prone to manipulation (fragility) 5

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Limits & Biases 6

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7 Benevolent or evil?

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”Absence of Evidence is not Evidence of Absence" --- Data Scientist’s Proverbs

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10 Source: http://www.gpmfirst.com/books/exploiting-future-uncertainty/risk-concepts

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”I beseech you, in the bowels of Christ, think it possible that you may be mistaken" --- Oliver Cromwell Dennis Lindley: avoid prior probabilities of 0 and 1.

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Problem of Induction •More general as the black swan problem. •ML models have an inductive bias. 13 ” The process of inferring a general law or principle from the observation of particular instances." --- Oxford's Dictionary (direct opposite of deduction)

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” When you have two competing theories that make exactly the same predictions, the simpler one is the better." --- Ockham’s Razor

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Technical Things What goes wrong often…

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Multiple Testing Retrying the tests so often, until "hitting" the significance level by chance. Solution: Bayesian or correction (e.g. Bonferroni correction) or different experimental design. Data Snooping: http://bit.ly/2iWoFrV

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Statistical Power

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Simpson's Paradox Let's try at: https://vudlab.com/simpsons/

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Frequentist vs Bayesian

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"P-hacking"

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"P-hacking" II "When a measure becomes a target, it ceases to be a good measure" --- Goodhart's law

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selection ≠ evaluation

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23 Paper: http://bit.ly/2gBIR1M Prefer to call it “over-selection” In “Learning with Kernels” from Smola & Schölkopf they name ex. 5.10. “overfitting on the test set”.

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Empirical Risk Minimization • 24

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Empirical Loss • 25

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Empirical Risk Minimization II • 26

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Bias / Variance 27

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• 28

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29 Source: http://bit.ly/2vDfoLp

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30 Source: University of Potsdam

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31 Source: University of Potsdam

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Nested CV 32 From Quora: http://bit.ly/2wvz2aZ

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Messing up your experiments •Data split strategy is part of experiment. •Mainly care for: • Class distribution • Problem domain relevant issues such as time 33 ”Validation and Test sets should model nature and nature is not accommodating." --- Data Scientist’s Proverbs

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34 “Model evaluation, model selection…“ by Sebastian Raschka: http://bit.ly/2p6PGY0 “Approximate Statistical Tests For Comparing Supervised Class. Learning Algorithms” (Dietterich 98): http://bit.ly/2wyItF6

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Gallery of Fails

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Courier/Terrorist detection in Pakistan 36 Source: http://bit.ly/1KY4SQE

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Feedback loops abused Tay.ai was a chat bot deployed on Twitter by Microsoft for just a day. Trolls started to "subvert" the bot by "teaching" it to be politically incorrect by focussed exposure to extreme content.

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Moral Machine http://moralmachine.mit.edu 38

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Smaller tips for ML •Always model uncertainty. •Read this •Don’t mock values of a non-existant predictive model. 39

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Books

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Other Links •https://www.ma.utexas.edu/users/mks/statmistakes/StatisticsMist akes.html •Quantopian Lecture Series: p-Hacking and Multiple Comparison bias https://www.youtube.com/watch?v=YiDfbYtgUPc •David Hume: A Treatise on Human Nature: http://www.davidhume.org/texts/thn.html 41

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Thanks! Questions? Github: github.com/uberwach 42