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