1. Ethical Implications of using Big Data for Official Statistics
Albrecht Wirthmann, Eurostat,
Fernando Reis, Eurostat,
28 June 2018
Session 07

2. Objective Use of Big Data in official statistics
Analysis of ethical issues related to use of big data in official statistics
Ethical review
Code of Practice
Recommendations for using big data in official statistics
Ethical guidelines
Data linking
Secondary data sources
Statistical modelling
Emerging data market

3. Data sources Telecommunication network data
Sensor data (non-personal, identifiable)
Data obtained from internet
Financial transaction data
Personal health data
Electronic reservation systems data
Cash register data
Telecommunication network data
Sensor data that can be related to individual persons or households:
Personal communication devices
Wearables, e.g. smart watches, heart rate monitors, etc.
Smart meters
Non-personal sensor data:
Road traffic loops
Remote sensing data (satellite images, unmanned aerial vehicles (UAV), etc)
Data obtained from the internet including:
Social media data with or without geolocation information
Web-scraped data from company websites, e-commerce websites, job vacancy websites or real estate agencies' websites
Query and clickout data from internet searches
Financial transaction data (credit cards, debit cards, online payment systems)
Personal health data
Electronic reservation systems data - e.g. data from flight or hotel booking systems
Cash register data, e.g. from supermarkets
Crowd sourced data including volunteered geographic data, community picture collections

4. Professional Ethics Declaration on professional ethics
UN Fundamental principles of Official Statistics European Statistics Code of Practice
The European Statistics Code of Practice (CoP) defines the principles for the production and dissemination of European official statistics and the institutional environment under which statistical authorities (at national and European level) operate.
These principles are in line with the UN Fundamental principles of official statistics and convey the shared professional values of statisticians described in the
Declaration on professional ethics, adopted by the International Statistical Institute (ISI) Council in These are respect, professionalism, truthfulness and integrity.
Declaration on professional ethics & Eurostat values
Respect & Trust
Fostering excellence
Promoting innovation
Service orientation
Professional independence
institutional environment under which statistical authorities operate
Principles for production and dissemination of European statistics

5. Approach Identify issues of the use big data in official statistics
Review the principles and indicators
Identify principles of the CoP that give the answers to the ethical questions
Consult stake holders of official statistics, to verify with professional values
Formulate ethical questions
Identify issues of the use big data in official statistics
Identify issues of the use big data in official statistics on the basis of ongoing projects and theoretical thinking
Formulate ethical questions
Consult stake holders of official statistics, to verify with professional values
Identify principles of the CoP that give the answers to the ethical questions
Review the principles and indicators

6. Characteristics of Big Data
Secondary sources
Observing instead of asking
Not designed for statistical purposes
Held by third parties, mostly private entities
Indirect relationship with data objects
Emerging data market
New analysis methods
Machine learning, neural networks
Data linking
Secondary sources
Observing instead of asking
Not designed for statistical purposes
Held by third parties, mostly private entities
Indirect relationship with data objects
Emerging data market
New analysis methods
Machine learning, neural networks
Data linking

7. Data acquisition and access
Mandate for data collection
Professional independence
"Data for statistical purposes may be drawn from all types of sources"
Impartiality
Public and private interest in using data
Mandate for data collection
Professional independence
"Data for statistical purposes may be drawn from all types of sources"
Impartiality
Public and private interest in using data

8. Third party data providers
Direct and indirect data collectors
Professional Independence of statistical office
Emphasis on input quality
Possible bias
Data manipulation
Volatility of data sources
Consequences for data provider
Reputation
Economic consequences
Additional burden
Reputation of individual and of statistical office
Direct and indirect data collectors
Professional Independence of statistical office
Emphasis on input quality
Possible bias
Data manipulation
Volatility of data sources
Consequences for data provider
Reputation
Economic consequences
Additional burden
Reputation of individual and of statistical office

9. Privacy & Confidentiality
GDPR for personal data
Personal data from non-personal information
Threat of disclosure through data accumulation
Identification of smaller groups
Consent of persons / Information of public
GDPR for personal data
Personal data from non-personal information
Threat of disclosure through data accumulation
Identification of smaller groups
Consent of persons / Information of public

10. Big Data Analytics Statistical inference from observed behaviour
Statistical concepts
Machine learning algorithms
"Black box" algorithms
Lack of Skills
Sourcing
Commitment to quality and transparency
Adequacy of resources

11. Quality -> Transparency Assessment of input quality
Scientifically sound methodology
Data manipulation
Inform on output quality
Quality dimensions
Assessment of input quality
Scientifically sound methodology
Data manipulation
Inform on output quality
Quality dimensions
Use of big data and data integration
-> Transparency
Use of big data and data integration
-> Transparency

12. Data Market Data as economic asset Data as public good
Algorithms for data analysis
Data as public good
Right of citizens to be informed
Expectation to make profit
Exchange of data between private and public actors
Data as economic asset
Algorithms for data analysis
Data as public good
Right of citizens to be informed
Expectation to make profit
Exchange of data between private and public actors

13. Conclusions New principles are not necessary
Amendment or emphasis of certain indicators
Similarities with administrative data
Private data holders
Privacy and confidentiality
New aspect related to burden
Transparency
source data, methods, products
Standard setting
Role as trusted third party
New principles are not necessary
Amendment or emphasis of certain indicators
Similarities with administrative data
Private data holders
Privacy and confidentiality
New aspect related to burden
Transparency
source data, methods, products
Standard setting
Role as trusted third party

14. Ethical Implications of using Big Data for Official Statistics
Thank you for your attention!
Albrecht Wirthmann, Eurostat,
Fernando Reis, Eurostat,
Ethical Review: al.pdf
Draft Ethical guidelines: