data protection and ethics in the age of machine learning · data protection and ethics in the age...
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Data Protection and Ethics in the Age of Machine Learning
OECD, Paris, October 27, 2017
Peter FleischerGlobal Privacy Counsel, Google
Making machine learning requires four ingredients
Computationalresources
Trainingexamples
Algorithms+ tools
Creativity +ingenuity
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Machine Learning
Write a computer program with explicit rules to followif email contains V!agrå
then mark is-spam;
if email contains …
if email contains …
Write a computer program to learn from examplestry to classify some emails;
change self to reduce errors;
repeat;
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Deep Neural Networks Step 1: training
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Deep Neural Networks Step 2: testing
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Machine learning is already improving many of our products
SearchSearch ranking
Speech recognition
AndroidKeyboard & speech input
PlayApp recommendations
Game developer experience
GmailSmart reply
Spam classification
DriveIntelligence in Apps
ChromeSearch by image
AssistantSmart connections
across products
YouTubeVideo recommendations
Better thumbnails
MapsParsing local search
TranslateText, graphic and speech
translation
CardboardSmart stitching
PhotosPhotos search
Recent Translate improvements
https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
Perfect translation
HumanNeural (GNMT)Phrase-based (PBMT)
English>
Spanish
English>
French
English>
Chinese
Spanish>
English
French>
Spanish
Chinese>
Spanish
Translation model
Tran
slat
ion
qual
ity
old: PBMT
new: GNMT
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Google Home
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“running”, “score”: 0.99803412,“Marathon”, ”score”: 0.99482006
“joyLikelihood”: “VERY_LIKELY”
“description”: “ABIERTO\n”,“local”: “es”
https://cloud.google.com/ml/
Google Cloud Machine Learning
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Using machine learning, a 40% reduction in the amount of energy used for cooling was consistently achieved
Machine Learning examples: Diabetic retinopathy screening
387 million diabetic patients worldwide at risk and 200 thousand ophthalmologists
Machine Learning, the GDPR, and Ethics
Core Privacy Principles
Notice
Purpose Limitation
Data Minimisation
Data Accuracy
Data Integrity and Confidentiality
Access and Choice
Data Security
Accountability
Human Control
Profiling
Automated Decision Making
Correction for Algorithmic Bias