GDPR doesn't have to mean slowing down the progress of your AI/ML projects. By following a few fundamental rules during a development process, your model could not only be compliant with this directive but also gain the trust of the users. During this talk, we will discover an effective approach to transparency and privacy, with profit for users and piece of mind for developers.

1. GDPR in practise
How to develop ML/Data projects with
privacy and transparency in mind?
Łukasz Mokrzycki / PyData Berlin 2018
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2. me: Software Developer
worked for various markets:
IoT (Estimote Inc.)
Healthcare (Harimata)
SaaS (Base CRM, Copper.io)
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3. !3
disclaimer
Photo by Cytonn Photography on Unsplash law

4. disclaimer:
it’s help guide for developers,
not an official legal advice
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5. but we had a talk about GDPR
yesterday, what’s the difference?
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6. why developers should care
about GDPR?
responsibility & practicality
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7. weak excuses
* not my problem
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8. weak excuses
* not my problem
* rejecting EU clients
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9. weak excuses
* not my problem
* rejecting EU clients
* postponing implementation
until legally required
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10. some market require
additional care
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11. some market require
additional care
healthcare
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12. some market require
additional care
IOT
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13. basic terms
* data subject
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14. basic terms
* data subject
* personal data
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15. basic terms
* data subject
* personal data
* data controller
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16. basic terms
* data subject
* personal data
* data controller
* data processor
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17. two scenarios to consider:
* profiling
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18. two scenarios to consider:
* profiling
* automated decision making
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19. consent and transparency
two wise actions for gaining
users trust
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20. !20
anonymization
Photo by davide ragusa on Unsplash crowd

21. anonymization middleware
if you avoid collecting and
processing unnecessary personal
data, you reduce amount of work
needed to be done
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22. !22
documentation
Photo by Thomas Kelley on Unsplash Books

23. documentation for:
datasets
(format, selection, subjects)
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24. documentation for:
models & parameters
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25. documentation for:
data pipelines
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26. documentation for:
external services
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27. !27
data pipelines
Photo by Samuel Zeller on Unsplash pipeline

28. take control of your data pipeline
you should be prepare to modify,
edit or delete users data from
your system
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29. control over automated system
you should be able to supervise
and modify actions performed by
automated decision making code
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30. automate workflow early
use code to speed up
reproduction of all training and
configuration parts
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31. start with simple models
adding complexity may increase
accuracy but decrease
interpretability - be sure what’s
more important to you
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32. !32
ethics
Photo by Giammarco Boscaro on Unsplash ethics

33. test against the bias
even unintended correlations can
lead to problems
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34. use diversified training data
society is diverse, models should
mirror this fact
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35. don’t ignore domain experts
they can have crucial insight
about possible effects of your
ML solution
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36. thank you
email:
lucas.mokrzycki@gmail.com
twitter:@mokrzu
blog: http://medium.com/
@l.mokrzycki !36